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引用“”的文章Deep Learning in Physical Layer Communications

标题/作者	年份
Towards 6G wireless communication networks: Vision, enabling technologies, and new paradigm shifts X You, CX Wang, J Huang, X Gao, Z Zhang, M Wang, Y Huang, C Zhang, ... Science China Information Sciences 64, 1-74	2021
6G and beyond: The future of wireless communications systems IF Akyildiz, A Kak, S Nie IEEE access 8, 133995-134030	2020
Reconfigurable intelligent surfaces: Principles and opportunities Y Liu, X Liu, X Mu, T Hou, J Xu, M Di Renzo, N Al-Dhahir IEEE communications surveys & tutorials 23 (3), 1546-1577	2021
A very brief introduction to machine learning with applications to communication systems O Simeone IEEE Transactions on Cognitive Communications and Networking 4 (4), 648-664	2018
Wireless networks design in the era of deep learning: Model-based, AI-based, or both? A Zappone, M Di Renzo, M Debbah IEEE Transactions on Communications 67 (10), 7331-7376	2019
Quantum machine learning for 6G communication networks: State-of-the-art and vision for the future SJ Nawaz, SK Sharma, S Wyne, MN Patwary, M Asaduzzaman IEEE access 7, 46317-46350	2019
Model-driven deep learning for physical layer communications H He, S Jin, CK Wen, F Gao, GY Li, Z Xu IEEE Wireless Communications 26 (5), 77-83	2019
Deep learning enabled semantic communication systems H Xie, Z Qin, GY Li, BH Juang IEEE Transactions on Signal Processing 69, 2663-2675	2021
6G Visions: Mobile ultra-broadband, super internet-of-things, and artificial intelligence L Zhang, YC Liang, D Niyato China Communications 16 (8), 1-14	2019
Model-driven deep learning for MIMO detection H He, CK Wen, S Jin, GY Li IEEE Transactions on Signal Processing 68, 1702-1715	2020
Deep learning-based end-to-end wireless communication systems with conditional GANs as unknown channels H Ye, L Liang, GY Li, BH Juang IEEE Transactions on Wireless Communications 19 (5), 3133-3143	2020
Reinforcement learning in multiple-UAV networks: Deployment and movement design X Liu, Y Liu, Y Chen IEEE Transactions on Vehicular Technology 68 (8), 8036-8049	2019
Deep CNN-based channel estimation for mmWave massive MIMO systems P Dong, H Zhang, GY Li, IS Gaspar, N NaderiAlizadeh IEEE Journal of Selected Topics in Signal Processing 13 (5), 989-1000	2019
ComNet: Combination of deep learning and expert knowledge in OFDM receivers X Gao, S Jin, CK Wen, GY Li IEEE Communications Letters 22 (12), 2627-2630	2018
Convolutional neural network-based multiple-rate compressive sensing for massive MIMO CSI feedback: Design, simulation, and analysis J Guo, CK Wen, S Jin, GY Li IEEE Transactions on Wireless Communications 19 (4), 2827-2840	2020
Deep learning-based channel estimation for doubly selective fading channels Y Yang, F Gao, X Ma, S Zhang IEEE Access 7, 36579-36589	2019
Interplay between NOMA and other emerging technologies: A survey M Vaezi, GAA Baduge, Y Liu, A Arafa, F Fang, Z Ding IEEE Transactions on Cognitive Communications and Networking 5 (4), 900-919	2019
A tutorial on ultrareliable and low-latency communications in 6G: Integrating domain knowledge into deep learning C She, C Sun, Z Gu, Y Li, C Yang, HV Poor, B Vucetic Proceedings of the IEEE 109 (3), 204-246	2021
Deep-learning-based wireless resource allocation with application to vehicular networks L Liang, H Ye, G Yu, GY Li Proceedings of the IEEE 108 (2), 341-356	2019
Deep unfolding for communications systems: A survey and some new directions A Balatsoukas-Stimming, C Studer 2019 IEEE International Workshop on Signal Processing Systems (SiPS), 266-271	2019

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