【2021年更新】面向通信技术的机器学习和深度学习文献汇总

偏执的太偏执、 2023-01-08 08:13 128阅读 0赞

### 目录 ###

*  综述
 *  信号检测、信号分类和比较
 *  信道编码和解码
 *  端到端通信的学习
 *  定位、传感和本地化
 *  安全性和鲁棒性
 *  毫米波通信
 *  资源分配
 *  其他类

[参考IEEE的Library][IEEE_Library]  
[附带源码的文献汇总][Link 1]

# 综述 #

• C. Jiang, H. Zhang, Y. Ren, Z. Han, K.-C. Chen and L. Hanzo, “Machine learning paradigms for next-generation wireless networks,” IEEE Wireless Communications, vol. 24, no. 2, pp. 98-105, April 2017.  
• T. J. O’Shea and J. Hoydis, “An introduction to deep learning for the physical layer,” IEEE Transactions on Cognitive Communications and Networking, vol. 3, no. 4, pp. 563-575, December 2017.  
• L. Liang, H. Ye and G. Y. Li, “Towards intelligent vehicular networks: a machine learning framework,” to appear/early access in IEEE Internet of Things Journal.  
• K. Arulkumaran, M. P. Deisenroth, M. Brundage and A. A. Bharath, “Deep reinforcement learning: a brief survey,” IEEE Signal Processing Magazine, vol. 34, no. 6, pp. 26-38, November 2017.  
• M. Ibnkahla, “Applications of neural networks to digital communications – A survey,” Elsevier Signal Processing, no. 80, pp. 1185-1215, July 2000.  
• O. Simeone, “A very brief introduction to machine learning with applications to communication systems,” in IEEE Transactions on Cognitive Communications and Networking, vol. 4, no. 4, pp. 648-664, December 2018.  
• Q. Mao, F. Hu and Q. Hao, “Deep learning for intelligent wireless networks: A comprehensive survey,” in IEEE Communications Surveys & Tutorials, vol. 20, no. 4, pp. 2595-2621, Fourthquarter 2018.  
• M. Chen, U. Challita, W. Saad, C. Yin and M. Debbah, “Machine learning for wireless networks with artificial intelligence: A tutorial on neural networks,” preprint arXiv:1710.02913, 2017.  
• Y. Sun, M. Peng, Y. Zhou, Y. Huang and S. Mao, “Application of machine learning in wireless networks: key techniques and open issues,” preprint arXiv:1809.08707, 2018.  
• J. Jagannath, N. Polosky, A. Jagannath, F. Restuccia and T. Melodia, “Machine learning for wireless communications in the internet of things: A comprehensive survey,” preprint arXiv:1901.07947, 2019.  
• J. Park, S. Samarakoon, M. Bennis and M. Debbah, “Wireless network intelligence at the edge,” preprint arXiv:1812.02858, 2018.  
• J. Wang, C. Jiang, H. Zhang, Y. Ren, K.-C. Chen and L. Hanzo, “Thirty years of machine learning: The road to Pareto-optimal next-generation wireless networks,” preprint arXiv:1902.01946, 2019.  
• G. Zhu, D. Liu, Y. Du, C. You, J. Zhang and K. Huang, “Towards an intelligent edge: Wireless communication meets machine learning,” preprint arXiv:1809.00343, 2018.  
• A. Zappone, M. Di Renzo and M. Debbah, “Wireless networks design in the era of deep learning: Model-based, AI-based, or both?,” preprint arXiv:1902.02647, 2019.  
• M. Kulin, C. Fortuna, E. De Poorter, D. Deschrijver and I. Moerman,”Data-driven design of intelligent wireless networks: An overview and tutorial,” Sensors, 2016.  
• Z. Qin, H. Ye, G. Y. Li and B.-H. F. Juang, “Deep learning in physical layer communications,” preprint arXiv:1807.11713, 2018.  
• X. Li, F. Dong, S. Zhang and W. Guo, “A survey on deep learning techniques in wireless signal recognition,” Wireless Communications and Mobile Computing, vol. 2019.  
• H. He, S. Jin, C.-K. Wen, F. Gao, G. Y. Li and Z. Xu, “Model-driven deep learning for physical layer communications,” preprint arXiv:1809.06059, 2018.  
• E. Björnson, L. Sanguinetti, H. Wymeersch, J. Hoydis and T. L. Marzetta, “Massive MIMO is a Reality – What is Next? Five Promising Research Directions for Antenna Arrays,” preprint arXiv:1902.07678, 2019.  
• S. M. Aldossari and K.-C. Chen, “Machine learning for wireless communication channel modeling: An overview,” Wireless Personal Communications, 2019.  
• S. J. Nawaz, S. K. Sharma, S. Wyne, M. N.Patwary and M. Asaduzzaman, “Quantum machine learning for 6G communication networks: State-of-the-art and vision for the future,” IEEE Access, 2019.  
• H. Huang, S. Guo, G. Gui, Z. Yang, J. Zhang, H. Sari and F. Adachi, “Deep learning for physical-layer 5G wireless techniques: Opportunities, challenges and solutions,” preprint arXiv:1904.09673, 2019.  
• D. Gunduz, P. de Kerret, N. D. Sidiropoulos, D. Gesbert, C. Murthy and M. van der Schaar, “Machine learning in the air,” preprint arXiv:1904.12385, 2019.  
• N. C. Luong, D. T. Hoang, S. Gong, D. Niyato, P. Wang, Y.-C. Liang and D. In Kim, “Applications of deep reinforcement learning in communications and networking: A survey,” in IEEE Communications Surveys & Tutorials., 2018.  
• D. Roy, T. Mukherjee and M. Chatterjee, “Machine learning in adversarial RF environments,” in IEEE Communications Magazine, 2019.  
• S. Zheng et al., “Big data processing architecture for radio signals empowered by deep learning: Concept, experiment, applications and challenges,” in IEEE Access, 2018.  
• A. B.-Stimming and C. Studer, “Deep unfolding for communications systems: A survey and some new directions,” preprint arXiv:1906.05774, 2019.  
• R. Shafin, L. Liu, V. Chandrasekhar, H. Chen, J. Reed and J. Zhang, “Artificial intelligence-enabled cellular networks: A critical path to beyond-5G and 6G,” preprint arXiv:1907.07862, 2019.  
• M. A. Amirabadi, “A survey on machine learning for optical communication,” preprint arXiv:1909.05148, 2019.  
• M. Zamanipour, “A survey on deep-learning based techniques for modeling and estimation of massive MIMO channels,” preprint arXiv:1910.03390, 2019.  
• U. Challita, H. A. Ryden, and H. Tullberg, “When machine learning meets wireless cellular networks: Deployment, challenges, and applications,” preprint arXiv:1911.03585, 2019.  
• W. Guo, “Explainable artificial intelligence (XAI) for 6G: Improving trust between human and machine,” preprint arXiv:1911.04542, 2019.  
• S.-H. ZHANG, J.-H. ZHANG, Y. CHEN and J.-K. ZHU, “Wireless big data enabled emerging technologies for beyond 5G system,”. Journal of Beijing University of Posts and Telecommunications, 2018.  
• D. Chelmins, J. Briones, J. Downey, G. Clark and A. Gannon, “Cognitive communications for NASA space systems,” NASA Technical Report, 2019.  
• Y. Liu, S. Bi, Z. Shi, and L. Hanzo, “When machine learning meets big data: A wireless communication perspective,” preprint arXiv:1901.08329, 2019.  
• O. Simeone, S. Park, and J. Kang, “From learning to meta-learning: Reduced training overhead and complexity for communication systems,” preprint arXiv:2001.01227, 2019.  
• E. Björnson, and P. Giselsson, “Two applications of deep learning in the physical layer of communication systems,” preprint arXiv:2001.03350, 2020.  
• M. Kulin, T. Kazaz, I. Moerman, and E. de Poorter, “A survey on machine learning-based performance improvement of wireless networks: PHY, MAC and network layer,” preprint arXiv:2001.04561, 2020.  
• Y. E. Sagduyu, Y. Shi, T. Erpek, W. Headley, B. Flowers, G. Stantchev, and Z. Lu, “When wireless security meets machine learning: Motivation, challenges, and research directions,” preprint arXiv:2001.08883, 2020.  
• F. Restuccia and T. Melodia, “Physical-Layer Deep Learning: Challenges and Applications to 5G and Beyond,” preprint arXiv:2004.10113, 2020.  
• A. Jagannath, J. Jagannath, and T. Melodia, “Redefining Wireless Communication for 6G: Signal Processing Meets Deep Learning,” preprint arXiv:2004.10715, 2020.  
• G. Cerar, H. Yetgin, M. Mohorčič, and C. Fortuna, “Machine Learning for Wireless Link Quality Estimation: A Survey,” preprint arXiv:1812.08856, 2018.  
• A. M. Elbir, and K. V. Mishra, “A Survey of Deep Learning Architectures for Intelligent Reflecting Surfaces,” preprint arXiv:2009.02540, 2020.  
• C. Qi, P. Dong, W. Ma, H. Zhang, Z. Zhang and G. Y. Li, “Acquisition of Channel State Information for mmWave Massive MIMO: Traditional and Machine Learning-based Approaches,”, preprint arXiv:2006.08894, 2020.  
• A. M. Elbir and K. V. Mishra, “Cognitive Learning-Aided Multi-Antenna Communications,” preprint arXiv:2010.03131, 2020.  
• Q.-V. Pham, N. T. Nguyen, T. Huynh-The, L. B. Le, K. Lee, and W.-J. Hwang, “Intelligent Radio Signal Processing: A Contemporary Survey,” preprint arXiv:2008.08264, 2020.

