【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 08:13 122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.  
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# 信道编码和解码 #

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# 端到端通信的学习 #

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• 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.  
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• 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.  
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• 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.  
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• 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 #

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• 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 #

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• 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