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
附带源码的文献汇总

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.
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信道编码和解码

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• 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.
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• 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]
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• 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, “* 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.
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其他类

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