# 信号检测、信号分类和比较 #

• H. Ye, G. Y. Li and B.-H. Juang, “Power of deep learning for channel estimation and signal detection in OFDM systems,” IEEE Wireless Communications Letters, vol. 7, no. 1, pp. 114-117, February 2018.  
• N. Samuel, T. Diskin and A. Wiesel, “Deep MIMO detection,” in Proc. IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), July 2017. \[Simulation code\]  
• N. Samuel, T. Diskin and A. Wiesel, “Learning to Detect,” preprint arXiv:1805.07631, 2018. \[Simulation code\]  
• T. J. O’Shea, J. Corgan and T. C. Clancy, “Convolutional radio modulation recognition networks,” in Proc. 17th International Conference on Engineering Applications of Neural Networks (EANN), September 2016.  
• N. Farsad and A. Goldsmith, “Neural network detection of data sequences in communication systems,” IEEE Transactions on Signal Processing, vol. 66, no. 21, pp. 5663-5678, November 2018.  
• A. Caciularu and D. Burshtein, “Blind channel equalization using variational autoencoders,” in Proc. IEEE International Conference on Communications Workshops (ICC Workshops), May 2018.  
• T. J. O’Shea, T. Roy and T. C. Clancy, “Over-the-air deep learning based radio signal classification,” IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 1, pp.168-179, February 2018.  
• T. J. O’Shea, L. Pemula, D. Batra and T. C. Clancy, “Radio transformer networks: attention models for learning to synchronize in wireless systems,” in Proc. Asilomar Conference on Signals, Systems and Computers, October 2016.  
• D. Neumann, T. Wiese and W. Utschick, “Learning the MMSE channel estimator,” IEEE Transactions on Signal Processing, vol.11, no. 66, pp. 2905-2917, June 2018.  
• C.-K. Wen, W.-T. Shih and S. Jin, “Deep learning for massive MIMO CSI feedback,” IEEE Wireless Communications Letters, vol. 7, no. 5, pp. 748-751, October 2018. \[Simulation code\]  
• P. Jiang, T. Wang, B. Han, X. Gao, J. Zhang, C-K. Wen, S. Jin and G. Y. Li, “Artificial intelligence-aided OFDM receiver: Design and experimental results,” preprint arXiv:1812.06638, 2018.  
• S. Takabe, M. Imanishi, T. Wadayama and K. Hayashi, “Trainable projected gradient detector for massive overloaded MIMO channels: Data-driven tuning approach,” preprint arXiv:1812.10044, 2018.  
• C. Häger and H. D. Pfister, “Nonlinear interference mitigation via deep neural networks,” in Proc. Optical Fiber Communications Conference and Exposition (OFC), March 2018.  
• G. Cerar, M. Mohorčič, T. Gale and C. Fortuna, “Analysis of machine learning for link quality estimation,” preprint arXiv:1812.08856, 2018.  
• N. Shlezinger and Y. C. Eldar, “Deep task-based quantization,” preprint arXiv:1908.06845, 2019.  
• J. Choi, Y. Cho, B. L. Evans and A. Gatherer, “Robust learning-Based ML detection for massive MIMO systems with one-bit quantized signals,” preprint arXiv:1811.12645, 2018.  
• C. Lu, W. Xu, S. Jin and K. Wang, “Bit-level optimized neural network for multi-antenna channel quantization,” preprint arXiv:1909.10730, 2019.  
• Y. Zhang, M. Alrabeiah, and A. Alkhateeb, “Deep learning for massive MIMO with 1-bit ADCs: When more antennas need fewer pilots,” preprint arXiv:1910.06960, 2019. \[Simulation code\]  
• E. Balevi, and J. G. Andrews, “Two-stage learning for uplink channel estimation in one-bit massive MIMO,” preprint arXiv:1911.12461, 2019.  
• B. Poudel, J. Oshima, H. Kobayashi and K. Iwashita, “MIMO detection using a deep learning neural network in a mode division multiplexing optical transmission system,” Optics Communications, vol. 440, pp. 41-48, June 2019.  
• V. Corlay, J. J. Boutros, P. Ciblat and L. Brunel, “Multilevel MIMO detection with deep learning,” preprint arXiv:1812.01571, 2018.  
• Q. Zhou, C. Yang, A. Liang, X. Zheng and Z. Chen, “Low computationally complex recurrent neural network for high speed optical fiber transmission,” Optics Communications, 2019.  
• A. Klautau, N. González-Prelcic, A. Mezghani and R. W. Heath, “Detection and channel equalization with deep learning for low resolution MIMO systems,” in Proc. 52nd Asilomar Conference on Signals, Systems, and Computers, 2018.  
• N. Farsad and A. Goldsmith, “Detection over rapidly changing communication channels using deep learning,” in Proc. 52nd Asilomar Conference on Signals, Systems, and Computers, 2018.  
• E. Balevi and J. G. Andrews, “Reliable low resolution OFDM receivers via deep learning,” in Proc. 52nd Asilomar Conference on Signals, Systems, and Computers, 2018.  
• X. Cheng, D. Liu, C. Wang, S. Yan and Z. Zhu, “Deep learning based channel estimation and equalization scheme for FBMC/OQAM systems,” in IEEE Wireless Communications Letters, 2019.  
• Y. Wang, M. Liu, J. Yang and G. Gui, “Data-driven deep learning for automatic modulation recognition in cognitive radios,” in IEEE Transactions on Vehicular Technology, 2019.  
• S. Park, H. Jang, O. Simeone and J. Kang, “Learning how to demodulate from few pilots via meta-learning,” preprint arXiv:1903.02184, 2019.  
• C. Huang, G. C. Alexandropoulos, A. Zappone, C. Yuen and M. Debbah, “Deep learning for UL/DL channel calibration in generic massive MIMO systems,” preprint arXiv:1903.02875, 2019.  
• J. Zhang, C.-K. Wen, S. Jin and G. Y. Li, “Artificial intelligence-aided receiver for A CP-free OFDM system: Design, simulation, and experimental test,” preprint arXiv:1903.04766, 2019.  
• M. Li, O. Li, G. Liu and C. Zhang, “An automatic modulation recognition method with low parameter estimation dependence based on spatial transformer networks,” Appl. Sci., 2019.  
• Q. Chen, S. Zhang, S. Xu and S. Cao, “Efficient MIMO detection with imperfect channel knowledge – A deep learning approach,” preprint arXiv:1903.07831, 2019.  
• Y.-S. Jeon, N. Lee and H. V. Poor, “Robust data detection for MIMO systems with one-bit ADCs: A reinforcement learning approach,” preprint arXiv:1903.12546, 2019.  
• Y. Yang, F. Gao, X. Ma and S. Zhang, “Deep learning-based channel estimation for doubly selective fading channels,” in IEEE Access, 2019.  
• A. Aboutaleb, W. Fatnassi, M. Soltani, and Z. Rezki, “Symbol detection and channel estimation using neural networks in optical communication systems,” IEEE International Conference on Communications (ICC): Wireless Communications Symposium, 2019.  
• Z. Jia, W. Cheng and H. Zhang, “A partial learning based detection scheme for massive MIMO,” in IEEE Wireless Communications Letters, 2019.  
• G. Gao, C. Dong and K. Niu, “Sparsely connected neural network for massive MIMO detection,” EasyChair Preprint no. 376, 2018.  
• K. W. McClintick and A. M. Wyglinski, “Physical layer neural network framework for training data formation,” in Proc. IEEE 88th Vehicular Technology Conference (VTC-Fall), 2018.  
• J. Zhang, H. He, C. Wen, S. Jin and G. Y. Li, “Deep learning based on orthogonal approximate message passing for CP-free OFDM,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019.  
• P. Gorday, N. Erdöl and H. Zhuang, “LMS to deep learning: How DSP analysis adds depth to learning,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019.  
• T. Van Luong, Y. Ko, N. A. Vien, D. H. N. Nguyen and M. Matthaiou, “Deep learning-based detector for OFDM-IM,” in IEEE Wireless Communications Letters, 2019.  
• E. Balevi and J. G. Andrews, “Deep learning-based channel estimation for high-dimensional signals,” preprint arXiv:1904.09346, 2019.  
• A. Al-Baidhani and H. H. Fan, “Learning for detection: A deep learning wireless communication receiver over Rayleigh fading channels,” in Proc. International Conference on Computing, Networking and Communications (ICNC), 2019.  
• S. Rajendran, W. Meert, D. Giustiniano, V. Lenders and S. Pollin, “Deep learning models for wireless signal classification with distributed low-cost spectrum sensors,” in IEEE Transactions on Cognitive Communications and Networking, 2018.  
• T. Wu, “CNN and RNN-based deep learning methods for digital signal demodulation,” in Proc. International Conference on Image, Video and Signal Processing, 2019.  
• S. Ramjee, S. Ju, D. Yang, X. Liu, A. El Gamal, and Y. C. Eldar, “Fast Deep Learning for Automatic Modulation Classification,” preprint arXiv:1901.05850, 2019. \[Simulation code\]  
• Z. Zhao, M. C. Vuran, F. Guo, and S. Scott, “Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex Convolutional Networks,” preprint arXiv:1810.07181, 2018. \[Simulation code\]  
• F. Meng, P. Chen, L. Wu, and X. Wang, “Automatic Modulation Classification: A Deep Learning Enabled Approach,” IEEE Transactions on Vehicular Technology, vol. 67, no. 11, pp. 10760-10772, 2018. \[Simulation code\]  
• E. Yamazaki and N. Farsad and A. Goldsmith, “Low noise non-linear equalization using neural networks and belief propagation,” preprint arXiv:1905.04893, 2019.  
• C. Huang, G. C. Alexandropoulos, A. Zappone, C. Yuen and M. Debbah, “Deep learning for UL/DL channel calibration in generic massive MIMO systems,” preprint arXiv:1903.02875, 2019.  
• L. Chu, H. Li and R. C. Qiu, “LEMO: Learn to equalize for MIMO-OFDM systems with low-resolution ADCs,” preprint arXiv:1905.06329, 2019.  
• S. Ramjee, S. Ju, D. Yang, X. Liu, A. El Gamal and Y. C. Eldar, “Fast deep learning for automatic modulation classification,” preprint arXiv:1901.05850, 2019.  
• H. Liu, Y. Liu and M. Yang, “A novel demodulation and estimation algorithm for blackout communication: Extract principal components with deep learning,” preprint arXiv:1905.11229, 2019.  
• N. Shlezinger, N. Farsad, Y. C. Eldar and A. J. Goldsmith, “ViterbiNet: A deep learning based Viterbi algorithm for symbol detection,” preprint arXiv:1905.10750, 2019.  
• S. Hu, D. Kapetanovic, N. Wang and W. Hu, “Deep-neural-network based fall-back mechanism in interference-aware receiver design,” preprint arXiv:1905.10890, 2019.  
• T.L. Pham, H. Nguyen, T. Nguyen and Y. M. Jang, “A novel neural network-based method for decoding and detecting of the DS8-PSK scheme in an OCC system,” Appl. Sci., 2019.  
• W. Xie, S. Hu, C. Yu, P. Zhu, X. Peng and J. Ouyang, “Deep learning in digital modulation recognition using high order cumulants,” in IEEE Access, 2019.  
• K. Yashashwi, A. Sethi and P. Chaporkar, “A learnable distortion correction module for modulation recognition,” in IEEE Wireless Communications Letters, 2019.  
• S. Zheng, P. Qi, S. Chen and X. Yang, “Fusion methods for CNN-based automatic modulation classification,” in IEEE Access, 2019.  
• P. Hand and B. Joshi, “Global guarantees for blind demodulation with generative priors,” preprint arXiv:1905.12576, 2019.  
• Y. Wei, M.-M. Zhao, M. Hong, M.-J. Zhao and M. Lei, “Learned conjugate gradient descent network for massive MIMO detection,” preprint arXiv:1906.03814, 2019.  
• J. Liu, K. Mei, X. Zhang, D. Ma and J. Wei, “Online extreme learning machine-based channel estimation and equalization for OFDM systems,” in IEEE Communications Letters, 2019.  
• S. Scholl, “Classification of radio signals and HF transmission modes with deep learning,” preprint arXiv:1906.04459, 2019.  
• M. Khani, M. Alizadeh, J. Hoydis and P. Fleming, “Adaptive neural signal detection for massive MIMO,” preprint arXiv:1906.04610, 2019.  
• L. V. Nguyen, D. T. Ngo, N. H. Tran, A. L. Swindlehurst and D. H. N. Nguyen, “Supervised and semi-supervised learning for MIMO blind detection with low-resolution ADCs,” preprint arXiv:1906.04090, 2019.  
• J. Guo, C.-K. Wen, S. Jin and G. Ye Li, “Convolutional neural network based multiple-rate compressive sensing for massive MIMO CSI feedback: Design, simulation, and analysis,” preprint arXiv:1906.06007, 2019.  
• S. S. Mosleh, L. Liu, C. Sahin, Y. R. Zheng and Y. Yi, “Brain-inspired wireless communications: Where reservoir computing meets MIMO-OFDM,” in IEEE Transactions on Neural Networks and Learning Systems, 2018.  
• L. V. Nguyen, D. T. Ngo, N. H. Tran, A. L. Swindlehurst, and D. H. N. Nguyen, “Supervised and semi-supervised learning for MIMO blind detection with low-resolution ADCs,” preprint arXiv:1906.04090, 2019.  
• S. Xu, R. Wang, J. Chen, L. Yu and W. Zou, “Deep learning scheme for microwave photonic analog broadband signal recovery,” preprint arXiv:1907.07312, 2019.  
• P. Song, F. Gong and Q. Li, “Deep learning based blind symbol packing ratio estimation for faster-than-Nyquist signaling,” preprint arXiv:1907.05606, 2019.  
• M. H. Shah and X. Dang, “Classification of spectrally efficient constant envelope modulations based on radial basis function network and deep learning,” in IEEE Communications Letters., 2019.  
• C.-F. Teng, H.-M. Ou and A.-Y. Wu, “Neural network-based equalizer by utilizing coding gain in advance,” preprint arXiv:1907.04980, 2019.  
• T.-H. Li, M. R. A. Khandaker, F. Tariq, K.-K. Wong and R. T. Khan, “Learning the wireless V2I channels using deep neural networks,” preprint arXiv:1907.04831, 2019.  
• Z. Chen, D. Li and Y. Xu, “Deep MIMO detection scheme for high-speed railways with wireless big data,” in Proc. IEEE Vehicular Technology Conference (VTC-Spring), 2019.  
• F. Liu, Y. Zhou and Y. Liu, “A deep neural network method for automatic modulation recognition in OFDM with index modulation,” in Proc. IEEE Vehicular Technology Conference (VTC-Spring), 2019.  
• C. Lin, Q. Chang and X. Li, “A deep learning approach for MIMO-NOMA downlink signal detection,” Sensors, 2019.  
• Z. Zhou, L. Liu and H.-H.Chang, “Learn to demodulate: MIMO-OFDM symbol detection through downlink pilots,” preprint arXiv:1907.01516, 2019.  
• O. Shental and J. Hoydis, ““Machine LLRning”: Learning to softly demodulate,” preprint arXiv:1907.01512, 2019.  
• Q. Yang, M. B. Mashhadi and D. Gündüz, “Deep convolutional compression for massive MIMO CSI feedback,” preprint arXiv:1907.02942, 2019.  
• S. Han, Y. Oh and C. Song, “A deep learning based channel estimation scheme for IEEE 802.11p systems,” in Proc. IEEE International Conference on Communications (ICC), 2019.  
• H. Mao, H. Lu, Y. Lu and D. Zhu, “RoemNet: Robust meta learning based channel estimation in OFDM systems,” in Proc. IEEE International Conference on Communications (ICC), 2019.  
• A. Li, Y. Ma, S. Xue, N. Yi, R. Tafazolli and T. E. Dodgson, “Unsupervised deep learning for blind multiuser frequency synchronization in OFDMA uplink,” in Proc. IEEE International Conference on Communications (ICC), 2019.  
• S. Xue, Y. Ma, A. Li, N. Yi and R. Tafazolli, “On unsupervised deep learning solutions for coherent MU-SIMO detection in fading channels,” in Proc. IEEE International Conference on Communications (ICC), 2019.  
• B. Liu, S. Li, Y. Xie and J. Yuan, “Deep learning assisted sum-product detection algorithm for faster-than-Nyquist signaling,” preprint arXiv:1907.09225, 2019.  
• H. He, C.-K.Wen, S. Jin and G. Y. Li, “Model-driven deep learning for joint MIMO channel estimation and signal detection,” preprint arXiv:1907.09439, 2019.  
• Q. Zhou and C. Yang, “AdaNN: Adaptive neural network-based equalizer via online semi-supervised learning for high-speed optical fiber communication,” preprint arXiv:1907.10258, 2019.  
• I. Abidi, M. Hizem, I. Ahriz, M. Cherif and R. Bouallegue, “Convolutional neural networks for blind decoding in sparse code multiple access,” in Proc. International Wireless Communications & Mobile Computing Conference (IWCMC), 2019.  
• C. Qing, B. Cai, Q. Yang, J. Wang and C. Huang, “Deep learning for CSI feedback based on superimposed coding,” in IEEE Access, 2019. \[Simulation code\]  
• E. Balevi, A. Doshi and J. G. Andrews, “Massive MIMO Channel Estimation With an Untrained Deep Neural Network,” in IEEE Transactions on Wireless Communications, vol. 19, no. 3, pp. 2079-2090, March 2020.  
• P. Siyari, H. Rahbari and M. Krunz, “Lightweight machine learning for efficient frequency-offset-aware demodulation,” in IEEE Journal on Selected Areas in Communications., 2019.  
• S. Gao, P. Dong, Z. Pan and G. Y. Li, “Deep learning based channel estimation for massive MIMO with mixed-resolution ADCs,” preprint arXiv:1908.06245, 2019.  
• X. Li and H. Wu, “Spatio-temporal representation with deep neural recurrent network in MIMO CSI feedback,” preprint arXiv:1908.07934, 2019.  
• P. Triantaris, E. Tsimbalo, W. H. Chin and D. Gündüz, “Automatic modulation classification in the presence of interference,” in Proc. European Conference on Networks and Communications (EuCNC), 2019.  
• H. Gu, Y. Wang, S. Hong and G. Gui, “Blind channel identification aided generalized automatic modulation recognition based on deep learning,” in IEEE Access, 2019.  
• S. Park, H. Jang, O. Simeone and J. Kang, “Learning to demodulate from few pilots via offline and online meta-learning,” preprint arXiv:1908.09049, 2019.  
• Q. Bai, J. Wang, Y. Zhang and J. Song, “Deep learning based channel estimation algorithm over time selective fading channels,” preprint arXiv:1908.11013, 2019.  
• S. Zhou, Y. He, Y. Liu and C. Li, “Multi-channel deep networks for block-based image compressive sensing,” preprint arXiv:1908.11221, 2019.  
• N. T. Nguyen and K. Lee, “Deep learning-aided tabu search detection for large MIMO systems,” preprint arXiv:1909.01683, 2019.  
• S. Lohani and R. T. Glasser, “Generative machine learning for robust free-space communication,” preprint arXiv:1909.02249, 2019.  
• C. Liu, Q. Zhou, X. Wang and K. Chen, “MIMO signal multiplexing and detection based on compressive sensing and deep learning,” in IEEE Access., 2019.  
• A. Lee-Leon, C. Yuen and D. Herremans, “Doppler invariant demodulation for shallow water acoustic communications using deep belief networks,” preprint arXiv:1909.02850, 2019.  
• K. Liao, G. Tao, Y. Zhong, Y. Zhang and Z. Zhang, “Sequential convolutional recurrent neural networks for fast automatic modulation classification,” preprint arXiv:1909.03050, 2019.  
• T. Koike-Akino, Y. Wang, D. S. Millar, K. Kojima and K. Parsons, “Neural turbo equalization to mitigate fiber nonlinearity,” European Conference on Optical Communication (ECOC), 2019.  
• J. Ahrens, L. Ahrens and H. D. Schotten, “A machine learning method for prediction of multipath channels,” preprint arXiv:1909.04824, 2019.  
• S. Chen, S. Zheng, L. Yang and X. Yang, “Deep learning for large-scale real-world ACARS and ADS-B radio signal classification,” in IEEE Access, 2019.  
• M. A. Amirabadi, “On the performance of some new multiuser FSO-MIMO communication systems,” preprint arXiv:1909.05147, 2019.  
• M. A. Amirabadi, “Novel suboptimal approaches for hyperparameter tuning of deep neural network \[under the shelf of optical communication\],” preprint arXiv:1907.00036, 2019.  
• Z. Jia, W. Cheng and H. Zhang, “A partial learning-based detection scheme for massive MIMO,” in IEEE Wireless Communications Letters, 2019.  
• A. Mohammad, C. Masouros and Y. Andreopoulos, “Complexity-scalable neural network based MIMO detection with learnable weight scaling,” preprint arXiv:1909.06943, 2019.  
• J. Sun, Y. Zhang and J. Xue, “Learning to search for MIMO detection,” preprint arXiv:1909.07858, 2019.  
• A. Amari, X. Lin, O. A. Dobre, R. Venkatesan and A. Alvarado, “Fiber nonlinearity mitigation via the Parzen window classifier for dispersion managed and unmanaged links,” preprint arXiv:1909.08188, 2019.  
• M. A. Amirabadi, “Deep learning for channel estimation in FSO communication system,” preprint arXiv:1909.11003, 2019.  
• M. A. Amirabadi, “A deep learning based solution for imperfect CSI problem in correlated FSO communication channel,” preprint arXiv:1909.11002, 2019.  
• Y. Shi, K. Davaslioglu, Y. E. Sagduyu, W. C. Headley, M. Fowler and G. Green ,”Deep learning for RF signal classification in unknown and dynamic spectrum environments,” preprint arXiv:1909.11800, 2019.  
• B. Shamasundar and A. Chockalingam, “A DNN architecture for the detection of generalized spatial modulation signals,” preprint arXiv:1910.01948, 2019.  
• S. Chaudhari, H. Kwon, and K.-B. Song, “Reliable and low-complexity MIMO detector selection using neural network,” preprint arXiv:1910.05369, 2019.  
• N. Athreya, V. Raj, and S. Kalyani, “Beyond 5G: Leveraging cell free TDD massive MIMO using cascaded deep learning,” preprint arXiv:1910.05705, 2019.  
• S. Soltani, Y. E. Sagduyu, R. Hasan, K. Davaslioglu, H. Deng, and T. Erpek, “Real-time and embedded deep learning on FPGA for RF signal classification,” preprint arXiv:1910.05765, 2019.  
• Q. Cheng, Z. Shi, D. N. Nguyen and E. Dutkiewicz, “Sensing OFDM signal: A deep learning approach,” in IEEE Transactions on Communications, 2019.  
• S. Khan and S. Y. Shin, “Deep-learning-aided detection for reconfigurable intelligent surfaces,” preprint arXiv:1910.09136, 2019.  
• W. Yan, Q. Ling, and L. Zhang, “Convolutional neural networks for space-time block coding recognition,” preprint arXiv:1910.09952, 2019.  
• M. B. Mashhadi, Q. Yang, and D. Gunduz, “CNN-based analog CSI feedback in FDD MIMO-OFDM systems,” preprint arXiv:1910.10428, 2019.  
• S. Takabe, Y. Yamauchi, and T. Wadayama, “Trainable projected gradient detector for sparsely spread code division multiple access,” preprint arXiv:1910.10336, 2019.  
• X. Kuai, X. Yuan, W. Yan, H. Liu, and Y. Jun (A.) Zhang, “Double-sparsity learning based channel-and-signal estimation in massive MIMO with generalized spatial modulation,” preprint arXiv:1910.11504, 2019.  
• K. Davaslioglu, S. Soltani, T. Erpek, and Y. E. Sagduyu, “DeepWiFi: Cognitive WiFi with deep learning,” preprint arXiv:1910.13315, 2019.  
• Y. Song, M. Barzegar Khalilsarai, S. Haghighatshoar, and G. Caire, “Machine learning for geometrically-consistent angular spread function estimation in massive MIMO,” preprint arXiv:1910.13795, 2019.  
• M. Turhan, E. Öztük, and H. A. Çırpan “Deep convolutional learning-aided detector for generalized frequency division multiplexing with index modulation,” in Proc. IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2019.  
• Z. Lu, J. Wang, and J. Song, “Multi-resolution CSI feedback with deep learning in massive MIMO system,” preprint arXiv:1910.14322, 2019. \[Simulation code\]  
• A. H. Wahla, L. Chen, Y. Wang, R. Chen, “Automatic wireless signal classification: A neural-induced support vector machine-based approach,” Information, 2019.  
• H. Qiang, G. Feifei, Z. Hao, J. Shi, and L. G. Ye, “Deep learning for MIMO channel estimation: Interpretation, performance, and comparison,” preprint arXiv:1911.01918, 2019.  
• Z. Chang, Y. Wang, H. Li and Z. Wang, “Complex CNN-based equalization for communication signal,” IEEE 4th International Conference on Signal and Image Processing (ICSIP), China, 2019.  
• X. Jin and H. Kim, “Parallel deep learning detection network in the MIMO channel,” in IEEE Communications Letters., 2019.  
• Y. Han, Z. Wang, Q. Guo and W. Xiang, “Deep learning-based detection for moderate-density code multiple access in IoT networks,” in IEEE Communications Letters., 2019.  
• K. Mei, J. Liu, X. Zhang and J. Wei, “Machine learning based channel estimation: A computational approach for universal channel conditions,” preprint arXiv:1911.03886, 2019.  
• M. Mehlhose, D. A. Awany, R. L. G. Cavalcante, M. Kurras, and S. Stanczak, “Machine learning-based adaptive receive filtering: Proof-of-concept on an SDR platform,” preprint arXiv:1911.04291, 2019.  
• J. Liu and H. Lu, “IMNet: A learning based detector for index modulation aided MIMO-OFDM systems,” preprint arXiv:1911.04133, 2019.  
• T. Xu, and I. Darwazeh, “Deep learning for over-the-air non-orthogonal signal classification,” preprint arXiv:1911.06174, 2019.  
• Q. Huang, C. Zhao, M. Jiang, X. Li and J. Liang, “A novel OFDM equalizer for large Doppler shift channel through deep learning,” in Proc. IEEE 90th Vehicular Technology Conference (VTC2019-Fall), 2019.  
• S. Rostami, W. Saad and C. S. Hong, “Deep learning with persistent homology for orbital angular momentum (OAM) decoding,” preprint arXiv:1911.06858, 2019.  
• Z. Gao, Y. Wang, X. Liu, F. Zhou and K.-K. Wong, “FFDNet-based channel estimation for massive MIMO visible light communication systems,” preprint arXiv:1911.07404, 2019.  
• Ö. T. Demir and E. Björnson, “Channel estimation in massive MIMO under hardware non-linearities: Bayesian methods versus deep learning,” preprint arXiv:1911.07316, 2019.  
• Y. He, M. Jiang, X. Ling and C. Zhao, “Robust BICM design for the LDPC coded DCO-OFDM: A deep learning approach,” in IEEE Transactions on Communications., 2019.  
• Y. He, M. Jiang and C. Zhao, “Gaussian mixture learning for LDPC coded BICM receivers with blanking nonlinearity,” in IEEE Access., 2019.  
• S. A. I. Alfarozi, K. Pasupa, H. Hashizume, K. Woraratpanya and M. Sugimoto, “Robust and unified VLC decoding system for square wave quadrature amplitude modulation using deep learning approach,” in IEEE Access, 2019.  
• S. Şahin, C. Poulliat, A. M. Cipriano and M. Boucheret, “Doubly iterative turbo equalization: Optimization through deep unfolding,” in Proc. IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2019.  
• M. Camelo, A. Shahidy, J. Fontainey, F. A. P. de Figueiredoy, E. De Poortery, I. Moermany, and S. Latre, “A semi-supervised learning approach towards automatic wireless technology recognition,” in Proc. IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), 2019.  
• X. Ru, L. Wei, and Y. Xu, “Model-driven channel estimation for OFDM systems based on image super-resolution network,” preprint arXiv:1911.13106, 2019.  
• J. Zhu, Q. Li, L. Hu, H. Chen, and N. Ansari, “Machine learning-based signal detection for PMH signals in load-modulated MIMO system,” preprint arXiv:1911.13238, 2019.  
• T. Faghani, A. Shojaeifard, K. Wong and A. H. Aghvami, “Deep learning-based decision region for MIMO detection,” in Proc. IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2019.  
• L. Sun and Y. Wang, “CTBRNN: A novel deep-learning based signal sequence detector for communications systems,” in IEEE Signal Processing Letters., 2019.  
• N. Turan and W .Utschick, “Reproducible evaluation of neural network based channel estimators and predictors using a generic dataset,” preprint arXiv:1912.00005, 2019.  
• L. Huang, W. Pan, Y. Zhang, L. Qian, N. Gao, and Y. Wu, “Data augmentation for deep learning-based radio modulation classification,” preprint arXiv:1912.03026, 2019.  
• M. Du, Q. Yu, S. Fei, C. Wang, X. Gong, and R. Luo, “Fully dense neural network for the automatic modulation recognition,” preprint arXiv:1912.03449, 2019.  
• A. A. Marseet and T. Y. Elganimi, “Fast detection based on customized complex valued convolutional neural network for generalized spatial modulation systems,” in Proc. IEEE Western New York Image and Signal Processing Workshop (WNYISPW), 2019.  
• Z. Zhou, “MIMO-OFDM symbol detection via echo state networks“, Master Thesis, 2019.  
• S. Chandhok, H. Joshi, A. V. Subramanyam, and S. J. Darak, “SenseNet: Deep learning based wideband spectrum sensing and modulation classification network,” preprint arXiv:1912.05255, 2019.  
• H. Gu, Y. Wang, S. Hong and G. Gui, “Deep learning aided friendly coexistence of WiFi and LTE in unlicensed bands,” in Proc. 11th International Conference on Wireless Communications and Signal Processing (WCSP), 2019.  
• Z. He et al., “Deep learning-based automatic modulation recognition algorithm in non-cooperative communication systems,” in Proc. 11th International Conference on Wireless Communications and Signal Processing (WCSP), 2019.  
• J. Jang, H. Lee, S. Hwang, H. Ren, and I. Lee, “Deep learning-based limited feedback designs for MIMO systems,” preprint arXiv:1912.09043, 2019.  
• Z. Liu, L. Zhang, and Z. Ding, “Overcoming the channel estimation barrier in massive MIMO communication systems,” preprint arXiv:1912.10573, 2019.  
• Z. Liu, L. Zhang, and Z. Ding, “An efficient deep learning framework for low rate massive MIMO CSI reporting,” preprint arXiv:1912.10608, 2019.  
• Y. Yang, F. Gao, Z. Zhong, B. Ai, and A. Alkhateeb, “Deep transfer learning based downlink channel prediction for FDD massive MIMO systems,” preprint arXiv:1912.12265, 2019.  
• D. Weon and K. Lee, “Learning-aided deep path prediction for sphere decoding in large MIMO systems,” preprint arXiv:2001.00342, 2020.  
• A. Al-Baidhani and H. H. Fan, “Deep ensemble learning: A communications receiver over wireless fading channels,” in Proc. IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2019.  
• L. Ma and J. Kaewell, “Fast monte carlo dropout and error correction for radio transmitter classification,” preprint arXiv:2001.11963, 2020.  
• M. Goutay, F. Ait Aoudia, and J. Hoydis, “Deep HyperNetwork-based MIMO detection,” preprint arXiv:2002.02750, 2020.  
• S. Li, W. Zhang, and Y. Cui, “Jointly sparse signal recovery via deep auto-encoder and parallel coordinate descent unrolling,” preprint arXiv:2002.02628, 2020.  
• Y. Al-Eryani, M. Akrout, and E. Hossain, “Multiple access in dynamic cell-free networks: Outage performance and deep reinforcement learning-based design,” preprint arXiv:2002.02801, 2020.  
• N. Shlezinger, R. Fu, and Y. C. Eldar, “DeepSIC: Deep soft interference cancellation for multiuser MIMO detection,” preprint arXiv:2002.03214, 2020.  
• N. Farsad, N. Shlezinger, A. J. Goldsmith, and Y. C. Eldar, “Data-driven symbol detection via model-based machine learning,” preprint arXiv:2002.07806, 2020.  
• M. B. Mashhadi, and D. Gündüz, “Deep learning for massive MIMO channel state acquisition and feedback,” preprint arXiv:2002.06945, 2020.  
• M. van Lier, A. Balatsoukas-Stimming, H. Corporaaal, and Z. Zivkovic, “OptComNet: Optimized neural networks for low-complexity channel estimation,” preprint arXiv:2002.10493, 2020.  
• Y. Han, M. Li, S. Jin, C.-K. Wen, and X. Ma, “Deep learning based FDD non-stationary massive MIMO downlink channel reconstruction,” preprint arXiv:2002.09858, 2020.  
• Q. Yang, M. B. Mashhadi, and D. Gunduz, “Distributed Deep Convolutional Compression for Massive MIMO CSI Feedback,” preprint arXiv:2003.04684, 2020.  
• X. Ma and Z. Gao, “Data-Driven Deep Learning to Design Pilot and Channel Estimator For Massive MIMO,” preprint arXiv:2003.05875, 2020.  
• M. Soltani, V. Pourahmadi, and H. Sheikhzadeh, “Pilot Pattern Design for Deep Learning-Based Channel Estimation in OFDM Systems,” preprint arXiv:2003.08980, 2020.  
• M. Shohat, G. Tsintsadze, N. Shlezinger and Y. C. Eldar, “Deep quantization for MIMO channel estimation,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019.  
• S. Kim, S.-N. Hong, “Semi-supervised learning detector for MU-MIMO systems with one-bit ADCs,” preprint arXiv:1902.00866, 2019.  
• Y.-S. Jeon, J. Li, N. Tavangaran, and H. V. Poor, “Data-Aided Channel Estimator for MIMO Systems via Reinforcement Learning,” preprint arXiv:2003.10084, 2020.  
• P. Gorday, N. Erdöl and H. Zhuang, “Flexible FSK learning demodulator,” in Proc. IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York City, NY, USA, 2018.  
• C. Lu, W. Xu, H. Shen, J. Zhu and K. Wang, “MIMO Channel Information Feedback Using Deep Recurrent Network,” in IEEE Communications Letters, vol. 23, no. 1, pp. 188-191, Jan. 2019.  
• C. Teng, C. Liao, C. Chen and A. A. Wu, “Polar Feature Based Deep Architectures for Automatic Modulation Classification Considering Channel Fading,” in Proc. IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2018.  
• Y. Sun, C. Wang, H. Cai, C. Zhao, Y. Wu, and Y. Chen, “Deep Learning Based Equalizer for MIMO-OFDM Systems with Insufficient Cyclic Prefix,” preprint arXiv:2007.11757, 2020.  
• L. Sun and Y. Wang, “CTBRNN: A Novel Deep-Learning Based Signal Sequence Detector for Communications Systems,” in IEEE Signal Processing Letters, vol. 27, pp. 21-25, 2020.  
• A. H. Wahla, L. Chen, Y. Wang, R. Chen and F. Wu, “Automatic Wireless Signal Classification in Multimedia Internet of Things: An Adaptive Boosting Enabled Approach,” in IEEE Access, vol. 7, pp. 160334-160344, 2019.  
• D. Kim, S.-N. Hong, and N. Lee, “Supervised-learning for multi-hop MU-MIMO communications with one-bit transceivers,” in IEEE Journal on Selected Areas in Communications., 2019.  
• C.-F. Teng, C.-Y. Chou, C.-H. Chen, and A.-Y. Wu, “Accumulated Polar Feature-based Deep Learning for Efficient and Lightweight Automatic Modulation Classification with Channel Compensation Mechanism,” preprint arXiv:2001.01395, 2020.  
• T. Xu and I. Darwazeh, “Design and Prototyping of Neural Network Compression for Non-Orthogonal IoT Signals,” IEEE Wireless Communications and Networking Conference (WCNC), 2019.  
• T. Huynh-The, C. Hua, Q. Pham and D. Kim, “MCNet: An Efficient CNN Architecture for Robust Automatic Modulation Classification,” in IEEE Communications Letters, vol. 24, no. 4, pp. 811-815, April 2020.  
• E. Beck, C. Bockelmann and A. Dekorsy, “Concrete MAP Detection: A Machine Learning Inspired Relaxation,” 24th International ITG Workshop on Smart Antennas, Hamburg, Germany, 2020.  
• S. Khobahi, N. Naimipour, M. Soltanalian and Y. C. Eldar, “Deep signal recovery with one-bit quantization,” preprint arXiv:1812.00797, 2018.  
• D. Gao and Q. Guo, “Extreme learning machine-based receiver for MIMO LED communications,” Digital Signal Processing, 2019.  
• D. Gao, Q. Guo, J. Tong, N. Wu, J. Xi and Y. Yu, “Extreme-Learning-Machine-Based Noniterative and Iterative Nonlinearity Mitigation for LED Communication Systems,” in IEEE Systems Journal, 2020.  
• D. Gao, and Q. Guo, “Massive MIMO As Extreme Learning Machine,” preprint arXiv:2007.00221, 2020.  
• M. H. Jespersen, M. Pajovic, T. Koike-Akino, Y. Wang, P. Popovski and P. V. Orlik, “Deep Learning for Synchronization and Channel Estimation in NB-IoT Random Access Channel,” IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, 2019.  
• T. Fujihashi, T. Koike-Akino, T. Watanabe and P. V. Orlik, “Nonlinear Equalization with Deep Learning for Multi-Purpose Visual MIMO Communications,” IEEE International Conference on Communications (ICC), Kansas City, MO, 2018.  
• Y.-S. Jeon, S.-N. Hong, and N. Lee, “Supervised-Learning-Aided Communication Framework for MIMO Systems with Low-Resolution ADCs,” preprint arXiv:1610.07693, 2020.  
• A. Doshi, E. Balevi and J. G. Andrews, “Compressed Representation of High Dimensional Channels using Deep Generative Networks,” IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Atlanta, GA, USA, 2020.  
• E. Balevi, A. Doshi, A. Jalal, A. Dimakis, and J. G. Andrews, “High Dimensional Channel Estimation Using Deep Generative Networks,” preprint arXiv:2006.13494, 2020.  
• R. M. Dreifuerst, R. W. Heath, M. N. Kulkarni and J. Charlie, “Deep Learning-based Carrier Frequency Offset Estimation with One-Bit ADCs,” IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Atlanta, GA, USA, 2020.  
• X. Yi, and C. Zhong, “Deep Learning for Joint Channel Estimation and Signal Detection in OFDM Systems,” preprint arXiv:2008.03977, 2020.  
• Y. Sun, W. Xu, L. Fan, G. Y. Li, and G. K. Karagiannidis, “AnciNet: An Efficient Deep Learning Approach for Feedback Compression of Estimated CSI in Massive MIMO Systems,” preprint arXiv:2008.07112, 2020.  
• J. Cha, J. Choi, and D. J. Love, “Noncoherent OOK Symbol Detection with Supervised-Learning Approach for BCC,” preprint arXiv:2008.08286, 2020.  
• T. Huynh-The, V.-S. Doan, C.-H. Hua, Q.-V. Pham, and D.-S. Kim, “Chain-Net: Learning Deep Model for Modulation Classification Under Synthetic Channel Impairment,” preprint arXiv:2009.02023, 2020.  
• V.-S. Doan, T. Huynh-The, C.-H. Hua, Q.-V. Pham, and D.-S. Kim, “Learning Constellation Map with Deep CNN for Accurate Modulation Recognition,” preprint arXiv:2009.02026, 2020.  
• C. Liu, Z. Wei, D. W. K. Ng, J. Yuan, and Y.-C. Liang, “Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications,” preprint arXiv:2009.05231, 2020.  
• X. Liu, C. Liu, Y. Li, B. Vucetic, and D. W. K. Ng, “Deep Residual Learning-Assisted Channel Estimation in Ambient Backscatter Communications,” preprint arXiv:2009.07468, 2020.  
• H. Kim, S. Kim, H. Lee, C. Jang, Y. Choi, and J. Choi, “Massive MIMO Channel Prediction:Kalman Filtering vs. Machine Learning,” preprint arXiv:2009.09967, 2020.  
• S. Schwarz, “Recursive CSI Quantization of Time-Correlated MIMO Channels by Deep Learning Classification,” preprint arXiv:2009.13560, 2020.  
• Y. Zhang, A. Doshi, R. Liston, W.-T. Tan, X. Zhu, J. G. Andrews, and R. W. Heath, “DeepWiPHY: Deep Learning-based Receiver Design and Dataset for IEEE 802.11ax Systems,” preprint arXiv:2010.09268, 2020.  
• N. T. Nguyen, K. Lee, and H. Dai, “Application of Deep Learning to Sphere Decoding for Large MIMO Systems,” preprint arXiv:2010.13481, 2020.  
• D. Korpi, M. Honkala, J. M.J. Huttunen, and V. Starck, “DeepRx MIMO: Convolutional MIMO Detection with Learned Multiplicative Transformations,” preprint arXiv:2010.16283, 2020.  
• Z. Lu, J. Wang, and J. Song, “Binary Neural Network Aided CSI Feedback in Massive MIMO System,” preprint arXiv:2011.02692, 2020.  
• M. Hussien, K. K. Nguyen, and M. Cheriet, “PRVNet: Variational Autoencoders for Massive MIMO CSI Feedback,” preprint arXiv:2011.04178, 2020.  
• A. Mayouche, W. A. Martins, C. G. Tsinos, S. Chatzinotas, and B. Ottersten, “Deep Modulation Recognition with Multiple Receive Antennas: An End-to-end Feature Learning Approach,” preprint arXiv:2008.06720, 2020.  
• C. Liu, X. Liu, Z. Wei, D. W. K. Ng, J. Yuan, and Y.-C. Liang, “Deep Transfer Learning-Assisted Signal Detection for Ambient Backscatter Communications,” preprint arXiv:2011.05574, 2020.  
• J. Guo, C.-K. Wen, and S. Jin, “Deep Learning-Based CSI Feedback for Beamforming in Single- and Multi-cell Massive MIMO Systems,” preprint arXiv:2011.06099, 2020.  
• H. Sun, W. Pu, M. Zhu, X. Fu, T.-H. Chang, and M. Hong, “Deep Learning for Joint Channel Estimation and Feedback in Massive MIMO Systems,” preprint arXiv:2011.07242, 2020.  
• M. B. Mashhadi, N. Shlezinger, Y. C. Eldar, and D. Gunduz, “FedRec: Federated Learning of Universal Receivers over Fading Channels,” preprint arXiv:2011.07271, 2020.  
• S. Zheng, X. Zhou, S. Chen, P. Qi, and X. Yang, “DemodNet: Learning Soft Demodulation from Hard Information Using Convolutional Neural Network,” preprint arXiv:2011.11337, 2020.  
• K. Chitti, J. Vieira, and B. Makki, “Deep-Learning based Multiuser Detection for NOMA,” preprint arXiv:2011.11752, 2020.  
• Y. Yang, F. Gao, C. Xing, J. An, and A. Alkhateeb, “Deep Multimodal Learning: Merging Sensory Data for Massive MIMO Channel Prediction,” preprint arXiv:2007.09366, 2020.  
• Y. Li, X. Wang, and R. L. Olesen, “Unfolded Deep Neural Network (UDNN) for High Mobility Channel Estimation,” preprint arXiv:2012.00563, 2020.  
• C. Liu, X. Liu, D. W. K. Ng, and J. Yuan, “Deep Residual Network Empowered Channel Estimation for IRS-Assisted Multi-User Communication Systems,” preprint arXiv:2012.00241, 2020.

