[1] |
WONG V S , SCHOBER R , NG D W K ,et al. Key technologies for 5G wireless systems[M]. Cambridge: Cambridge University PressPress, 2017.
|
[2] |
YANG P , XIAO Y , XIAO M ,et al. 6G wireless communications:vision and potential techniques[J]. IEEE Network, 2019,33(4): 70-75.
|
[3] |
LOVE D J , HEATH R W , LAU V K N ,et al. An overview of limited feedback in wireless communication systems[J]. IEEE Journal on Selected Areas in Communications, 2008,26(8): 1341-1365.
|
[4] |
ZHOU Y , HERDIN M , SAYEED A M ,et al. Experimental study of MIMO channel statistics and capacity via the virtual channel representation[D]. Madison:University of Wisconsin-Madison, 2007.
|
[5] |
KYRITSI P , COX D C , VALENZUELA R A ,et al. Correlation analysis based on MIMO channel measurements in an indoor environment[J]. IEEE Journal on Selected Areas in Communications, 2003,21(5): 713-720.
|
[6] |
KUO P H , KUNG H T , TING P A . Compressive sensing based channel feedback protocols for spatially-correlated massive antenna arrays[C]// 2012 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2012: 492-497.
|
[7] |
RAO X B , LAU V K . Distributed compressive CSIT estimation and feedback for FDD multi-user massive MIMO systems[J]. IEEE Transactions on Signal Processing, 2014,62(12): 3261-3271.
|
[8] |
SHEA T O , HOYDIS J . An introduction to deep learning for the physical layer[J]. IEEE Transactions on Cognitive Communications and Networking, 2017,3(4): 563-575.
|
[9] |
QIN Z J , YE H , LI G Y ,et al. Deep learning in physical layer communications[J]. IEEE Wireless Communications, 2019,26(2): 93-99.
|
[10] |
张静, 金石, 温朝凯 ,等. 基于人工智能的无线传输技术最新研究进展[J]. 电信科学, 2018,34(8): 46-55.
|
|
ZHANG J , JIN S , WEN C K ,et al. An overview of wireless transmission technology utilizing artificial intelligence[J]. Telecommunications Science, 2018,34(8): 46-55.
|
[11] |
WANG T Q , WEN C K , WANG H Q ,et al. Deep learning for wireless physical layer:opportunities and challenges[J]. China Communications, 2017,14(11): 92-111.
|
[12] |
WEN C K , JIN S , WONG K K ,et al. Channel estimation for massive MIMO using Gaussian-mixture Bayesian learning[J]. IEEE Transactions on Wireless Communications, 2015,14(3): 1356-1368.
|
[13] |
DAUBECHIES I , DEFRISE M , MOL C D . An iterative thresholding algorithm for linear inverse problems with a sparsity constraint[J]. Communications on Pure and Applied Mathematics, 2003,57(11): 1413-1457.
|
[14] |
DONOHO D L , MALEKI A , MONTANARI A . Message passing algorithms for compressed sensing[J]. Proceedings of the National Academy of Sciences of the United States of America, 2009,106(45): 18914-18919.
|
[15] |
LI C , YIN W , ZHANG Y ,et al. TVAL3:TV minimization by augmented Lagrangian and alternating direction algorithms[E/OL]. .
|
[16] |
METZLER C A , MALEKI A , BARANIUK R G . From denoising to compressed sensing[J]. IEEE Transactions on Information Theory, 2016,62(9): 5117-5144.
|
[17] |
LOHIT S , KULKARNI K , KERVICHE R ,et al. Convolutional neural networks for non-iterative reconstruction of compressively sensed images[J]. IEEE Transactions on Computational Imaging, 2018,4(3): 326-340.
|
[18] |
MOUSAVI A , DASARATHY G , BARANIUK R G . DeepCodec:adaptive sensing and recovery via deep convolutional neural networks[J]. arXiv:1707.03386, 2017
|
[19] |
WEN C K , SHIH W T , JIN S . Deep learning for massive MIMO CSI feedback[J]. IEEE Wireless Communications Letters, 2018,7(5): 748-751.
|
[20] |
VINCENT P , LAROCHELLE H , BENGIO Y ,et al. Extracting and composing robust features with denoising autoencoders[C]// Proceedings of the 25th International Conference on Machine Learning (ICML). ACM, 2008: 1096-1103.
|
[21] |
LIU L F , OESTGES C , POUTANEN J ,et al. The COST 2100 MIMO channel model[J]. IEEE Wireless Communications, 2012,19(6): 92-99.
|
[22] |
XU K , REN F B . CSVideoNet:a real-time end-to-end learning framework for high-frame-rate video compressive sensing[C]// 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2018: 1680-1688.
|
[23] |
WANG T Q , WEN C K , JIN S ,et al. Deep learning-based CSI feedback approach for time-varying massive MIMO channels[J]. IEEE Wireless Communications Letters, 2019,8(2): 416-419.
|
[24] |
LU C , XU W , SHEN H ,et al. MIMO channel information feedback using deep recurrent network[J]. IEEE Communications Letters, 2019,23(1): 188-191.
|
[25] |
LIU Z Y , ZHANG L , DING Z . Exploiting bi-directional channel reciprocity in deep learning for low rate massive MIMO CSI feedback[J]. IEEE Wireless Communications Letters, 2019,8(3): 889-892.
|
[26] |
WU P X , LIU Z C , CHENG J L . Compressed CSI feedback with learned measurement matrix for mmWave massive MIMO[J]. arXiv:1903.02127, 2019
|
[27] |
LI X Y , WU H M . Spatio-temporal representation with deep neural recurrent network in MIMO CSI feedback[J]. IEEE Wireless Communications Letters, 2020
|
[28] |
CAI Q Y , DONG C , NIU K . Attention model for massive MIMO CSI compression feedback and recovery[C]// 2019 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2019.
|
[29] |
GUO J J , WANG J H , WEN C K ,et al. Compression and acceleration of neural networks for communications[J]. arXiv:1907.13269, 2019
|