# 信道编码和解码 #

• N. Farsad, M. Rao and A. Goldsmith, “Deep learning for joint source-channel coding of text,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), April 2018.  
• T. Gruber, S. Cammerer, J. Hoydis and S. ten Brink, “On deep learning-based channel decoding,” in Proc. Information Sciences and Systems (CISS), March 2017. \[Simulation code\]  
• E. Nachmani, E. Marciano, L. Lugosch, W. J. Gross, D. Burshtein and Y. Be’ery, “Deep learning methods for improved decoding of linear codes,” IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 1, pp.119-131, February 2018. \[Simulation code\]  
• L. Lugosch and W. J. Gross, “Learning from the syndrome,” in Proc. IEEE Asilomar Conference on Signal, System, Computers, October 2018. \[Simulation code\]  
• L. Lugosch and W. J. Gross, “Neural offset min-sum decoding,” in Proc. IEEE International Symposium on Information Theory (ISIT), June 2017. \[Simulation code\]  
• F. Liang, C. Shen and F. Wu, “An iterative BP-CNN architecture for channel decoding,” in IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 1, pp. 144-159, February 2018. \[Simulation code\]  
• V. Corlay, J. J. Boutros, P. Ciblat and L. Brunel, “Neural lattice decoders,” preprint arXiv:1807.00592, 2018.  
• V. Corlay, J. J. Boutros, P. Ciblat and L. Brunel, “On the CVP for the root lattices via folding with deep ReLU neural networks,” preprint arXiv:1902.05146, 2019.  
• W. Xu, X. You, C. Zhang and Y. Be’ery, “Polar decoding on sparse graphs with deep learning,” in Proc. IEEE Asilomar Conference on Signal, System, Computers, October 2018.  
• W. Xu, Z. Zhong, Y. Be’ery, X. You and C. Zhang, “Joint neural network equalizer and decoder,” in Proc. International Symposium on Wireless Communication Systems, August 2018.  
• X. Tan, W. Xu, Y. Be’ery, Z. Zhang, X. You and C. Zhang, “Improving massive MIMO belief propagation detector with deep neural network,” preprint arXiv:1804.01002, 2018.  
• E. Nachmani, Y. Bachar, E. Marciano, D. Burshtein and Y. Be’ery, “Near maximum likelihood decoding with deep learning,” in Proc. International Zurich Seminar on Informations and Communcation, February 2018.  
• E. Nachmani, E. Marciano, D. Burshtein and Y. Be’ery, “RNN decoding of linear block codes,” preprint arXiv:1702.07560, 2017.  
• E. Nachmani, Y. Be’ery and D. Burshtein, “Learning to decode linear codes using deep learning,” in Proc. 54’th Annual Allerton Conf. on Communication, Control and Computing, September 2016.  
• X. Wang, H. Zhang, R. Li, L. Huang, S. Dai, Y. Huangfu and J. Wang, “Learning to flip successive cancellation decoding of polar codes with LSTM networks,” preprint arXiv:1902.08394, 2019.  
• K. Besser, P. Lin, C. R. Janda and E. A. Jorswieck, “Machine learning assisted wiretapping,” in Proc. 52nd Asilomar Conference on Signals, Systems, and Computers, 2018.  
• M. Benammar and P. Piantanida, “Optimal training channel statistics for neural-based decoders,” in Proc. 52nd Asilomar Conference on Signals, Systems, and Computers, 2018.  
• F. Sun, K. Niu and C. Dong, “Deep learning based joint detection and decoding of non-orthogonal multiple access systems,” in Proc. IEEE Globecom Workshops (GC Wkshps), 2018.  
• X. Tan, Z. Zhong, Z. Zhang, X. You and C. Zhang, “Low-complexity message passing MIMO detection algorithm with deep neural network,” in Proc. IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2018.  
• Y. Jiang, H. Kim, H. Asnani, S. Kannan, S. Oh and P. Viswanath, “DeepTurbo: Deep turbo decoder,” preprint arXiv:1903.02295, 2019.  
• Y. Jiang, H. Kim, H. Asnani and S. Kannan, “MIND: Model independent neural decoder,” preprint arXiv:1903.02268, 2019.  
• V. Corlay, J. J. Boutros, P. Ciblat and L. Brunel, “An Turyn-based neural Leech decoder,” in Proc. International Workshop on Coding and Cryptography (WCC), 2019.  
• A. Irawan, G. Witjaksono and W. K. Wibowo, “Deep learning for polar codes over flat fading channels,” in Proc. International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2019.  
• L. Huang, H. Zhang, R. Li, Y. Ge and J. Wang, “Reinforcement learning for nested polar code construction,” preprint arXiv:1904.07511, 2019.  
• C. Deng and S. L. Bo Yuan, “Reduced-complexity deep neural network-aided channel code decoder: A case Study for BCH decoder,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019.  
• V. V. Butov and V. N. Dumachev, “Neural net decoders of binary codes,” in Journal of Physics: Conference Series, 2019.  
• M. Zhao, Q. Shi, Y. Cai, M. Zhao and Q. Yu, “Decoding binary linear codes using penalty dual decomposition method,” in IEEE Communications Letters., 2019.  
• J. Zhang, H. He, C.-K. Wen, S. Jin and G. Ye Li, “Deep learning based on orthogonal approximate message passing for CP-Free OFDM,” preprint arXiv:1905.02541, 2019.  
• S. Varsamopoulos, K. Bertels and C. G. Almudever, “Decoding surface code with a distributed neural network based decoder,” preprint arXiv:1901.10847, 2019.  
• M. Sheth, S. Z. Jafarzadeh and V. Gheorghiu, “Neural ensemble decoding for topological quantum error-correcting codes,” preprint arXiv:1905.02345, 2019.  
• H. Kim, Y. Jiang, S. Kannan, S. Oh, and P. Viswanath, “Deepcode: Feedback Codes via Deep Learning,” preprint arXiv:1807.00801, 2018. \[Simulation code\]  
• D. Tandler, S. Dörner, S. Cammerer and S. ten Brink, “On recurrent neural networks for sequence-based processing in communications,” preprint arXiv:1905.09983, 2019.  
• K. Yashashwi, D. Anand, S. R. B Pillai, P. Chaporkar and K Ganesh, “MIST: A novel training strategy for low-latency scalable neural net decoders,” preprint arXiv:1905.08990, 2019.  
• A. Caciularu and D. Burshtein, “Unsupervised linear and nonlinear channel equalization and decoding using variational autoencoders,” preprint arXiv:1905.08795, 2019.  
• L. Huang, H. Zhang, R. Li, Y. Ge and J. Wang, “AI Coding: Learning to construct error correction codes,” preprint arXiv:1901.05719, 2019.  
• Y. He, J. Zhang, C.-K. Wen and S. Jin, “TurboNet: A model-driven DNN decoder based on max-log-MAP algorithm for turbo code,” preprint arXiv:1905.10502, 2019.  
• N. Doan, S. Ali Hashemi and W. J. Gross, “Neural successive cancellation decoding of Polar codes,” in Proc. IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2018.  
• N. Doan, S. Ali Hashemi, E. N. Mambou, T. Tonnellier and W. J. Gross, “Neural belief propagation decoding of CRC-Polar concatenated codes,” preprint arXiv:1811.00124, 2018.  
• W. Lin, X. Ma, S. Cai and B. Wei, “Statistical learning aided list decoding of semi-random block oriented convolutional codes,” preprint arXiv:1905.11392, 2019.  
• Y. Hu, L. Zhao, Z. Yan, A. Kaushik, Y. Hou and J. Thompson, “GatedNet: Neural network decoding for decoding over impulsive noise channels,” in IEEE Communications Letters, 2019.  
• F. Carpi, C. Häger, M. Martalò, R. Raheli and H. D. Pfister, “Reinforcement learning for channel coding: Learned bit-flipping decoding,” in Proc. 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2019. \[Simulation code\]  
• C. Cao, D. Li and I. Fair, “Deep learning-based decoding of constrained sequence codes,” preprint arXiv:1906.06172, 2019.  
• V. Raj and S. Kalyani, “Blind decoding in α-Stable noise: An online learning approach,” preprint arXiv:1906.09811, 2019.  
• J. Wang, Y. Ma, S. Xue, N. Yi, R. Tafazolli, and T. E. Dodgson, “Parallel decoding for non-recursive convolutional codes and its enhancement through artificial neural networks,” in Proc. 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2019), 2019.  
• A. Askri and G. Rekaya-Ben Othman, “DNN assisted sphere decoder,” in Proc. IEEE International Symposium on Information Theory (ISIT), 2019.  
• I. Wodiany and A. Pop, “Low-precision neural network decoding of polar codes,” in Proc. IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2019.  
• T. L. Pham, T. Nguyen, M. D. Thieu, H. Nguyen, H. Nguyen and Y. M. Jang, “An artificial intelligence-based error correction for optical camera communication“, in Proc. The International Conference on Ubiquitous and Future Network (ICUFN), 2019.  
• I. Be’ery, N. Raviv, T. Raviv and Y. Be’ery, “Active deep decoding of linear codes,” preprint arXiv:1906.02778, 2019.  
• Z. Cao, H. Zhu, Y. Zhao and D. Li, “Learning to denoise and decode: A novel residual neural network decoder for polar codes,” preprint arXiv:1908.00460, 2019.  
• J. Gao and R. Liu, “Neural network aided SC decoder for polar codes,” in Proc. IEEE International Conference on Computer and Communications (ICCC), 2018.  
• H. Ye, L. Liang and G. Y. Li, “Circular convolutional auto-encoder for channel coding,” in Proc. IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2019.  
• E. Nachmani and L. Wolf, “Hyper-graph-network decoders for block codes,” preprint arXiv:1909.09036, 2019.  
• M. Ebada, S. Cammerer, A. Elkelesh and S. ten Brink, “Deep learning-based polar code design,” in Proc. Annual Allerton Conference on Communication, Control, and Computing, 2019.  
• A. Machireddy and S. S. Garani, “Guessing the code: Learning encoding mappings using the back propagation algorithm,” in Proc. International Joint Conference on Neural Networks (IJCNN), 2019.  
• P. Upadhyaya, and A. Jiang, “Machine learning for error correction with natural redundancy,” preprint arXiv:1910.07420, 2019.  
• A. Tato and C. Mosquera, “Deep learning assisted rate adaptation in spatial modulation links,” International Symposium on Wireless Communication Systems (ISWCS), 2019.  
• R. Zhang, F. Liu, Z. Zeng, Q. Shang and S. Zhao, “Neural network based successive cancellation decoding algorithm for polar codes in URLLC,” International Symposium on Wireless Communication Systems (ISWCS), 2019.  
• C.-F. Teng, and A.-Y. Wu, “Unsupervised learning for neural network-based Polar decoder via syndrome loss,” preprint arXiv:1911.01710, 2019.  
• C.-F. Teng, K.-S. Ho, C.-H. Wu, S.-S. Wong, and A.-Y. Wu, “Convolutional neural network-aided bit-flipping for belief propagation decoding of Polar codes,” preprint arXiv:1911.01704, 2019.  
• J. Li, X. Zhao, J. Fan, F. Shu, S. Jin and Y. Qian, “A double-CNN BP decoder on fast fading channels using correlation information,” IEEE/CIC International Conference on Communications in China (ICCC), 2019.  
• D. Xiong and B. Tian, “Deep learning method of Polar codes under colored noise,” IEEE/CIC International Conference on Communications in China (ICCC), Changchun, 2019.  
• X. Liu, S. Wu, Y. Wang, N. Zhang, J. Jiao and Q. Zhang, “Exploiting error-correction-CRC for Polar SCL decoding: A deep learning based approach,” in IEEE Transactions on Cognitive Communications and Networking, 2019.  
• Y. Wang, S. Zhang, C. Zhang, X. Chen and S. Xu, “A low-complexity belief propagation based decoding scheme for Polar codes – Decodability detection and early stopping prediction,” in IEEE Access., 2019.  
• E. Nachmani, and L. Wolf, “A gated hypernet decoder for Polar codes,” preprint arXiv:1911.03229, 2019.  
• Yihan Jiang, Hyeji Kim, Himanshu Asnani, Sreeram Kannan, Sewoong Oh, Pramod Viswanath, “Turbo autoencoder: Deep learning based channel codes for point-to-point communication channels,” preprint arXiv:1911.03038, 2019.  
• A. Abotabl, J. H. Bae and K. Song, “Offset min-sum optimization for general decoding scheduling: A deep learning approach,” in Proc. IEEE Vehicular Technology Conference (VTC2019-Fall), 2019.  
• G. Xie, Y. Luo, Y. Chen and X. ling, “Belief propagation decoding of Polar codes using intelligent post-processing,” in Proc. IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 2019.  
• R. Chaudhuri and I. Fiete, “Bipartite expander Hopfield networks asself-decoding high-capacity error correcting codes,” in Advances in Neural Information Processing Systems, 2019.”  
• T. Koike-Akino, Y. Wang, D. S. Millar, K. Kojima, and K. Parsons, “Neural turbo equalization: Deep learning for fiber-optic nonlinearity compensation,” preprint arXiv:1911.10131, 2019.  
• J. Gao, J. Dai and K. Niu, “Learning to decode Polar codes with quantized LLRs passing,” in Proc. IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2019.  
• V. Q. Pham, H. N. Dang, T. V. Nguyen, and T. V. Nguyen, “Performance of deep learning LDPC coded communications in large scale MIMO channels,” in Proc. NAFOSTED Conference on Information and Computer Science (NICS), 2019.  
• E. Kavvousanos and V. Paliouras, “Hardware implementation aspects of a syndrome-based neural network decoder for BCH codes,” in Proc. IEEE Nordic Circuits and Systems Conference (NORCAS): NORCHIP and International Symposium of System-on-Chip (SoC), 2019.  
• S. Ali Hashemi, N. Doan, T. Tonnellier, and W. J. Gross, “Deep-learning-aided successive-cancellation decoding of Polar codes,” preprint arXiv:1912.01086, 2019.  
• J. Li and W. Cheng, “Stacked denoising autoencoder enhanced polar codes over Rayleigh fading channels,” in IEEE Wireless Communications Letters., 2019.  
• C.-H. Chen, C.-F. Teng, and A.-Y. Wu, “Low-complexity LSTM-assisted bit-flipping algorithm for successive cancellation list polar decoder,” preprint arXiv:1912.05158, 2019.  
• O. P. Babalola, O. O. Ogundile and D. J. J. Versfeld, “A generalized parity-check transformation for iterative soft-decision decoding of binary cyclic codes,” in IEEE Communications Letters., 2019.  
• X. Song, Z. Zhang, J. Wang and K. Qin, “A graph-neural-network decoder with MLP-based processing cells for polar codes,” in Proc. 11th International Conference on Wireless Communications and Signal Processing (WCSP), 2019.  
• C. Wen, J. Xiong, L. Gui and L. Zhang, “A BP-NN decoding algorithm for polar codes,” in Proc. 11th International Conference on Wireless Communications and Signal Processing (WCSP), 2019.  
• S. Bi, Q. Wang, Z. Chen, J. Sun and X. Ma, “Deep learning-based decoding of block Markov superposition transmission,” in Proc. 11th International Conference on Wireless Communications and Signal Processing (WCSP), 2019.  
• X. Qin, S. Peng, X. Yang and Y. Yao, “Deep learning based channel code recognition using TextCNN,” in Proc. IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), 2019.  
• X. Xiao, B. Vasic, R. Tandon, and S. Lin, “Finite alphabet iterative decoding of LDPC codeswith coarsely quantized neural networks,” 2019.  
• A. C. Vaz, C. G. Nayak and D. Nayak, “Hamming code performance evaluation using artificial neural network decoder,” in Proc. 15th International Conference on Engineering of Modern Electric Systems (EMES), 2019.  
• E. Nisioti and N. Thomos, “Design of capacity-approaching low-density parity-check codes using recurrent neural networks,” preprint arXiv:2001.01249, 2020.  
• J. Fang, M. Bi, S. Xiao, G. Yang, H. Yang, Z. Chen, Z. Liu, and W. Hu, “Neural network decoder of polar codes with tanh-based modified LLR over FSO turbulence channel,” Opt. Express 28, 2020.  
• S. Meng, X. Q. Jiang, Y. Gao, H. Hai and J. Hou, “Performance evaluation of channel decoder based on recurrent neural network. In Journal of Physics: Conference Series, 2020.  
• T. Wadayama and S. Takabe, “Quantizer optimization based on neural quantizer for sum-product decoder,” in Proc. IEEE Global Communications Conference (GLOBECOM), 2018.  
• T. Raviv, N. Raviv and Y. Be’ery, “Data-Driven ensembles for deep and hard-decision hybrid decoding,” preprint arXiv:2001.06247, 2020.  
• C.-F. Teng and Y.-L. Chen, “Syndrome-enabled unsupervised learning for channel adaptive blind equalizer with joint optimization mechanism. 10.13140/RG.2.2.30154.3168, 2020.  
• J. Wang, J. Li, H. Huang, and H. Wang. “Fine-grained recognition of error correcting codes based on 1-D convolutional neural network.” Digital Signal Processing, 2020.  
• A. Buchberger, C. Häger, H. D. Pfister, L. Schmalen, and A. G. i Amat, “Pruning Neural Belief Propagation Decoders,” preprint arXiv:2001.07464, 2020.  
• L. Li, X. Tang and C. Tellambura, “Deep learning based modified message passing algorithm for sparse code multiple access,” in Proc. Ninth International Workshop on Signal Design and its Applications in Communications (IWSDA), 2019.  
• C. Wen, J. Xiong, L. Gui, Z. Shi and Y. Wang, “A novel decoding scheme for polar code using convolutional neural network,” in Proc. IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2019.  
• J. Gao, K. Niu and C. Dong, “Learning to decode polar codes with one-bit quantizer,” in IEEE Access., 2020.  
• N. Shlezinger, N. Farsad, Y. C. Eldar, and A. J. Goldsmith, “Data-driven factor graphs for deep symbol detection,” preprint arXiv:2002.00758, 2020.  
• N. Raviv, A. Caciularu, T. Raviv, J. Goldberger, and Y. Be’ery, “perm2vec: Graph permutation selection for decoding of error correction codes using self-attention,” preprint arXiv:2002.02315, 2020.  
• W. Song, Y. Fu, Q. Chen, L. Li and C. Zhang, “ANN based adaptive successive cancellation list decoder for polar codes,” in Proc. IEEE 13th International Conference on ASIC (ASICON), 2019.  
• Y. Wei, M.-M. Zhao, M.-J. Zhao, and M. Lei, “ADMM-based decoder for binary linear codes aided by deep learning,” preprint arXiv:2002.07601, 2020.  
• C. T. Leung, R. V. Bhat and M. Motani, “Low-Latency Neural Decoders for Linear and Non-Linear Block Codes,” in Proc. IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, 2019.  
• J. Fang, M. Bi, S. Xiao, G. Yang, H. Yang, Z. Chen, Z. Liu, and W. Hu, “Neural network decoder of polar codes with tanh-based modified LLR over FSO turbulence channel,” Opt. Express 28, 1679-1689, 2020.  
• Z. Zhang, D. Yao, L. Xiong, B. Ai, and S. Guo, “A Convolutional Neural Network Decoder for Convolutional Codes,” in Proc. ChinaCom, 2019.

• V. Garcia Satorras, and M. Welling, “Neural Enhanced Belief Propagation on Factor Graphs,” preprint arXiv:2003.01998, 2020.  
• A. Dhok and S. Bhole, “ATRNN: Using Seq2Seq Approach for Decoding Polar Codes,” in Proc. International Conference on COMmunication Systems & NETworkS (COMSNETS), 2020.  
• D. Le, D. Nguyen, T. Tran and Y. Nakashima, “Run-Length Limited Decoding for Visible Light Communications: A Deep Learning Approach,” in Proc. Asia-Pacific Conference on Communications (APCC), 2019.  
• Y. Ren, Y. Shen, Z. Zhang, X. You and C. Zhang, “Efficient Belief Propagation Polar Decoder With Loop Simplification Based Factor Graphs,” in IEEE Transactions on Vehicular Technology., 2020.  
• Y. Qin and F. Liu, “Convolutional Neural Network-Based Polar Decoding,” World Symposium on Communication Engineering (WSCE), 2019.  
• Shridhar B. Devamane, Rajeshwari L. Itagi, “Recurrent Neural Network Based Turbo Decoding Algorithms for Different Code Rates,” Journal of King Saud University – Computer and Information Sciences, 2020.  
• A. Mandal, A. Chatterjee and A. Thangaraj, “Noisy Deletion, Markov Codes and Deep Decoding,” National Conference on Communications (NCC), 2020.  
• X. Wang, J. Li, H. Chang, and J. He, “Optimization design of polar-LDPC concatenated scheme based on deep learning,” Computers & Electrical Engineering, Volume 84, 2020.  
• N. Katz, “CommUnet: U-net decoder for convolutional codes in communication,” preprint arXiv:2004.10057, 2020.  
• H. Lee, E.Y. Seo, H. Ju, S.-H Kim, “On Training Neural Network Decoders of Rate Compatible Polar Codes via Transfer Learning,” Entropy, 2020.  
• C. Teng, C. D. Wu, A. Kuan-Shiuan Ho and A. A. Wu, “Low-complexity Recurrent Neural Network-based Polar Decoder with Weight Quantization Mechanism,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, United Kingdom, 2019.  
• W. Lyu, Z. Zhang, C. Jiao, K. Qin and H. Zhang, “Performance Evaluation of Channel Decoding with Deep Neural Networks,” IEEE International Conference on Communications (ICC), Kansas City, MO, 2018.  
• W.J. Gross, N. Doan, E. Ngomseu Mambou, and S. Ali Hashemi, “Deep Learning Techniques for Decoding Polar Codes,” in Machine Learning for Future Wireless Communications, 2020.  
• N. Doan, S. A. Hashemi, F. Ercan, T. Tonnellier and W. J. Gross, “Neural Dynamic Successive Cancellation Flip Decoding of Polar Codes,” IEEE International Workshop on Signal Processing Systems (SiPS), Nanjing, China, 2019.  
• A. Ben-Yishai and O. Shayevitz, “Simple Modulo can Significantly Outperform Deep Learning-based Deepcode,” preprint arXiv:2008.01686, 2020.  
• M. Lian, F. Carpi, C. Häger and H. D. Pfister, “Learned Belief-Propagation Decoding with Simple Scaling and SNR Adaptation,” in Proc. IEEE International Symposium on Information Theory (ISIT), Paris, France, 2019.  
• H. Kim, Y. Jiang, S. Kannan, S. Oh, and P. Viswanath, “Deepcode and Modulo-SK are Designed for Different Settings,” preprint arXiv:2008.07997, 2020.  
• A. Abotabl, J. H. Bae and K. Song, “Learning PHY Layer Parameters via SNR-Value Network,” in Proc. International Conference on Computing, Networking and Communications (ICNC), Big Island, HI, USA, 2020.  
• T. Raviv, A. Schwartz, and Y. Be’ery, “Deep Ensemble of Weighted Viterbi Decoders for Tail-Biting Convolutional Codes,” preprint arXiv:2009.02591, 2020.  
• N. Doan, S. A. Hashemi, and W. Gross, “Decoding Polar Codes with Reinforcement Learning,” preprint arXiv:2009.06796, 2020.  
• S. Dehdashtian, M. Hashemi, and S. Salehkaleybar, “Deep-Learning Based Blind Recognition of Channel Code Parameters over Candidate Sets under AWGN and Multi-Path Fading Conditions,” preprint arXiv:2009.07774, 2020.  
• Y. Liao, S. A. Hashemi, J. Cioffi, and A. Goldsmith, “Construction of Polar Codes with Reinforcement Learning,” preprint arXiv:2009.09277, 2020.  
• A. Elkelesh, S. Cammerer and S. ten Brink, “Reducing Polar Decoding Latency by Neural Network-Based On-the-Fly Decoder Selection,” in Proc. IEEE Workshop on Signal Processing Systems (SiPS), 2020.  
• S. Habib, A. Beemer, and J. Kliewer, “Learning to Decode: Reinforcement Learning for Decoding of Sparse Graph-Based Channel Codes,” preprint arXiv:2010.05637, 2020.  
• H. -M. Ou, C. -F. Teng, W. -C. Tsai and A. -Y. A. Wu, “A Neural Network-Aided Viterbi Receiver for Joint Equalization and Decoding,” in Proc. IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP), 2020.  
• A. Buchberger, C. Häger, H. D. Pfister, L. Schmalen, A. G. i Amat, “Learned Decimation for Neural Belief Propagation Decoders,” preprint arXiv:2011.02161, 2020.  
• J. K. S. Kamassury, and D. Silva, “Iterative Error Decimation for Syndrome-Based Neural Network Decoders,” preprint arXiv:2012.00089, 2020.

# 端到端通信的学习 #

• S. Dorner, S. Cammerer, J. Hoydis and S. ten Brink, “Deep learning-based communication over the air,” IEEE Journal Selected Topics in Signal Processing, vol.12, no. 1, pp. 132-143, February 2018.  
• H. Ye, G. Y. Li, B.-H. Juang and K. Sivanesan, “Channel agnostic end-to-end learning based communication systems with conditional GAN,” in Proc. IEEE Global Communication Conference (Globecom), December 2018.  
• F. Ait Aoudia and J. Hoydis, “End-to-end learning of communications systems without a channel model,” in Proc. IEEE Asilomar Conference on Signal, System, Computers, October 2018.  
• B. Karanov, M. Chagnon, F. Thouin, T. A. Eriksson, H. Bulow, D. Lavery, P. Bayvel and L. Schmalen., “End-to-end deep learning of optical fiber communications,” Journal of Lightwave Technology, vol. 36, no. 20, pp. 4843-4855, October 2018.  
• T. J. O’Shea, T. Erpek and T. C. Clancy, “Physical layer deep learning of encodings for the MIMO fading channel,” in Proc. 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton), October 2017.  
• T. J. O’Shea, K. Karra and T. C. Clancy, “Learning to communicate: channel auto-encoders, domain specific regularizers, and attention,”, in Proc. IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), December 2016  
• M. Kim, N.-I. Kim, W. Lee and D.-H. Cho, “Deep learning-aided SCMA,” IEEE Communications Letters, vol. 22, no. 4, pp. 720-723, April 2018.  
• M. Goutay, F. Ait Aoudia and J. Hoydis, “Deep Reinforcement Learning Autoencoder with Noisy Feedback,” in Proc. International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOPT), 2019.  
• F. Ait Aoudia and J. Hoydis, “Model-free training of end-to-end communication systems,” in IEEE Journal on Selected Areas in Communications, 2019.  
• E. Balevi and J. G. Andrews, “Autoencoder-based error correction coding for one-bit quantization,” preprint arXiv:1909.12120, 2019.  
• D. Wu, M. Nekovee and Y. Wang, “Deep learning based autoencoder for interference channel,” preprint arXiv:1902.06841, 2019.  
• J. Kim, B. Lee, H. Lee, Y. Kim and J. Lee, “Deep learning-assisted multi-dimensional modulation and resource mapping for advanced OFDM systems,” in Proc. IEEE Globecom Workshops (GC Wkshps), 2018.  
• L. Hao, D. Wang, Y. Tao, W. Cheng, J. Li and Z. Liu, “The extended SLM combined autoencoder of the PAPR reduction scheme in DCO-OFDM systems,” in Applied Sciences, 2019.  
• R. Fritschek, R. F. Schaefer and G. Wunder, “Deep learning for channel coding via neural mutual information estimation,” preprint arXiv:1903.02865, 2019.  
• H. Ye, L. Liang, G. Y. Li and B.-H. F. Juang, “Deep learning based end-to-end wireless communication systems with conditional GAN as unknown channel,” preprint arXiv:1903.02551, 2019.  
• Y. Wang and T. Koike-Akino, “Learning to modulate for non-coherent MIMO,” preprint arXiv:1903.03711, 2019.  
• T. Matsumine, T. Koike-Akino and Y. Wang, “Deep learning-based constellation optimization for physical network coding in two-way relay networks,” preprint arXiv:1903.03713, 2019.  
• D. B. Kurka and D. Gunduz, “Successive refinement of images with deep joint source-channel coding,” preprint arXiv:1903.06333, 2019.  
• L. Hao, D. Wang, W. Cheng, J. Li and A. Ma, “Performance enhancement of ACO-OFDM-based VLC systems using a hybrid autoencoder scheme,” Optics Communications, Volume 442, 2019.  
• E. Bourtsoulatze, D. B. Kurka and D. Gunduz, “Deep Joint source-channel coding for wireless image transmission,” preprint arXiv:1809.01733, 2018.  
• E. Stauffer, A. Wang and N. Jindal, “Deep learning for the degraded broadcast channel,” preprint arXiv:1903.08577, 2019.  
• H. Wu, Z. Sun and X. Zhou, “Deep learning-based frame and timing synchronization for end-to-End communications,” Journal of Physics: Conference Series, 2019.  
• X. Lin and L. Zhang, “Deep learning based reliable and intelligent chaotic OFDM communications for cognitive radio system,” in Proc. 10th International Conference on Wireless Communications and Signal Processing (WCSP), 2018.  
• B. Zhu, J. Wang, L. He and J. Song, “Joint transceiver optimization for wireless communication PHY using neural network,” in IEEE Journal on Selected Areas in Communications, 2019.  
• A. Zubow, P. Gawłowicz and S. Bayhan, “Deep learning for cross-technology communication design,” preprint arXiv:1904.05401, 2019.  
• A. Al-Baidhani and H. H. Fan, “Learning for detection: A deep learning wireless communication receiver over Rayleigh fading channels,” in Proc. International Conference on Computing, Networking and Communications (ICNC), 2019.  
• V. Raj and S. Kalyani, “Design of communication systems using deep learning: A variational inference perspective,” preprint arXiv:1904.08559, 2019.  
• J. Song, B. Peng, C. Häger, H. Wymeersch and A. Sahai, “Learning physical-layer communication with quantized feedback,” preprint arXiv:1904.09252, 2019.  
• K. Zhang, N. Wu and X. Wang, “Analysis of end-to-end communication system network model,” in Proc. International Conference in Communications, Signal Processing, and Systems, 2019.  
• G. Min, C. Zhang, X. Zhang and W. Tan, “Deep vocoder: Low bit rate compression of speech with deep autoencoder,” preprint arXiv:1905.04709, 2019.  
• M. Kim, W. Lee, J. Yoon and O. Jo, “Toward the realization of encoder and decoder using deep neural networks,” in IEEE Communications Magazine, 2019.  
• Y. M. Saidutta, A. Abdi and F. Fekri, “M to 1 joint source-channel coding of Gaussian sources via dichotomy of the input space based on deep learning,” in Proc. Data Compression Conference (DCC), 2019.  
• S. Mohamed, J. Dong, A. R. Junejo and D. C. Zuo, “Model-based: End-to-end molecular communication system through deep reinforcement learning auto encoder,” in IEEE Access, 2019.  
• J. Schmitz, C. von Lengerke, N. Airee, A. Behboodi and R. Mathar, “A deep learning wireless transceiver with fully learned modulation and synchronization,” preprint arXiv:1905.10468, 2019.  
• P. Miao, B. Zhu, C. Qi, Y. Jin and C. Lin, “A model-driven deep learning method for LED nonlinearity mitigation in OFDM-based optical communications,” in IEEE Access., 2019.  
• C. Lee, J. Lin, P. Chen and Y. Chang, “Deep learning-constructed joint transmission-recognition for internet of things,” in IEEE Access., 2019.  
• J. Lin, S. Feng, Z. Yang, Y. Zhang and Y. Zhang, “A novel deep neural network based approach for sparse code multiple access,” preprint arXiv:1906.03169, 2019.  
• W. Zhang, Y. Wang, C. Shen and N. Liang, “A regression approach to certain information transmission problems,” in IEEE Journal on Selected Areas in Communications., 2019.  
• S. Khobahi, and M. Soltanalian, “Model-aware deep architectures for one-bit compressive variational autoencoding,” preprint arXiv:1911.12410, 2019.  
• E. Balevi and J. G. Andrews, “Deep Learning-Based Encoder for One-Bit Quantization,” IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, 2019, pp. 1-6.  
• E. Balevi, and J. G. Andrews, “High Rate Communication over One-Bit Quantized Channels via Deep Learning and LDPC Codes,” preprint arXiv:2003.00081, 2020.  
• T. Mu, X. Chen, L. Chen, H. Yin and W. Wang, “An end-to-end block autoencoder for physical layer based on neural networks,” preprint arXiv:1906.06563, 2019.  
• M. Arvinte, S. Vishwanath and A. H. Tewfik, “Deep learning-based quantization of L-values for Gray-coded modulation,” preprint arXiv:1906.07849, 2019.  
• M. Stark, F. Ait Aoudia and J. Hoydis, “Joint Learning of Geometric and Probabilistic Constellation Shaping,” in Proc. IEEE Globecom Workshops (GC Wkshps), 2019…  
• Y. Liao, H. Yao, Y. Hua and C. Li, “CSI feedback based on deep learning for massive MIMO systems,” in IEEE Access., 2019.  
• V. Raj and S. Kalyani, “Backpropagating through the air: Deep learning at physical layer without channel models,” in IEEE Communications Letters, 2018.  
• A. Smith and J. Downey, “A communication system density estimating generative adversarial network,” preprint, 2019.  
• N. Wu, X. Wang, B. Lin and K. Zhang, “A CNN-based end-to-end learning framework towards intelligent communication systems,” in IEEE Access., 2019.  
• Y. Zhang, X. Wang, J. Wang, Y. Xue and J. Song, “Deep learning-based space shift keying systems,” in Proc. International Conference on Artificial Intelligence for Communications and Networks (AICON), 2019.  
• R. T. Jones, M. P. Yankov and D. Zibar, “End-to-end learning for GMI optimized geometric constellation shape,” preprint arXiv:1907.08535, 2019.  
• D. J. Ji, J. Park and D. Cho, “ConvAE: A new channel autoencoder based on convolutional layers and residual connections,” in IEEE Communications Letters., 2019.  
• Y. Ko and J. Choi, “Unsupervised machine intelligence for automation of multi-dimensional modulation,” in IEEE Communications Letters., 2019.  
• A. Ali, K. Inoue, A. Shalaby, M. S. Sayed and S. M. Ahmed, “Efficient autoencoder-based human body commutation transceiver for WBAN,” in IEEE Access., 2019.  
• M. Varasteh, J. Hoydis and B. Clerckx, “Learning modulation design for SWIPT with nonlinear energy harvester: Large and small signal power regimes,” preprint arXiv:1908.11726, 2019.  
• M. A. ElMossallamy, Z. Han, M. Pan, R. Jäntti, K. G. Seddik and G. Y. Li, “Noncoherent MIMO codes construction using autoencoders,” to appear in Proc. IEEE Global Communications Conference (GLOBECOM), 2019.  
• Z. Zhu, J. Zhang, R. Chen and H. Yu, “Autoencoder-based transceiver design for OWC systems in log-normal fading channel,” in IEEE Photonics Journal., 2019.  
• P. G. Pachpande, M. H. Khadr, H. Hussien, H. Elgala, and D. Saha, “Autoencoder model for OFDM-based optical wireless communication,” in OSA Advanced Photonics Congress (AP), 2019.  
• M. Varasteh, J. Hoydis and B. Clerckx, “Learning to communicate and energize: Modulation, coding and multiple access designs for wireless information-power transmission,” preprint arXiv:1909.06492, 2019.  
• T. Cousik, R. Shafin, Z. Zhou, K. Kleine, J. Reed and L. Liu, “CogRF: A new frontier for machine learning and artificial intelligence for 6G RF systems,” preprint arXiv:1909.06862, 2019.  
• B. Karanov, G. Liga, V. Aref, D. Lavery, P. Bayvel, and L. Schmalen, “Deep learning for communication over dispersive nonlinear channels: Performance and comparison with classical digital signal processing,” preprint arXiv:1910.01028, 2019.  
• S. Park, O. Simeone, and J. Kang, “Meta-learning to communicate: Fast end-to-end training for fading channels,” preprint arXiv:1910.09945, 2019.  
• A. Sahai, J. Sanz, V. Subramanian, C. Tran, and K. Vodrahalli, “Learning to communicate in a noisy environment,” preprint arXiv:1910.09630, 2019.  
• M. Jankowski, D .Gunduz, and K. Mikolajczyk, “Deep joint source-channel coding for wireless image retrieval,” preprint arXiv:1910.12703, 2019.  
• H. Lee, T. Q. S. Quek, and S. H. Lee, “A deep learning approach to universal binary visible light communication transceiver,” preprint arXiv:1910.12048, 2019.  
• L. Shi, X. Zhang, W. Wang, Y. Zhang, Z. Wang, A. Vladimirescu, Y. Zhang, and J. Wang, “PAPR reduction based on deep autoencoder for VLC DCO-OFDM system,” in Proc. IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, 2019.  
• A. Sahai, J. Sanz, V. Subramanian, C. Tran and K. Vodrahall, “Learning to communicate with limited co-design,” in Proc. 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2019.  
• Nuwanthika Rajapaksha, N. Rajatheva and M. Latva-aho, “Low complexity autoencoder based end-to-end learning of coded communications systems,” preprint arXiv:1911.08009, 2019.  
• D. B. Kurka, and D. Gündüz, “DeepJSCC-f: Deep joint-source channel coding of images with feedback,” preprint arXiv:1911.11174, 2019.  
• S. Cammerer, F. Ait Aoudia, S. Dörner, M. Stark, J. Hoydis and S. T. Brink, “Trainable Communication Systems: Concepts and Prototype,” in IEEE Transactions on Communications, 2020.  
• A. Tato and C. Mosquera, “Spatial modulation for beyond 5G communications: Capacity calculation and link adaptation,” Proceedings, 2019.  
• R. Daniels and R. W. Heath, Jr., “An online learning framework for link adaptation in wireless networks,” in Proc. Information Theory and Applications Workshop, February 2009.  
• M. P. Mota, D. C. Araujo, F. H. C. Neto, A. L. F. de Almeida, and F. R. P. Cavalcanti, “Adaptive modulation and coding based on reinforcement learning for 5G networks,” preprint arXiv:1912.04030, 2019.  
• H. Zhang, L. Zhang and Y. Jiang, “Overfitting and underfitting analysis for deep learning based end-to-end communication systems,” in Proc. 11th International Conference on Wireless Communications and Signal Processing (WCSP), 2019.  
• L. Li, C. Tellambura and X. Tang, “Improved tone reservation method based on deep learning for PAPR reduction in OFDM system,” in Proc. 11th International Conference on Wireless Communications and Signal Processing (WCSP), 2019.  
• M. Zhang, M. Liu and Z. Zhong, “Neural network assisted active constellation extension for PAPR reduction of OFDM system,” in Proc. 11th International Conference on Wireless Communications and Signal Processing (WCSP), 2019.  
• Y. Song, M. Xu, L. Yu, H. Zhou, S. Shao, and Y. Yu, “Infomax neural joint source-channel coding via adversarial bit flip,” in Proc. 34th AAAI Conference on Artificial Intelligence (AAAI), 2019.  
• J. Xu, W. Chen, B. Ai, R. He, Y. Li, J. Wang, T. Juhana, and A. Kurniawan, “Performance evaluation of autoencoder for coding and modulation in wireless communications,” in Proc. 11th International Conference on Wireless Communications and Signal Processing (WCSP), 2019.  
• K. Gümüs, A. Alvarado, B. Chen, C. Häger, and E. Agrell, “End-to-end learning of geometrical shaping maximizing generalized mutual information,” preprint arXiv:1912.05638, 2019.  
• B. Karanov, M. Chagnon, V. Aref, D. Lavery, P. Bayvel, and L. Schmalen, “Concept and experimental demonstration of optical IM/DD end-to-end system optimization using a generative model,” preprint arXiv:1912.05146, 2019.  
• E. Sillekens, W. Yi, D. Semrau, A. Ottino, B. Karanov, S. Zhou, K. Law, J. Chen, D. Lavery, L. Galdino, P. Bayvel, and R. I. Killey, “Experimental demonstration of learned time-domain digital back-propagation,” preprint arXiv:1912.12197, 2019.  
• S. Park, O. Simeone, and J. Kang, “End-to-End Fast Training of Communication Links Without a Channel Model via Online Meta-Learning,” preprint arXiv:2003.01479, 2020.  
• S. Dörner, M. Henninger, S. Cammerer, and S. ten Brink, “WGAN-based Autoencoder Training Over-the-air,” preprint arXiv:2003.02744, 2020.  
• J. Guo, X. Yang, C.-K. Wen, S. Jin, and G. Ye Li, “DL-based CSI feedback and cooperative recovery in massive MIMO,” preprint arXiv:2003.03303, 2020.  
• K. Ullrich, F. Viola, and D. J. Rezende, “Neural Communication Systems with Bandwidth-limited Channel,” preprint arXiv:2003.13367, 2020.  
• F. Ait Aoudia and J. Hoydis, “Joint Learning of Probabilistic and Geometric Shaping for Coded Modulation Systems,” preprint arXiv:2004.05062, 2020.  
• S. Xue, Y. Ma, N. Yi, and R. Tafazolli, “On Deep Learning Solutions for Joint Transmitter and Noncoherent Receiver Design in MU-MIMO Systems,” preprint arXiv:2004.06599, 2020.  
• M. B. Mashhadi, and D. Gunduz, “Pruning the Pilots: Deep Learning-Based Pilot Design and Channel Estimation for MIMO-OFDM Systems,” preprint arXiv:2006.11796, 2020.  
• T. Van Luong, Y. Ko, N. A. Vien, M. Matthaiou and H. Q. Ngo, “Deep Energy Autoencoder for Noncoherent Multicarrier MU-SIMO Systems,” in IEEE Transactions on Wireless Communications, vol. 19, no. 6, pp. 3952-3962, June 2020.  
• A. Sahin, D. W. Matolak, “Golay Layer: Limiting Peak-to-Average Power Ratio for OFDM-based Autoencoders,” preprint arXiv:2002.07701, 2020.  
• K. Vedula, R. Paffenroth and D. R. Brown, “Joint Coding and Modulation in the Ultra-Short Blocklength Regime for Bernoulli-Gaussian Impulsive Noise Channels Using Autoencoders,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020.  
• T. Fujihashi, T. Koike-Akino, S. Chen, and T. Watanabe, “Wireless 3D Point Cloud Delivery Using Deep Graph Neural Networks,” preprint arXiv:2006.09835, 2020.  
• Y. Jiang, H. Kim, H. Asnani, S. Kannan, S. Oh and P. Viswanath, “Joint Channel Coding and Modulation via Deep Learning,” IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Atlanta, GA, USA, 2020.  
• N. Skatchkovsky, H. Jang, and O. Simeone, “End-to-End Learning of Neuromorphic Wireless Systems for Low-Power Edge Artificial Intelligence,” preprint arXiv:2009.01527, 2020.  
• F. Ait Aoudia and J. Hoydis, “End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication,” preprint arXiv:2009.05261, 2020.  
• N. A. Letizia, and A. M. Tonello, “Capacity-Approaching Autoencoders for Communications,” preprint arXiv:2009.05273, 2020.  
• D. Burth K., and D. Gündüz, “Bandwidth-Agile Image Transmission with Deep Joint Source-Channel Coding,” preprint arXiv:2009.12480, 2020.  
• J. Xu, B. Ai, W. Chen, A. Yang, and P. Sun, “Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules,” preprint arXiv:2012.00533, 2020.

# 定位、传感和本地化 #

• X. Wang, L. Gao, S. Mao and S. Pandey, “CSI-based fingerprinting for indoor localization: a deep learning approach,” IEEE Transactions on Vehicular Technology, vol. 66, no. 1, pp. 763-776, January 2017.  
• C. Studer, S. Medjkouh, E. Gönültas, T. Goldstein and O. Tirkkonen, “Channel charting: locating users within the radio environment using channel state information,” IEEE Access, vol. 6. pp. 47682-47698, August 2018.  
• J. Vieira, E. Leitinger, M. Sarajlic, X. Li, and F. Tufvesson, “Deep convolutional neural networks for massive MIMO fingerprint-based positioning,” in Proc. IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), October 2017.  
• K. Davaslioglu and Y. E. Sagduyu, “Generative adversarial learning for spectrum sensing,” 2018 IEEE International Conference on Communications (ICC), May 2018.  
• M. Sadegh Safari and V. Pourahmadi, “Deep UL2DL: Channel knowledge transfer from uplink to downlink,” preprint arXiv:1812.07518, 2018.  
• M. Arnold, S. Dörner, S. Cammerer, S. Yan, J. Hoydis and S. ten Brink, “Enabling FDD massive MIMO through deep learning-based channel prediction,” preprint arXiv:1901.03664, 2019.  
• M. Mehrabi, M. Mohammadkarimi, M. Ardakani and Y. Jing, “Decision directed channel estimation based on deep neural network k-step predictor for MIMO communications in 5G,” preprint arXiv:1901.03435, 2019.  
• K. Bregar and M. Mohorčič, “Improving indoor localization using convolutional neural networks on computationally restricted devices,” in IEEE Access, vol. 6, pp. 17429-17441, 2018.  
• C. Huang, G. C. Alexandropoulos, A. Zappone, C. Yuen and M. Debbah, “Deep learning for UL/DL channel calibration in generic massive MIMO systems,” in Proc. IEEE International Conference on Communications (ICC), May 2019.  
• M. Soltani, V. Pourahmadi, A. Mirzaei and H. Sheikhzadeh, “Deep learning-based channel estimation,” preprint arXiv:1810.05893, 2018. \[Simulation code\]  
• M. Arnold, J. Hoydis and S. ten Brink, “Novel massive MIMO channel sounding data applied to deep learning-based indoor positioning,” preprint arXiv:1810.04126, 2018.  
• A. Y. Abyaneh, A. H. G. Foumani and V. Pourahmadi, “Deep neural networks meet CSI-based authentication,” preprint arXiv:1812.04715, 2018.  
• P. Yazdanian and V. Pourahmadi, “DeepPos: Deep supervised autoencoder network for CSI based indoor localization,” preprint arXiv:1811.12182, 2018.  
• A. Decurninge, L. G. Ordóñez, P. Ferrand, H. Gaoning, L. Bojie, Z. Wei and M. Guillaud, “CSI-based outdoor localization for massive MIMO: experiments with a learning approach,” in Proc. 15th International Symposium on Wireless Communication Systems (ISWCS), August 2018.  
• S.-J. Liu, R. Y. Chang and F.-T.Chien, “Analysis and visualization of deep neural networks in device-free Wi-Fi indoor localization,” preprint arXiv:1904.10154, 2018.  
• J. Chan, A. Wang, A. Krishnamurthy and S. Gollakota, “DeepSense: Enabling carrier sense in low-power wide area networks using deep learning,” preprint arXiv:1904.10607, 2019.  
• J. Xie, C. Liu, Y. Liang and J. Fang, “Activity pattern aware spectrum sensing: A CNN-based deep learning approach,” in IEEE Communications Letters, 2019.  
• S. Abeywickrama, L. Jayasinghe, H. Fu, S. Nissanka, and C. Yuen, “RF-based Direction Finding of UAVs Using DNN,” in Proc. IEEE International Conference on Communication Systems (ICCS), 2018. \[Simulation code\]  
• Y. Xu, P. Cheng, Z. Chen, Y. Li and B. Vucetic, “Mobile collaborative spectrum sensing for heterogeneous networks: A bayesian machine learning approach,” in IEEE Transactions on Signal Processing, 2018.  
• S. Chaudhari and D. Cabric, “Unsupervised frequency clustering algorithm for null space estimation in wideband spectrum sharing networks,” IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2017.  
• Siddhartha, Y. H. Lee, D. J.M. Moss, J. Faraone, P. Blackmore, D. Salmond, D. Boland and P. H.W. Leong, “Long short-term memory for radio frequency spectral prediction and its real-time FPGA implementation,” in Proc. IEEE Military Communications Conference (MILCOM), 2018.  
• Z. Ye, A. Gilman, Q. Peng, K. Levick, P. Cosman and L. Milstein, “Comparison of neural network architectures for spectrum sensing,” preprint arXiv:1907.07321, 2019.  
• Z. Ye, Q. Peng, K. Levick, H. Rong, A. Gilman, P. Cosman and L. Milstein, “A neural network detector for spectrum sensing under uncertainties,” preprint arXiv:1907.07326, 2019.  
• N. Nayak, V. Raj and S. Kalyani, “Leveraging online learning for CSS in frugal IoT network,” preprint arXiv:1907.07201, 2019.  
• J. Choi, Y.-S. Choi and S. Talwar, “Unsupervised learning technique to obtain the coordinates of Wi-Fi access points,” preprint arXiv:1907.09514, 2019.  
• Y. Xu, P. Cheng, Z. Chen, Y. Hu, Y. Li and B. Vucetic, “Mobile bayesian spectrum learning for heterogeneous networks,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.  
• H. Sallouha, A. Chiumento, S. Rajendran and S. Pollin, “Localization in ultra narrow band IoT networks: Design guidelines and trade-offs,” preprint arXiv:1907.11205, 2019.  
• Q. Peng, A. Gilman, N. Vasconcelos, P. C. Cosman and L. B. Milstein, “Robust deep sensing through transfer learning in cognitive radio,” preprint arXiv:1908.00658, 2019.  
• J. Wang, Y. Ding, S. Bian, Y. Peng, M. Liu and G. Gui, “UL-CSI data driven deep learning for predicting DL-CSI in cellular FDD systems,” in IEEE Access, 2019.  
• P. Huang, O. Castañeda, E. Gönültaş, S. Medjkouh, O. Tirkkonen, T. Goldstein and C. Studer, “Improving channel charting with representation-constrained autoencoders,” preprint arXiv:1908.02878, 2019.  
• C. Liu, J. Wang, X. Liua and Y. Liang, “Deep CM-CNN for spectrum sensing in cognitive radio,” in IEEE Journal on Selected Areas in Communications., 2019.  
• J. L. C. V, Z. Zhao, T. Braun and Z. Li, “A particle filter-based reinforcement learning approach for reliable wireless indoor positioning,” in IEEE Journal on Selected Areas in Communications., 2019.  
• Y. Yang, F. Gao, G. Y. Li and M. Jian, “Deep learning based downlink channel prediction for FDD massive MIMO system,” preprint arXiv:1908.03360, 2019.  
• T. Zhang, S. Liu, W. Xiang; L. Xu, K. Qin and X. Yan, “A real-time channel prediction model based on neural networks for dedicated short-range communications,” Sensors, 2019.  
• T. F. Sanam and H. Godrich, “A multi-view discriminant learning approach for indoor localization using bimodal features of CSI,” preprint arXiv:1908.07370, 2019.  
• Y. Zhu, X. Dong and T. Lu, “An adaptive and parameter-free recurrent neural structure for wireless channel prediction,” in IEEE Transactions on Communications., 2019.  
• J. Gao, X. Yi, C. Zhong, X. Chen and Z. Zhang, “Deep learning for spectrum sensing,” preprint arXiv:1909.02730, 2019.  
• E. Lei, O. Castañeda, O. Tirkkonen, T. Goldstein and C. Studer, “Siamese neural networks for wireless positioning and channel charting,” preprint arXiv:1909.13355, 2019.  
• Z. Gao, Y. Gao, S. Wang, D. Li, Y. Xu, and H. Jiang, “CRISLoc: Reconstructable CSI fingerprintingfor indoor smartphone localization,” preprint arXiv:1910.06895, 2019.  
• M. Najla, Z. Becvar, P. Mach and D. Gesbert, “Predicting device-to-device channels from cellular channel measurements: A learning approach,” preprint arXiv:1911.07191, 2019.  
• N. Turan and W. Utschick, “Learning the MMSE channel predictor,” preprint arXiv:1911.07256, 2019.  
• M. M. Butt, A. Rao, and D. Yoon, “RF fingerprinting and deep learning assisted UE positioning in 5G,” preprint arXiv:2001.00977, 2020.  
• P. Ferrand, A. Decurninge, and M. Guillaud, “DNN-based Localization from Channel Estimates: Feature Design and Experimental Results,” preprint arXiv:2004.00363, 2020.  
• S. Fan, Y. Wu, C. Han and X. Wang, “Structured Bidirectional LSTM Deep Learning Method For 3D Terahertz Indoor Localization,” in Proc. IEEE Conference on Computer Communications (INFOCOM), 2020.  
• T. Gale, T. Šolc, R. Moşoi, M. Mohorčič and C. Fortuna, “Automatic Detection of Wireless Transmissions,” in IEEE Access, vol. 8, pp. 24370-24384, 2020.  
• T. Koike-Akino, P. Wang, M. Pajovic, H. Sun and P. V. Orlik, “Fingerprinting-Based Indoor Localization With Commercial MMWave WiFi: A Deep Learning Approach,” in IEEE Access, vol. 8, 2020.  
• K. M. Attiah, F. Sohrabi, and W. Yu, “Deep Learning Approach to Channel Sensing and Hybrid Precoding for TDD Massive MIMO Systems,” preprint arXiv:2011.10709, 2020.  
• L. Antsfeld, B. Chidlovskii, and E. Sansano-Sansano, “Deep Smartphone Sensors-WiFi Fusion for Indoor Positioning and Tracking,” preprint arXiv:2011.10799, 2020.

# 安全性和鲁棒性 #

• M. Sadeghi and E. G. Larsson , “Adversarial attacks on deep-learning based radio signal classification,” IEEE Wireless Communications Letters, vol. 8, no. 1, pp. 213–216, Feb. 2019. \[Simulation code\]  
• Y. Shi, Y. E. Sagduyu, T. Erpek, K. Davaslioglu, Z. Lu and J. H. Li, “Adversarial deep learning for cognitive radio security: Jamming attack and defense strategies,” in Proc. IEEE International Conference on Communications Workshops (ICC Workshops), 2018.  
• T. Erpek, Y. E. Sagduyu and Y. Shi, “Deep learning for launching and mitigating wireless jamming attacks,” in IEEE Transactions on Cognitive Communications and Networking., 2018.  
• K. K. Nguyen, D. T. Hoang, D. Niyato, P. Wang, D. Nguyen and E. Dutkiewicz, “Cyberattack detection in mobile cloud computing: A deep learning approach,” in Proc. IEEE Wireless Communications and Networking Conference (WCNC), 2018.  
• A. Diro and N. Chilamkurti, “Leveraging LSTM networks for attack detection in fog-to-things communications,” in IEEE Communications Magazine, vol. 56, no. 9, pp. 124-130, Sept. 2018.  
• I. Shakeel, “Machine learning based featureless signalling,” in Proc. IEEE Military Communications Conference (MILCOM), October 2018.  
• F. B. Mismar and B. L. Evans, “Deep Q-Learning for self-organizing networks fault management and radio performance improvement,” in Proc. Asilomar Conference on Signals, Systems, and Computers, October 2018. \[Simulation code\]  
• Y. Shi, T. Erpek, Y. E. Sagduyu and J. H. Li, “Spectrum data poisoning with adversarial deep learning,” in Proc. IEEE Military Communications Conference (MILCOM), 2018.  
• M. Bensalem, S. Kumar Singh and A. Jukan, “Machine learning techniques to detecting and preventing jamming attacks in optical networks,” preprint arXiv:1902.07537, 2019.  
• M. Sadeghi and E. G. Larsson, “Physical adversarial attacks against end-to-end autoencoder communication systems,” IEEE Communications Letters, 2019. \[Simulation code\]  
• R. Fritschek, R. F. Schaefer and G. Wunder, “Deep learning for the Gaussian wiretap channel,” preprint arXiv:1810.12655, 2018.  
• M. Pajovic, T. Koike-Akino and P. V. Orlik, “Model-driven deep learning method for jammer suppression in massive connectivity systems,” preprint arXiv:1903.06266, 2019.  
• N. V. Huynh, D. N. Nguyen, D. T. Hoang and E. Dutkiewicz, “Jam me if you can”: Defeating jammer with deep dueling neural network architecture and ambient backscattering augmented communications,” in IEEE Journal on Selected Areas in Communications., 2019.  
• K. Besser, C. R. Janda, P. Lin and E. A. Jorswieck, “Flexible design of finite blocklength wiretap codes by autoencoders,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019.  
• Z. Luo, S. Zhao, Z. Lu, J. Xu and Y. E. Sagduyu, “When attackers meet AI: Learning-empowered attacks in cooperative spectrum sensing,” preprint arXiv:1905.01430, 2019.  
• Y. Shi, K. Davaslioglu and Y. E. Sagduyu”Generative adversarial network for wireless signal spoofing“, preprint arXiv:1905.01008, 2019.  
• S. Rajendran, W. Meert, V. Lenders and S. Pollin, “SAIFE: Unsupervised wireless spectrum anomaly detection with interpretable features,” in Proc. IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), 2018.  
• S. Rajendran, V. Lenders, W. Meert and S. Pollin, “Crowdsourced wireless spectrum anomaly detection,” preprint arXiv:1903.05408, 2019.  
• S. Rajendran, W. Meert, V. Lenders and S. Pollin, “Unsupervised wireless spectrum anomaly detection with interpretable features,” in IEEE Transactions on Cognitive Communications and Networking., 2019.  
• D. Roy, T. Mukherjee, M. Chatterjee and E. Pasiliao, “Detection of rogue RF transmitters using generative adversarial nets,” in proc. IEEE WCNC, 2019.  
• Y. E. Sagduyu, Y. Shi and T. Erpek, “IoT network security from the perspective of adversarial deep learning,” preprint arXiv:1906.00076, 2019.  
• M. Bensalem, S. K. Singh and A. Jukan, “On detecting and preventing jamming attacks with machine learning in optical networks,” preprint arXiv:1902.07537, 2019.  
• D. J. M. Moss, D. Boland, P. Pourbeik and P. H. W. Leong, “Real-time FPGA-based anomaly detection for radio frequency signals,” IEEE International Symposium on Circuits and Systems (ISCAS), 2018.  
• F. Shu, L. Liu, Y. Zhang, G. Xia, X. Liu, J. Li, S. Jin and J. Wang, “A deep-learning-based joint inference for secure spatial modulation receiver,” preprint arXiv:1907.02215, 2019.  
• F. Jameel, W. U. Khan, Z. Chang, T. Ristaniemi and J. Liu, “Secrecy analysis and learning-based optimization of cooperative NOMA SWIPT systems,” preprint arXiv:1907.05753, 2019.  
• J. Yu, A. Hu, F. Zhou, Y. Xing, Y. Yu, G. Li and L. Peng, “Radio frequency fingerprint identification based on denoising autoencoders,” preprint arXiv:1907.08809, 2019.  
• B. Liu, Z. Wei, J. Yuan and M. Pajovic, “Deep learning assisted user identification in massive machine-type communications,” preprint arXiv:1907.09735, 2019.  
• M. Usama, J. Qadir and A. Al-Fuqaha, “Black-box adversarial ML attack on modulation classification,” preprint arXiv:1908.00635, 2019.  
• A. Anderson, S. R. Young, F. K. Reed and J. M. Vann, “Deep modulation (Deepmod): A self-taught PHY layer for resilient digital communications,” preprint arXiv:1908.11218, 2019.  
• R. Yao, Y. Zhang, S. Wang, N. Qi, N. I. Miridakis and T. A. Tsiftsis, “Deep neural network assisted approach for antenna selection in untrusted relay networks,” in IEEE Wireless Communications Letters., 2019.  
• U. Masood, A. Asghar, A. Imran and A. N. Mian, “Deep learning based detection of sleeping cells in next generation cellular networks,” in Proc. IEEE Global Communications Conference (GLOBECOM), 2018.  
• X. Zhang and M. Vaezi, “Deep learning based precoding for the MIMO Gaussian wiretap channel,” preprint arXiv:1909.07963, 2019.  
• M. Usama, M. Asim, J. Qadir, A. Al-Fuqaha and M. Ali Imran, “Adversarial machine learning attack on modulation classification,” preprint arXiv:1909.12167, 2019.  
• K. Davaslioglu and Y. E. Sagduyu, “Trojan attacks on wireless signal classification with adversarial machine learning,” preprint arXiv:1910.10766, 2019.  
• Y. E. Sagduyu, Y. Shi, and T. Erpek, “Adversarial deep learning for over-the-air spectrum poisoning attacks,” preprint arXiv:1911.00500, 2019.  
• D. T. Hoang, D. N. Nguyen, M. A. Alsheikh, S. Gong, E. Dutkiewicz, D. Niyato, and Z. Han, “Borrowing arrows with thatched boats”: The art of defeating reactive jammers in IoT networks,” preprint arXiv:1912.11170, 2019.  
• L. Senigagliesi, M. Baldi and E. Gambi, “Performance of statistical and machine learning techniques for physical layer authentication,” preprint arXiv:2001.06238 2020.  
• B. Kim, Y. E. Sagduyu, K. Davaslioglu, T. Erpek, and S. Ulukus, “Over-the-air adversarial attacks on deep learning based modulation classifier over wireless channels,” preprint arXiv:2002.02400, 2020.  
• Q. Liu, J. Guo, C.-K. Wen, and S. Jin, “Adversarial attack on DL-based massive MIMO CSI feedback,” preprint arXiv:2002.09896, 2020.  
• Y. Arjoune, F. Salahdine, M. S. Islam, E. Ghribi, and N. Kaabouch, “A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication,” preprint arXiv:2003.07308 2020.  
• N. Abuzainab et al., “QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning,” in Proc. IEEE Military Communications Conference (MILCOM), Norfolk, VA, USA, 2019.  
• M. Z. Hameed, A. Gyorgy, and D. Gunduz, “The Best Defense Is a Good Offense: Adversarial Attacks to Avoid Modulation Detection,” preprint arXiv:1902.10674, 2019.  
• Z. Utkovski, P. Agostini, M. Frey, I. Bjelakovic and S. Stanczak, “Learning Radio Maps for Physical-Layer Security in the Radio Access,” IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Cannes, France, 2019.  
• B. Kim, Y. E. Sagduyu, T. Erpek, K. Davaslioglu, and S. Ulukus, “Adversarial Attacks with Multiple Antennas Against Deep Learning-Based Modulation Classifiers,” preprint arXiv:2007.16204, 2020.  
• J. Stankowicz, J. Robinson, J. M. Carmack and S. Kuzdeba, “Complex Neural Networks for Radio Frequency Fingerprinting,” IEEE Western New York Image and Signal Processing Workshop (WNYISPW), 2019.  
• Q. Zhu and L. Sun, “Big Data Driven Anomaly Detection for Cellular Networks,” in IEEE Access, vol. 8, pp. 31398-31408, 2020.  
• M. Liu, and R. Wang, “Deep Reinforcement Learning Based Dynamic Power and Beamforming Design for Time-Varying Wireless Downlink Interference Channel,” preprint arXiv:2011.03750, 2020.  
• L. Senigagliesi, M. Baldi, and E. Gambi, “Comparison of Statistical and Machine Learning Techniques for Physical Layer Authentication,” preprint arXiv:2001.06238, 2020.  
• R. Kolcun, D. A. Popescu, V. Safronov, P. Yadav, A. M. Mandalari, Y. Xie, R. Mortier., and H. Haddadi, “The Case for Retraining of ML Models for IoT Device Identification at the Edge,” preprint arXiv:2011.08605, 2020.  
• G. Cerar, H. Yetgin, B. Bertalanič, and C. Fortuna, “Learning to Detect Anomalous Wireless Links in IoT Networks,” preprint arXiv:2008.05232, 2020.

# 毫米波通信 #

• X. Li, A. Alkhateeb and C. Tepedelenlioğlu, “Generative adversarial estimation of channel covariance in vehicular millimeter wave systems,” in Proc.Asilomar Conference on Signals, Systems, and Computers, 2018.  
• A. Alkhateeb and I.Beltagy, “Machine learning for reliable mmWave systems: Blockage prediction and proactive handoff,” in Proc. IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2018.  
• A. Alkhateeb, S. Alex, P. Varkey, Y. Li, Q. Qu and D. Tujkovic, “Deep learning coordinated beamforming for highly-mobile millimeter wave systems,” in IEEE Access, vol. 6, pp. 37328-37348, 2018. \[Simulation code\]  
• F. B. Mismar and B. L. Evans, “Partially blind handovers for mmWave new radio aided by sub-6 GHz LTE signaling,” in Proc. IEEE International Conference on Communications Workshops (ICC Workshops), May 2018.  
• H. He, C.-K. Wen, S. Jin and G. Y. Li, “Deep learning-based channel estimation for beamspace mmWave massive MIMO systems,” IEEE Wireless Communications Letters, vol. 7, no. 5, pp. 852-855, October 2018. \[Simulation code\]  
• Y. Wang, M. Narasimha and R. W. Heath, Jr., “mmWave beam prediction with situational awareness: a machine learning approach,” in Proc. IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), June 2018.  
• V. Va, J. Choi, T. Shimizu, G. Bansal and R. W. Heath, “Inverse multipath fingerprinting for millimeter wave V2I beam alignment,” IEEE Transactions on Vehicular Technology, vol. 67, no. 5, pp. 4042-4058, May 2018.  
• C. Antón-Haro and X. Mestre, “Learning and data-driven beam selection for mmWave communications: An angle of arrival-based approach,” in IEEE Access, vol. 7, pp. 20404-20415, 2019.  
• J. Yang, K. Chen, X. Ge, Y. Li and L. Tian, “Neural networks in hybrid precoding for millimeter wave massive MIMO systems,” preprint arXiv:1903.08849, 2019.  
• T. Lin and Y. Zhu, “Beamforming design for large-scale antenna arrays using deep learning,” preprint arXiv:1904.03657, 2019. \[Simulation code\]  
• Y. Koda, K. Nakashima, K. Yamamoto, T. Nishio and M. Morikura, “End-to-end learning of proactive handover policy for camera-assisted mmWave networks using deep reinforcement learning,” preprint arXiv:1904.04585, 2019.  
• P. Dong, H. Zhang, G. Y. Li, N. NaderiAlizadeh and I. S. Gaspar, “Deep CNN based channel estimation for mmWave massive MIMO systems,” preprint arXiv:1904.06761, 2019.  
• S.-E. Chiu, N. Ronquillo and T. Javidi, “Active learning and CSI acquisition for mmWave initial alignment,” in IEEE Journal on Selected Areas in Communications., 2019.  
• Y. Shabara, E. Ekici and C. E. Koksal, “Source coding based mmWave channel estimation with deep learning based decoding,” preprint arXiv:1905.00124, 2019.  
• A. M. Elbir and K. V. Mishra, “Joint antenna selection and hybrid beamformer design using unquantized and quantized deep learning networks“, preprint arXiv:1905.03107, 2019.  
• R. Li, C. Zhang, P. Patras, P. Cao, and J. S. Thompson, “DELMU: A Deep Learning Approach to Maximising the Utility of Virtualised Millimetre-Wave Backhauls,” preprint arXiv:1810.00356, 2018. \[Simulation code\]  
• A. Klautau, P. Batista, N. Gonzalez-Prelcic, Y. Wang, and R. W. Heath Jr., “MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning,” in Information Theory and Applications Workshop, 2018. \[Simulation code\]  
• X. Li and A. Alkhateeb, “Deep learning for direct hybrid precoding in millimeter wave massive MIMO systems,” preprint arXiv:1905.13212, 2019.  
• A. M. Elbir, “CNN-based precoder and combiner design in mmWave MIMO systems,” in IEEE Communications Letters, 2019.  
• S. Amuru, “Beam learning – Using machine learning for finding beam directions,” preprint arXiv:1906.04368, 2019.  
• H. Huang, Y. Song, J. Yang, G. Gui and F. Adachi, “Deep-learning-based millimeter-wave massive MIMO for hybrid precoding,” in IEEE Transactions on Vehicular Technology, 2019.  
• Y.-G. Lim, Y. J. Cho, M. Sim, Y. Kim, C.-B. Chae, R. A. Valenzuela, “Map-based millimeter-wave channel models: An overview, hybrid modeling, data, and learning,” preprint arXiv:1711.09052, 2019.  
• J. Tao, Q. Wang, S. Luo and J. Chen, “Constrained deep neural network based hybrid beamforming for millimeter wave massive MIMO systems,” in Proc. IEEE International Conference on Communications (ICC), China, 2019.  
• Y. Liao, N. Farsad, N. Shlezinger, Y. C. Eldar and A. J. Goldsmith, “Deep neural network symbol detection for millimeter wave communications,” preprint arXiv:1907.11294, 2019.  
• Q. Wang and K. Feng, “PrecoderNet: Hybrid beamforming for millimeter wave systems using deep reinforcement learning,” preprint arXiv:1907.13266, 2019.  
• L. Yan, H. Ding, L. Zhang, J. Liu, X. Fang, Y. Fang, M. Xiao, and X. Huang, “Machine learning based handovers for sub-6 GHz and mmWave integrated vehicular networks,” in IEEE Transactions on Wireless Communications., 2019.  
• T. Nishio, H. Okamoto, K. Nakashima, Y. Koda, K. Yamamoto, M. Morikura, Y. Asai and R. Miyatake, “Proactive received power prediction using machine learning and depth images for mmWave networks,” in IEEE Journal on Selected Areas in Communications., 2019.  
• Q. Zhang, W. Saad and M. Bennis, “Reflections in the sky: Millimeter wave communication with UAV-carried intelligent reflectors,” preprint arXiv:1908.03271, 2019.  
• J. Yang, S. Jin, C.-K. Wen, J. Guo and M. Matthaiou, “3-D positioning and environment mapping for mmWave communication systems,” preprint arXiv:1908.04142, 2019.  
• D. Kim and N. Lee, “Machine mearning based detections for mmWave two-hop MIMO systems using one-bit transceivers,” in Proc. IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2019.  
• H. Xie, J. Rodríguez-Fernández and N. González-Prelcic, “Dictionary learning for channel estimation in hybrid frequency-selective mmWave MIMO systems,” preprint arXiv:1909.09181, 2019.  
• M. Alrabeiah and A. Alkhateeb, “Deep learning for mmWave beam and blockage prediction using sub-6GHz channels,” preprint arXiv:1910.02900, 2019. \[Simulation code\]  
• J. Chen, W. Feng, J. Xing, P. Yang, G. E. Sobelman, D. Lin, and S. Li, “Hybrid beamforming/combining for millimeter wave MIMO: A machine learning approach,” preprint arXiv:1910.06585, 2019.  
• X. Wei, C. Hu, and L. Dai, “Knowledge-aided deep learning for beamspace channel estimation in millimeter-wave massive MIMO systems,” preprint arXiv:1910.12455, 2019.  
• H.-L. Chiang, K.-C. Chen, W. Rave, M. K. Marandi, and G. Fettweis, “Machine-learning beam tracking and weight optimization for mmWave multi-UAV links,” preprint arXiv:1910.13538, 2019.  
• A. M. Elbir and A. Papazafeiropoulos, “Hybrid precoding for multi-user millimeter wave massive MIMO systems: A deep learning approach,” preprint arXiv:1911.04239, 2019.  
• M. Alrabeiah, A. Hredzak, and A. Alkhateeb, “Millimeter wave base stations with cameras: Vision aided beam and blockage prediction,” preprint arXiv:1911.06255, 2019.  
• A. M. Elbir and K. V. Mishra, “Deep learning strategies for joint channel estimation and hybrid beamforming in multi-carrier mm-Wave massive MIMO systems,” preprint arXiv:1912.10036, 2019.  
• N. Ronquillo, S.-E. Chiu, and T. Javidi, “Sequential learning of CSI for mmWave initial alignment,” preprint arXiv:1912.12738, 2019.  
• O. Chraiti, D. Chizhik, J. Du, R. A. Valenzuela, A. Ghrayeb, and C. Assi, “Beamforming learning for mmWave communication: Theory and experimental validation,” preprint arXiv:1912.12406, 2019.  
• H. Khan, A. Elgabli, S. Samarakoon, M. Bennis, and C. S. Hong, “Reinforcement learning based vehicle-cell association algorithm for highly mobile millimeter wave communication,” preprint arXiv:2001.07915, 2020.  
• V. Raj, and S. Kalyani, “Deep reinforcement learning based blind mmWave MIMO beam alignment,” preprint arXiv:2001.09251, 2020.  
• L. F. Abanto-Leon, and G. H. Sim, “Learning-based max-min fair hybrid precoding for mmWave multicasting,” preprint arXiv:2002.00670, 2020.  
• M. Alrabeiah, J. Booth, A. Hredzak, and A. Alkhateeb, “ViWi vision-aided mmWave beam tracking: Dataset, task, and baseline solutions,” preprint arXiv:2002.02445, 2020.  
• C. Huang, R. Mo, and C. Yuen, “Reconfigurable intelligent surface assisted multiuser MISO systems exploiting deep reinforcement learning,” preprint arXiv:2002.10072, 2020.  
• Q. Zhang, W. Saad, and M. Bennis, “Millimeter wave communications with an intelligent reflector: Performance optimization and distributional reinforcement learning,” preprint arXiv:2002.10572, 2020.  
• Y. Zhang, M. Alrabeiah, and A. Alkhateeb, “Learning beam codebooks with neural networks: Towards environment-aware mmWave MIMO,” preprint arXiv:2002.10663, 2020.  
• Y. Koda, J. Park, M. Bennis, K. Yamamoto, T. Nishio, and M. Morikura, “Communication-efficient multimodal split learning for mmWave received power prediction,” preprint arXiv:2003.00645, 2020.  
• H. Hojatian, V. N. Ha, J. Nadal, J.-F. Frigon, F. Leduc-Primeau, “RSSI-Based Hybrid Beamforming Design with Deep Learning,” preprint arXiv:2003.06042, 2020.  
• H. S. Ghadikolaei, H. Ghauch, G. Fodor, M. Skoglund, and C. Fischione, “A Hybrid Model-based and Data-driven Approach to Spectrum Sharing in mmWave Cellular Networks,” preprint arXiv:2003.08611, 2020.  
• P. Wu, Z. Liu, J. Cheng, “Learning a Measurement Matrix in Compressed CSI Feedback for Millimeter Wave Massive MIMO Systems,” preprint arXiv:1903.02127, 2019.  
• S. Khosravi, H. S. Ghadikolaei, M. Petrova, “Learning-based Handover in Mobile Millimeter-wave Networks,” preprint arXiv:2003.11009, 2020.  
• W. Ma, C. Qi and G. Ye Li, ”Machine learning for beam alignment in millimeter wave massive MIMO,” IEEE Wireless Communications Letters, Vol.9, No.6, pp.875-878, June 2020.  
• W. Ma, C. Qi, Z. Zhang and J. Cheng, ”Deep learning for compressed sensing based channel estimation in millimeter wave massive MIMO,” The Eleventh International Conference on Wireless Communications and Signal Processing (WCSP), Xi’an, China, Oct. 2019.  
• W. Ma, C. Qi, Z. Zhang and J. Cheng, ”Sparse channel estimation and hybrid precoding using deep learning for millimeter wave massive MIMO,” IEEE Transactions on Communications, Vol.68, No.5, pp.2838-2849, May 2020.  
• W. Wu, N. Cheng, N. Zhang, P. Yang, W. Zhuang and X. Shen, “Fast mmwave Beam Alignment via Correlated Bandit Learning,” in IEEE Transactions on Wireless Communications, vol. 18, no. 12, pp. 5894-5908, Dec. 2019.  
• A. M. Elbir, and S. Coleri, “Federated Learning for Hybrid Beamforming in mm-Wave Massive MIMO,” preprint arXiv:2005.09969, 2020.  
• A. M. Elbir, A. Papazafeiropoulos, P. Kourtessis and S. Chatzinotas, “Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems,” in IEEE Wireless Communications Letters, 2020.  
• F. Jiang, L. Yang, D. B. da Costa, and Q. Wu, “Channel Estimation via Direct Calculation and Deep Learning for RIS-Aided mmWave Systems,” preprint arXiv:2008.04704, 2020.  
• Y. Koda, M. Shinzaki, K. Yamamoto, T. Nishio, M. Morikura, Y. Shirato, D. Uchida, and N. Kita, “Millimeter Wave Communications on Overhead Messenger Wire: Deep Reinforcement Learning-Based Predictive Beam Tracking,” preprint arXiv:2012.00982, 2020.

# 资源分配 #

• L. Sanguinetti, A. Zappone and M. Debbah, “Deep learning power allocation in massive MIMO,” Proc. Asilomar Conference on Signals, Systems, and Computers, 2018. \[Simulation code\]  
• T. V. Chien, T. N. Canh, E. Björnson and E. G. Larsson, “Power control in cellular massive MIMO with varying user activity: A deep learning solution,” preprint arXiv:1901.03620, 2019.  
• H. Sun, X. Chen, Q. Shi, M. Hong, X. Fu and N. D. Sidiropoulos, “Learning to optimize: training deep neural networks for interference management,” IEEE Transactions on Signal Processing, vol. 66, no. 20, pp. 5438-5453, October 2018.  
• U. Challita, L. Dong and W. Saad, “Proactive resource management for LTE in unlicensed spectrum: a deep learning perspective,” IEEE Transactions on Wireless Communications, vol. 17, no. 7, pp. 4674-4689, July 2018.  
• F. Liang, C. Shen, W. Yu and F. Wu, “Towards optimal power control via ensembling deep neural networks,” preprint arXiv:1807.10025, 2018.  
• F. B. Mismar and B. L. Evans, “Q-Learning algorithm for VoLTE closed-loop power control in indoor small cells,” in Proc. Asilomar Conference on Signals, Systems, and Computers, October 2018. \[Simulation code\]  
• D. S. Wickramasuriya, C. A. Perumalla, K. Davaslioglu and R. D. Gitlin, “Base station prediction and proactive mobility management in virtual cells using recurrent neural networks,” 2017 IEEE 18th Wireless and Microwave Technology Conference (WAMICON), Cocoa Beach, FL, 2017.  
• M. Chen, W. Saad and C. Yin, “Virtual reality over wireless networks: Quality-of-service model and learning-based resource management,” in IEEE Transactions on Communications, vol. 66, no. 11, pp. 5621-5635, Nov. 2018.  
• M. Chen, W. Saad and C. Yin, “Echo state networks for self-organizing resource allocation in LTE-U with uplink–downlink decoupling,” in IEEE Transactions on Wireless Communications, vol. 16, no. 1, pp. 3-16, Jan. 2017.  
• M. Chen, W. Saad, C. Yin and M. Debbah, “Echo state networks for proactive caching in cloud-based radio access networks with mobile users,” in IEEE Transactions on Wireless Communications, vol. 16, no. 6, pp. 3520-3535, June 2017.  
• M. Chen, M. Mozaffari, W. Saad, C. Yin, M. Debbah and C. S. Hong, “Caching in the sky: Proactive deployment of cache-enabled unmanned aerial vehicles for optimized quality-of-experience,” in IEEE Journal on Selected Areas in Communications, vol. 35, no. 5, pp. 1046-1061, May 2017.  
• M. Chen, W. Saad, C. Yin and M. Debbah, “Echo state transfer learning for data correlation aware resource allocation in wireless virtual reality,” in Proc. Asilomar Conference on Signals, Systems, and Computers, October 2017.  
• M. Chen, O. Semiari, W. Saad, X. Liu and C. Yin, “Federated echo state learning for minimizing breaks in presence in wireless virtual reality networks,” preprint arXiv:1812.01202, 2018.  
• M. Chen, W. Saad and C. Yin, “Echo state learning for wireless virtual reality resource allocation in UAV-enabled LTE-U networks,”. In Proc. IEEE International Conference on Communications (ICC), May 2019.  
• S. Wang et al., “When edge meets learning: Adaptive control for resource-constrained distributed machine learning,” in Proc. IEEE Conference on Computer Communications (INFOCOM), Honolulu, HI, 2018.  
• Y. Sun, M. Peng and S. Mao, “Deep reinforcement learning based mode selection and resource management for green fog radio access networks,” in IEEE Internet of Things Journal., 2018.  
• S. Samarakoon, M. Bennis, W. Saad and M. Debbah, “Federated learning for ultra-reliable low-latency V2V communications,” in Proc. IEEE Global Communications Conference (GLOBECOM), 2018.  
• U. Challita, W. Saad and C. Bettstetter, “Cellular-connected UAVs over 5G: Deep reinforcement learning for interference management,” preprint arXiv:1801.05500, 2018.  
• U. Challita, A. Ferdowsi, M. Chen and W. Saad, “Machine learning for wireless connectivity and security of cellular-Connected UAVs,” preprint arXiv:1804.05348, 2018.  
• S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He and K. Chan, “Adaptive federated learning in resource constrained edge computing systems,” preprint arXiv:1804.05271, 2018.  
• Y. Yu, T. Wang and S. C. Liew, “Deep-reinforcement learning multiple access for heterogeneous wireless networks,” in Proc. IEEE International Conference on Communications (ICC), May 2018.  
• M. Chen, W. Saad, C. Yin and M. Debbah, “Data correlation-aware resource management in wireless virtual reality (VR): An echo state transfer learning approach,” preprint arXiv:1902.05181, 2019.  
• S.M. Zafaruddin, I. Bistritz, A. Leshem and D. Niyato, “Distributed learning for channel allocation over a shared spectrum,” in IEEE Journal on Selected Areas in Communications., 2019.  
• F. B. Mismar, J. Choi and B. L. Evans, “A framework for automated cellular network tuning with reinforcement learning,” in IEEE Transactions on Commununications, 2019.  
• M. K. Sharma, A. Zappone, M. Debbah and M. Assaad, “Deep learning based online power control for large energy harvesting networks,” in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2019.  
• Y. Lu, H. Lu, L. Cao, F. Wu and D. Zhu, “Learning deterministic policy with target for power control in wireless networks,” preprint arXiv:1902.07903, 2019.  
• D. Burghal, R. Wang and A. F. Molisch, “Deep learning and Gaussian process based band assignment in dual band systems,” preprint arXiv:1902.10890, 2019.  
• F. B. Mismar and B. L. Evans, “Deep learning in downlink coordinated multipoint in new radio heterogeneous networks,” in IEEE Wireless Communications Letters, 2019. \[Simulation code\]  
• F. Restuccia and T. Melodia, “Big data goes small: Real-time spectrum-driven embedded wireless networking through deep learning in the RF loop,” to appear in Proc. IEEE INFOCOM, 2019.  
• R. Amiri, M. A. Almasi, J. G. Andrews and H. Mehrpouyan, “Reinforcement learning for self-organization and power control of two-tier heterogeneous networks,” preprint arXiv:1812.09778, 2018.  
• T. V. Chien, E. Björnson and E. G. Larsson, “Sum spectral efficiency maximization in massive MIMO systems: Benefits from deep learning,” preprint arXiv:1903.08163, 2019.  
• Y. S. Nasir and D. Guo, “Multi-agent deep reinforcement learning for dynamic power allocation in wireless networks,” in IEEE Journal on Selected Areas in Communications., 2019.  
• Z. Li and C. Guo, “A multi-agent deep reinforcement learning based spectrum allocation framework for D2D underlay communications,” preprint arXiv:1904.06615, 2019.  
• O. Habachi, M.-A. Adjif and J.-P.Cances, “Fast uplink grant for NOMA: A federated learning based approach“, preprint arXiv:1904.07975, 2019.  
• M. Eisen, C. Zhang, L. F. O. Chamon, D. D. Lee and A. Ribeiro, “Dual domain learning of optimal resource allocations in wireless systems,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019.  
• Y. Zhang, C. Kang, T. Ma, Y. Teng, D. Guo, “Power Allocation in Multi-cell Networks Using Deep Reinforcement Learning,” in Proc. IEEE 88th Vehicular Technology Conference (VTC-Fall), 2018. \[Simulation code\]  
• H. Ye, G. Y. Li, “Deep Reinforcement Learning for Resource Allocation in V2V Communications,” in Proc. IEEE International Conference on Communications (ICC), 2018. \[Simulation code\]  
• N. N. Krishnan, E. Torkildson, N. Mandayam, D. Raychaudhuri, E.-H. Rantala and K. Doppler, “Optimizing throughput performance in distributed MIMO Wi-Fi networks using deep reinforcement learning,” preprint arXiv:1812.06885, 2018.  
• T. Şahin, R. Khalili, M. Boban and A. Wolisz, “Reinforcement learning scheduler for vehicle-to-vehicle communications outside coverage,” preprint arXiv:1904.12653, 2019.  
• K. I. Ahmed and E. Hossain, “A deep Q-learning method for downlink power allocation in multi-cell networks,” preprint arXiv:1904.13032, 2019.  
• C. Saha and H. S. Dhillon, “Machine learning meets stochastic geometry: Determinantal subset selection for wireless networks,” preprint arXiv:1905.00504, 2019. \[Simulation code\]  
• F. B. Mismar, J. Choi and B. L. Evans, “A framework for automated cellular network tuning with reinforcement learning,” preprint arXiv:1808.05140, 2018.  
• L. Liang, H. Ye and G. Ye Li, “Spectrum sharing in vehicular networks based on multi-agent reinforcement learning,” in IEEE Journal on Selected Areas in Communications., 2019.  
• G. Cao, Z. Lu, X. Wen, T. Lei, and Z. Hu, “AIF: An Artificial Intelligence Framework for Smart Wireless Network Management,” IEEE Communications Letters, vol. 22, no. 2, pp. 400-403, 2018. \[Simulation code\]  
• W. Lee, M. Kim, and D.-H. Cho, “Transmit Power Control Using Deep Neural Network for Underlay Device-to-Device Communication,” IEEE Wireless Communications Letters, vol. 8, no. 1, pp. 141-144, 2019. \[Simulation code\]  
• W. Cui, K. Shen, and W. Yu, “Spatial Deep Learning for Wireless Scheduling,” preprint arXiv:1808.01486, 2018. \[Simulation code\]  
• B. Matthiesen, A. Zappone, E. A. Jorswieck, and M. Debbah, “Deep Learning for Optimal Energy-Efficient Power Control in Wireless Interference Networks,” preprint arXiv:1808.01486, 2018. \[Simulation code\]  
• B. Yan, Y. Zhao, Y. Li, X. Yu, J. Zhang, Y. Wang, L. Yan, and S. Rahman, “Actor-Critic-Based Resource Allocation for Multi-Modal Optical Networks,” in IEEE Globecom Workshops, 2018. \[Simulation code\]  
• M. Kozlowski, R. McConville, R. Santos-Rodriguez, and R. Piechocki, “Energy Efficiency in Reinforcement Learning for Wireless Sensor Networks,” preprint arXiv:1812.02538, 2018. \[Simulation code\]  
• F. Wilhelmi, B. Bellalta, C. Cano, A. Jonsson, “Implications of Decentralized Q-learning Resource Allocation in Wireless Networks,” in IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 2017. \[Simulation code\]  
• Q. Zhang, Y.-C.Liang and H. V. Poor, “Intelligent user association for symbiotic radio networks using deep reinforcement learning,” preprint arXiv:1905.04041, 2019.  
• Y. Hua, R. Li, Z. Zhao, H. Zhang and X. Chen, “GAN-based deep distributional reinforcement learning for resource management in network slicing,” preprint arXiv:1905.03929, 2019  
• K. Pathak and A. Banerjee, “Harvest-or-transmit policy for cognitive radio networks: A learning theoretic approach,” preprint arXiv:1906.00548, 2019.  
• C. Sun and C. Yang, “Unsupervised deep learning for ultra-reliable and low-latency communications,” preprint arXiv:1905.13014, 2019.  
• A. Yazar and H. Arslan, “Selection of waveform parameters using machine learning for 5G and beyond,” preprint arXiv:1906.03909, 2019.  
• M. Liu, T. Song, J. Hu, J. Yang and G. Gui, “Deep learning-inspired message passing algorithm for efficient resource allocation in cognitive radio networks,” in IEEE Transactions on Vehicular Technology, 2019.  
• M. Liu, T. Song and G. Gui, “Deep cognitive perspective: Resource allocation for NOMA-based heterogeneous IoT with imperfect SIC,” in IEEE Internet of Things Journal, 2019.  
• G. Gui, H. Huang, Y. Song and H. Sari, “Deep learning for an effective nonorthogonal multiple access scheme,” in IEEE Transactions on Vehicular Technology, 2018.  
• N. Naderializadeh, J. Sydir, M. Simsek, H. Nikopour, and S. Talwar, “When multiple agents learn to schedule: A distributed radio resource management framework“, preprint arXiv:1906.08792, 2019.  
• J. Gao, M. R. A. Khandaker, F. Tariq, K.-K. Wong, and R. T. Khan, “Deep neural network based resource allocation for V2X communications,” preprint arXiv:1906.10194, 2019.  
• F. B. Mismar, B. L. Evans, and A. Alkhateeb, “Deep reinforcement learning for 5G Networks: Joint beamforming, power control, and interference coordination,” preprint arXiv:1907.00123, 2019.  
• L. Liang, H. Ye, G. Yu and G. Y. Li, “Deep learning based wireless resource allocation with application to vehicular networks,” preprint arXiv:1907.03289, 2019.  
• Y. Shen, Y. Shi, J. Zhang and K. B. Letaief, “A graph neural network approach for scalable wireless power control,” preprint arXiv:1907.08487, 2019.  
• R. Zhang, P. Cheng, Z. Chen, Y. Li and B. Vucetic, “A learning-based two-stage spectrum sharing strategy with multiple primary transmit power levels,” preprint arXiv:1907.09949, 2019.  
• M. M. Amiri and D. Gunduz, “Federated learning over wireless fading channels,” preprint arXiv:1907.09769, 2019.  
• R. Zhang, P. Cheng, Z. Chen, Y. Li and B. Vucetic, “Learning multiple primary transmit power levels for smart spectrum sharing,” in Proc. IEEE International Conference on Communications (ICC), 2019.  
• E. Nisioti and N. Thomos, “Robust coordinated reinforcement learning for MAC design in sensor networks,” in IEEE Journal on Selected Areas in Communications., 2019.  
• S. J. Darak and M. K. Hanawal, “Multi-player multi-armed bandits for stable allocation in heterogeneous ad-hoc networks,” in IEEE Journal on Selected Areas in Communications., 2019.  
• H. Lee, S. H. Lee and T. Q. S. Quek, “Deep learning for distributed optimization: Applications to wireless resource management,” in IEEE Journal on Selected Areas in Communications., 2019.  
• A. Ortiz, A. Asadi, M. Engelhardt, A. Klein and M. Hollick, “CBMoS: Combinatorial bandit learning for mode selection and resource allocation in D2D systems,” in IEEE Journal on Selected Areas in Communications., 2019.  
• C. He, Y. Hu, Y. Chen and B. Zeng, “Joint power allocation and channel assignment for NOMA with deep reinforcement learning,” in IEEE Journal on Selected Areas in Communications., 2019.  
• Y. Huang, X. Mo, J. Xu and L. Qiu, “Reinforcement learning for maneuver design in UAV-enabled NOMA system with segmented channel,” preprint arXiv:1908.03984, 2019.  
• S. Khan and S. Y. Shin, “Deep learning aided transmit power estimation in mobile communication system,” in IEEE Communications Letters, 2019.  
• C. Zhong, Z. Lu, M. C. Gursoy and S. Velipasalar, “A deep actor-critic reinforcement learning framework for dynamic multichannel access,” preprint arXiv:1908.08401, 2019.  
• L. Lei, L. You, Q. He, T. X. Vu, S. Chatzinotas, D. Yuan and B. Ottersten, “Learning-assisted optimization for energy-efficient scheduling in deadline-aware NOMA systems,” in IEEE Transactions on Green Communications and Networking, 2019.  
• C. D’Andrea, A. Zappone, S. Buzzi and M. Debbah, “Uplink power control in cell-free massive MIMO via deep learning,” preprint arXiv:1908.11121, 2019.  
• Y. Yu, T. Wang and S. C. Liew, “Deep-reinforcement learning multiple access for heterogeneous wireless networks,” in IEEE Journal on Selected Areas in Communications, 2019.  
• K. N. Doan, M. Vaezi, W. Shin, H. V. Poor, H. Shin and T. Q. S. Quek, “Power allocation in cache-aided NOMA systems: Optimization and deep reinforcement learning approaches,” preprint arXiv:1909.11074, 2019.  
• Y. Yu, S. C. Liew and T. Wang, “Non-Uniform Time-Step Deep Q-Network for Carrier-Sense Multiple Access in Heterogeneous Wireless Networks,” in IEEE Transactions on Mobile Computing, 2020.  
• R. Raghu, P. Upadhyaya, M. Panju, V. Agarwal, and V. Sharma, “Deep reinforcement learning based power control for wireless multicast systems,” preprint arXiv:1910.05308, 2019.  
• A. B.Zaky, J. Zhexue Huang, K. , and B. M.ElHalawany, “Generative neural network based spectrum sharing using linear sum assignment problems,” preprint arXiv:1910.05510, 2019.  
• R. Raghu, P. Upadhyaya, M. Panju, V. Aggarwal, and V. Sharma, “Deep reinforcement learning based power control for wireless multicast systems,” preprint arXiv:1910.05308, 2019.  
• A. T. Z. Kasgari, W. Saad, M. Mozaffari, H. V. Poor, “Experienced deep reinforcement learning with generative adversarial networks (GANs) for model-free ultra reliable low latency communication,” arXiv:1911.03264, 2019.  
• A. Tondwalkar, A. Kwasinski, “Deep reinforcement learning for distributed uncoordinated cognitive radios resource allocation,” preprint arXiv:1911.03366, 2019.  
• Y. Zhou, F. Zhou, Y. Wu, R. Q. Hu and Y. Wang, “Subcarrier assignment schemes based on Q-Learning in wideband cognitive radio networks,” preprint arXiv:1911.07149, 2019.  
• Y. Alghorani, A. Chekkouri, D. Chekired, S. Muhaidat, S. Pierre and M. Flanagan, “Improved S-AF and S-DF relaying schemes using machine learning based power allocation over cascaded Rayleigh fading channels,” preprint arXiv:1912.01342, 2019.  
• H. Khan, M. M. Butt, S. Samarakoon, P. Sehier, and M. Bennis, “Deep Learning Assisted CSI Estimation for Joint URLLC and eMBB Resource Allocation,” preprint arXiv:2003.05685, 2020.  
• S. Takabe and T. Wadayama, “Deep Unfolded Multicast Beamforming“, preprint arXiv:2004.09345, 2020.  
• M. Zecchin, D. Gesbert, and M. Kountouris, “Team Deep Mixture of Experts for Distributed Power Control,” preprint arXiv:2007.14147, 2020.  
• Y. Lu, P. Cheng, Z. Chen, W. H. Mow, Y. Li, and B. Vucetic, “Deep Multi-Task Learning for Cooperative NOMA: System Design and Principles,” preprint arXiv:2007.13495, 2020.  
• M. Eisen, C. Zhang, L. F. O. Chamon, D. D. Lee and A. Ribeiro, “Learning Optimal Resource Allocations in Wireless Systems,” in IEEE Transactions on Signal Processing, vol. 67, no. 10, pp. 2775-2790, May, 2019.  
• M. Eisen and A. Ribeiro, “Optimal Wireless Resource Allocation With Random Edge Graph Neural Networks,” in IEEE Transactions on Signal Processing, vol. 68, pp. 2977-2991, 2020.  
• D. S. Kalogerias, M. Eisen, G. J. Pappas, and A. Ribeiro, “Model-Free Learning of Optimal Ergodic Policies in Wireless Systems,” preprint arXiv:1911.03988, 2019.  
• M. Eisen, M. M. Rashid, D. Cavalcanti and A. Ribeiro, “Control-Aware Scheduling for Low Latency Wireless Systems with Deep Learning,” IEEE International Conference on Communications Workshops (ICC Workshops), Dublin, Ireland, 2020.  
• S. Ali, H. Asgharimoghaddam, N. Rajatheva, W. Saad and J. Haapola, “Contextual Bandit Learning for Machine Type Communications in the Null Space of Multi-Antenna Systems,” in IEEE Transactions on Communications, vol. 68, no. 2, pp. 1284-1296, Feb. 2020.  
• S. Ali, A. Ferdowsi, W. Saad and N. Rajatheva, “Sleeping Multi-Armed Bandits for Fast Uplink Grant Allocation in Machine Type Communications,” IEEE Globecom Workshops (GC Wkshps), Abu Dhabi, United Arab Emirates, 2018.  
• S. Ali, A. Ferdowsi, W. Saad, N. Rajatheva and J. Haapola, “Sleeping Multi-Armed Bandit Learning for Fast Uplink Grant Allocation in Machine Type Communications,” in IEEE Transactions on Communications, 2020.  
• R. Mennes, M. Claeys, F. A. P. De Figueiredo, I. Jabandžić, I. Moerman and S. Latré, “Deep Learning-Based Spectrum Prediction Collision Avoidance for Hybrid Wireless Environments,” in IEEE Access, 2019.  
• Y. Yu, S. C. Liew, and T. Wang, “Multi-Agent Deep Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks with Imperfect Channels,” preprint arXiv:2003.11210, 2020.  
• M. G. Khoshkholgh and H. Yanikomeroglu, “Faded-Experience Trust Region Policy Optimization for Model-Free Power Allocation in Interference Channel,” preprint arXiv:2008.01705, 2020.  
• B. Özbek, M. Pischella and D. Le Ruyet, “Energy Efficient Resource Allocation for Underlaying Multi-D2D Enabled Multiple-Antennas Communications,” in IEEE Transactions on Vehicular Technology, vol. 69, no. 6, pp. 6189-6199, June 2020.  
• M. G. Khoshkholgh and H. Yanikomeroglu, “Learning Power Control from a Fixed Batch of Data,” preprint arXiv:2008.02669, 2020.  
• X. Foukas, M. K. Marina and K. Kontovasilis, “Iris: Deep Reinforcement Learning Driven Shared Spectrum Access Architecture for Indoor Neutral-Host Small Cells,” in IEEE Journal on Selected Areas in Communications, vol. 37, no. 8, pp. 1820-1837, Aug. 2019.  
• C. Hasan and M. K. Marina, “Communication-Free Inter-Operator Interference Management in Shared Spectrum Small Cell Networks,” in IEEE Transactions on Cognitive Communications and Networking, vol. 5, no. 3, pp. 661-677, Sept. 2019.  
• Y. S. Nasir and D. Guo, “Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile Networks,” preprint arXiv:2009.06681, 2020.  
• Q.-V. Pham, D. C. Nguyen, S. Mirjalili, D. T. Hoang, D. N. Nguyen, P. N. Pathirana, and W.-J. Hwang, “Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications,” preprint arXiv:2007.15221, 2020.  
• J. Zhou, S. Dang, B. Shihada, and M.-S. Alouini, “Power Allocation for Relayed OFDM with Index Modulation Assisted by Artificial Neural Network,” preprint arXiv:2010.12959, 2020.  
• M. Guo and M. C. Gursoy, “Statistical Learning Based Joint Antenna Selection and User Scheduling for Single-Cell Massive MIMO Systems,” preprint arXiv:2010.13848, 2020.  
• E. Almazrouei, G. Gianini, N. Almoosa, and E. Damiani, “What can Machine Learning do for Radio Spectrum Management,” In Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks (Q2SWinet), 2020.  
• H. Sun, W. Pu, M. Zhu, X. Fu, T.-H. Chang, and M. Hong, “Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment,” preprint arXiv:2011.07782, 2020.  
• R. Raghu, M. Panju, V. Aggarwal, and V. Sharma, “Scheduling and Power Control for Wireless Multicast Systems via Deep Reinforcement Learning,” preprint arXiv:2011.14799, 2020.  
• X. Gao, Y. Liu, X. Liu, and Z. Qin, “Resource Allocation in IRSs Aided MISO-NOMA Networks: A Machine Learning Approach,” preprint arXiv:2012.00548, 2020.  
• Z. Zhou, Y. Xin, H. Chen, C. Zhang, and L. Liu, “Pareto Deterministic Policy Gradients and Its Application in 5G Massive MIMO Networks,” preprint arXiv:2012.01279, 2020.

# 其他类 #

• A. Ligata, E. Perenda and H. Gacanin, “Quality of experience inference for video services in home WiFi networks,” in IEEE Communications Magazine, vol. 56, no. 3, pp. 187-193, March 2018.  
• R. Atawia and H. Gacanin, “Self-deployment of future indoor Wi-Fi networks: an artificial intelligence approach,” in Proc. IEEE Global Communications Conference, December 2017.  
• A. Balatsoukas-Stimming, “Non-linear digital self-interference cancellation for in-band full-duplex radios using neural networks,” in 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), June 2018.  
• S. Aneja, N. Aneja and M. S. Islam, “IoT device fingerprint using deep learning,” preprint arXiv:1902.01926, 2019.  
• Y. Kurzo, A. Burg and A. Balatsoukas-Stimming, “Design and implementation of a neural network aided self-interference cancellation scheme for full-duplex radios,” preprint arXiv:1812.00449, 2018.  
• A. Ozcelikkale, M. Koseoglu and M. Srivastava, “Optimization vs. reinforcement learning for wirelessly powered sensor networks,” in Proc. IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2018.  
• E. Balevi and J. G. Andrews, “Online antenna tuning in heterogeneous cellular networks with deep reinforcement learnings,” preprint arXiv:1903.06787, 2019.  
• M. Di Renzo, M. Debbah, D.-T. Phan-Huy, A. Zappone, M.-S. Alouini, C. Yuen, V. Sciancalepore, G. C. Alexandropoulos, J. Hoydis, H. Gacanin, J. de Rosny, A. Bounceu, G. Lerosey and M. Fink, “Smart radio environments empowered by AI reconfigurable meta-surfaces: An idea whose time has come,” preprint arXiv:1903.08925, 2019.  
• V. Yajnanarayana, H. Rydén, L. Hévizi, A. Jauhari and M. Cirkic, “5G handover using reinforcement learning,” prerint arXiv:1904.02572, 2019.  
• A. Ortiz, H. Al-Shatri, T. Weber and A. Klein, “Multi-agent reinforcement learning for energy harvesting two-hop communications with a partially observable state,” preprint arXiv:1702.06185, 2017.  
• M. Angjelichinoski, K. F. Trillingsgaard and P. Popovski, “A statistical learning approach to ultra-reliable low latency communication,” in IEEE Transactions on Communications, 2019.  
• A. Taha, M. Alrabeiah and A. Alkhateeb, “Enabling large intelligent surfaces with compressive sensing and deep learning,” preprint arXiv:1904.10136, 2019. \[Simulation code\]  
• C. Häger, H. D. Pfister, R. M. Bütler, G. Liga and A. Alvarado, “Revisiting multi-step nonlinearity compensation with machine learning,” preprint arXiv:1904.09807, 2019.  
• V. Houtsma, E. Chou, and D. van Veen, “92 and 50 Gbps TDM-PON using neural network enabled receiver equalization specialized for PON,” in Optical Fiber Communication Conference (OFC), 2019.  
• Z. Zhang, Y. Li, L. Liu and W. Hou, “Fixed-symbol aided random access scheme for machine-to-machine communications,” preprint arXiv:1904.10874, 2019.  
• A. M. Tonello, N. A. Letizia, D. Righini and F. Marcuzzi, “Machine learning tips and tricks for power line communications,” preprint arXiv:1904.11949, 2019.  
• S. Kokalj-Filipovic, R. Miller and J. Morman, “Autoencoders for training compact deep learning RF classifiers for wireless protocols,” preprint arXiv:1904.11874, 2019.  
• A. Tato, C. Mosquera, P. Henarejos and A. Pérez-Neira, “Neural network aided computation of mutual information for adaptation of spatial modulation,” preprint arXiv:1904.10844, 2019.  
• L. Darwesh and S. Arno, “Energy reduction using multi-channels optical wireless communication based OFDM“, in Proc. of the SPIE, 2017.  
• C. Liaskos, A. Tsioliaridou, S. Nie, A. Pitsillides, S. Ioannidis and I. Akyildiz, “An interpretable neural network for configuring programmable wireless environments,” preprint arXiv:1905.02495, 2019.  
• M. Alrabeiah and A. Alkhateeb, “Deep learning for TDD and FDD massive MIMO: Mapping channels in space and frequency,” preprint arXiv:1905.03761, 2019. \[Simulation code\]  
• J. Liu, B. Krishnamachari, S. Zhou, and Z. Niu, “DeepNap: Data-Driven Base Station Sleeping Operations Through Deep Reinforcement Learning,” IEEE Internet of Things Journal, vol. 5, no. 6, pp. 4273-4282, 2018. \[Simulation code\]  
• C. Morin, L. Cardoso, J. Hoydis, J.-M. Gorce, and T. Vial, “Transmitter classification with supervised deep learning,” preprint arXiv:1905.07923, 2019.  
• C. Huang, G. C. Alexandropoulos, C. Yuen andM. Debbah, “Indoor signal focusing with deep learning designed reconfigurable intelligent surfaces,” preprint arXiv:1905.07726, 2019.  
• H. Zhang, B. Ai, W. Xu, L. Xu and S. Cui, “Multi-antenna channel interpolation via Tucker decomposed extreme learning machine,” in IEEE Transactions on Vehicular Technology, 2019.  
• Y. Liu, X. Kuai, X. Yuan, Y. Liang and L. Zhou, “Learning based iterative interference cancellation for cognitive internet of things,” in IEEE Internet of Things Journal., 2019.  
• H. Huang, W. Xia, J. Xiong, J. Yang, G. Zheng and X. Zhu, “Unsupervised learning-based fast beamforming design for downlink MIMO,” in IEEE Access, 2019.  
• R. Shafin, H. Chen, Y. H. Nam, S. Hur, J. Park, J. Zhang, J. Reed, and L. Liu, “Self-tuning sectorization: Deep reinforcement learning meets broadcast beam optimization,” preprint arXiv:1906.06021, 2019.  
• H.-P. Ren, H.-E. Zhao, C. Bai, H.-P. Yin and C. Grebogi, “Artificial intelligence enhances the performance of chaos-based wireless communication,” preprint arXiv:1907.01521, 2019.  
• M. A. Ouameur and D. Massicotte, “Autoencoder for interconnect’s bandwidth relaxation in large scale MIMO-OFDM processing,” preprint arXiv:1907.12613, 2019.  
• F. Ait Aoudia and J. Hoydis, “Towards Hardware Implementation of Neural Network-based Communication Algorithms,” in Proc. IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2019.  
• J. Guo, J. Wang, C.-K. Wen, S. Jin and G. Y. Li, “Compression and acceleration of neural networks for communications,” preprint arXiv:1907.13269, 2019.  
• P. Yang, Y. Xiao, M. Xiao, Y. L. Guan, S. Li and W. Xiang, “Adaptive spatial modulation MIMO based on machine learning,” in IEEE Journal on Selected Areas in Communications., 2019.  
• N. Strodthoff, B. Göktepe, T. Schierl, C. Hellge and W. Samek, “Enhanced machine learning techniques for early HARQ feedback prediction in 5G,” in IEEE Journal on Selected Areas in Communications., 2019.  
• S. Seyedsalehi, V. Pourahmadi, H. Sheikhzadeh and A. H. G. Foumani, “Propagation channel modeling by deep learning techniques,” preprint arXiv:1908.06767, 2019.  
• J. Yu, Y.-K. Huang, S. Zhang, E. Ip, D. C. Kilper, T. J. Xia, and G. A. Wellbrock, “Neural-network-based G-OSNR estimation of probabilistic-shaped 144QAM channels in DWDM metro network field trial,” in Proc. OptoElectronics and Communications Conference (OECC) and Proc. International Conference on Photonics in Switching and Computing (PSC), 2019.  
• M. Zhou, X. Huang, Z. Feng and Y. Liu, “Coarse frequency offset estimation in MIMO systems using neural networks: A solution with higher compatibility,” in IEEE Access, 2019.  
• Z. Zhang, Y. Li, C. Huang, Q. Guo, C. Yuen and Y. L. Guan, “DNN-aided block sparse Bayesian learning for user activity detection and channel estimation in grant-free non-orthogonal random access,” preprint arXiv:1910.02953, 2019.  
• F. B. Mismar, A. AlAmmouri, A. Alkhateeb, J. G. Andrews, and B. L. Evans, “Deep learning predictive band switching in wireless networks,” preprint arXiv:1910.05305, 2019.  
• J-H. Lee, “Minimum euclidean distance evaluation using deep neural networks,” International Journal of Electronics and Communicationsn 2019.  
• S. Kojima, K. Maruta and C. Ahn, “Adaptive modulation and coding using neural network based SNR estimation,” in IEEE Access, 2019.  
• M. Alrabeiah, A. Hredzak, Z. Liu, and A. Alkhateeb, “ViWi: A deep learning dataset framework for vision-aided wireless communications,” preprint arXiv:1911.06257, 2019.  
• A. A. M. Habiby and A. Thoppu, “Application of reinforcement learning for 5G scheduling parameter optimization“, preprint arXiv:1911.07608, 2019.  
• R. Levie, Ç. Yapar, G. Kutyniok, and G. Caire, “RadioUNet: Fast radio map estimation with convolutional neural networks,” preprint arXiv:1911.09002, 2019.  
• V. Sathya, A. Dziedzic, M. Ghosh, and S. Krishnan, “Machine learning based detection of multiple Wi-Fi BSSs for LTE-U CSAT,” preprint arXiv:1911.09292, 2019.  
• D. Righini, N. A. Letizia and A. M. Tonello, “Synthetic power line communications channel generation with autoencoders and GANs,” in Proc. IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 2019.  
• M. B. Khalilsarai, Y. Song, T. Yang, S. Haghighatshoar, and G. Caire, “Uplink-downlink channel covariance transformations and precoding design for FDD massive MIMO,” preprint arXiv:1912.02455, 2019.  
• J. Gao, C. Zhong, X. Chen, H. Lin, and Z. Zhang, “Unsupervised learning for passive beamforming,” preprint arXiv:2001.02348, 2020.  
• W. Xia, G. Zheng, Y. Zhu, J. Zhang, J. Wang, and A. P. Petropulu, “A deep learning framework for optimization of MISO downlink beamforming,” preprint arXiv:1901.00354, 2019.  
• W. Xia, G. Zheng and K.-K. Wong, Hongbo Zhu, “Model-driven beamforming neural networks,” preprint arXiv:2001.05277, 2020.  
• Y. Al-Eryani, M. Akrout, and E. Hossain, “Simultaneous energy harvesting and information transmission in a MIMO full-duplex system: A machine learning-based design,” preprint arXiv:2002.06193, 2020.  
• A. Taha, Y. Zhang, F. B. Mismar, and A. Alkhateeb, “Deep reinforcement learning for intelligent reflecting surfaces: Towards standalone operation,” preprint arXiv:2002.11101, 2020.  
• H. Gacanin, M. Di Renzo, “Wireless 2.0: Towards an intelligent radio environment empowered by reconfigurable meta-surfaces and artificial intelligence,” preprint arXiv:2002.11040, 2020.  
• J. E. R. Ramirez and Y. Minami, “Design of neural network quantizers for networked control systems,” in Electronics, 2019.  
• M. Arvinte, A. H. Tewfik and S. Vishwanath, “Deep log-likelihood ratio quantization,” preprint arXiv:1903.04656, 2019.  
• A. Balatsoukas-Stimming, O. Castañeda, S. Jacobsson, G. Durisi and C. Studer, “Neural-network optimized 1-bit precoding for massive MU-MIMO,” preprint arXiv:1903.03718, 2019.  
• C. She, R. Dong, Z. Gu, Z. Hou, Y. Li, W. Hardjawana, C. Yang, L. Song, and B. Vucetic, “Deep learning for ultra-reliable and low-latency communications in 6G networks,” preprint arXiv:2002.11045, 2020.  
• J. Zhang, W. Xia, M. You, G. Zheng, S. Lambotharan, and K.-K. Wong, “Deep Learning Enabled Optimization of Downlink Beamforming Under Per-Antenna Power Constraints: Algorithms and Experimental Demonstration,” preprint arXiv:2002.12589, 2020.  
• Z. Aharoni, D. Tsur, Z. Goldfeld, and H. H. Permuter, “Capacity of Continuous Channels with Memory via Directed Information Neural Estimator,” preprint arXiv:2003.04179, 2020.  
• R. Barazideh, O. Semiari, S. Niknam, and B. Natarajan, “Reinforcement Learning for Mitigating Intermittent Interference in Terahertz Communication Networks,” preprint arXiv:2003.04832, 2020.  
• D. A. Awan, R. L. G. Cavalcante, Z. Utkovski and S. Stanczak, “SET-THEORETIC LEARNING FOR DETECTION IN CELL-LESS C-RAN SYSTEMS,” IEEE Global Conference on Signal and Information Processing (GlobalSIP), Anaheim, CA, USA, 2018.  
• T. Xu, T. Xu and I. Darwazeh, “Deep Learning for Interference Cancellation in Non-Orthogonal Signal Based Optical Communication Systems,” Progress in Electromagnetics Research Symposium (PIERS-Toyama), 2018.  
• C. Tarver, A. Balatsoukas-Stimming and J. R. Cavallaro, “Design and Implementation of a Neural Network Based Predistorter for Enhanced Mobile Broadband,” IEEE International Workshop on Signal Processing Systems (SiPS), 2019.  
• H. Yin, X. Guo, P. Liu, X. Hei, and Y. Gao, “Predicting Channel Quality Indicators for 5G Downlink Scheduling in a Deep Learning Approach,” preprint arXiv:2008.01000, 2020.  
• M. Elwekeil, S. Jiang, T. Wang and S. Zhang, “Deep Convolutional Neural Networks for Link Adaptations in MIMO-OFDM Wireless Systems,” in IEEE Wireless Communications Letters, vol. 8, no. 3, pp. 665-668, June 2019.  
• A. M. Elbir and K. V. Mishra, “Sparse Array Selection Across Arbitrary Sensor Geometries with Deep Transfer Learning,” preprint arXiv:2004.11637, 2020.  
• K. Kong, W.-J. Song, and M. Min, “Deep-learning-based precoding in multiuser MIMO downlink channels with limited feedback,” preprint arXiv:2008.04147, 2020.  
• Ö. Özdogan and E. Björnson, “Deep Learning-based Phase Reconfiguration for Intelligent Reflecting Surfaces,” preprint arXiv:2009.13988, 2020.  
• S. Itahara, T. Nishio, M. Morikura, and K. Yamamoto, “Online Trainable Wireless Link Quality Prediction System using Camera Imagery,” preprint arXiv:2009.13864, 2020.  
• T. Jiang, H. V. Cheng, and W. Yu, “Learning to Beamform for Intelligent Reflecting Surface with Implicit Channel Estimate,” preprint arXiv:2009.14404, 2020.  
• Y. Yuan, G. Zheng, K.-K. Wong, B. Ottersten, and Z.-Q. Luo, “Transfer Learning and Meta Learning Based Fast Downlink Beamforming Adaptation,” preprint arXiv:2011.00903, 2020.  
• M. Liu and R. Wang, “Deep Reinforcement Learning Based Dynamic Power and Beamforming Design for Time-Varying Wireless Downlink Interference Channel,” preprint arXiv:2011.03780, 2020.  
• M. Zhu, T.-H. Chang, and M. Hong, “Learning to Beamform in Heterogeneous Massive MIMO Networks,” preprint arXiv:2011.03971, 2020.  
• Y. Chen, X. Lin, T. Khan, M. Afshang, and M. Mozaffari, “5G Air-to-Ground Network Design and Optimization: A Deep Learning Approach,” preprint arXiv:2011.08379, 2020.

[IEEE_Library]: https://mlc.committees.comsoc.org/research-library/
[Link 1]: https://github.com/IIT-Lab/Paper-with-Code-of-Wireless-communication-Based-on-DL