1 |
LARSSON E G, EDFORS O, TUFVESSON F, et al. Massive MIMO for next generation wireless systems[J]. IEEE Communications Magazine, 2014, 52(2): 186‑195.
|
2 |
BARRIAC G, MADHOW U. Space‑time communication for OFDM with implicit channel feedback[J]. IEEE Transactions on Information Theory, 2004, 50(12): 3111‑3129.
|
3 |
RAO X, LAU V K N. Distributed compressive CSIT estimation and feedback for FDD multi‑user massive MIMO systems[J]. IEEE Transactions on Signal Processing, 2014, 62(12): 3261‑3271.
|
4 |
CHOI J, CHANCE Z, LOVE D J, et al. Noncoherent trellis coded quantization: a practical limited feedback technique for massive MIMO systems[J]. IEEE Transactions on Communications, 2013, 61(12): 5016‑5029.
|
5 |
MIRZA J, SHAFI M, SMITH P J, et al. Limited feedback massive MISO systems with trellis coded quantization for correlated channels[J]. IEEE Transactions on Vehicular Technology, 2016, 65(10): 8240‑8254.
|
6 |
DAI L, WANG Z, YANG Z. Spectrally efficient time‑frequency training OFDM for mobile large‑scale MIMO systems[J]. IEEE Journal on Selected Areas in Communications, 2013, 31(2): 251‑263.
|
7 |
RAGHAVAN V, SAYEED A M. Sublinear capacity scaling laws for sparse MIMO channels[J]. IEEE Transactions on Information Theory, 2010, 57(1): 345‑364.
|
8 |
DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289‑1306.
|
9 |
WEN C K, SHIH W T, JIN S. Deep learning for massive MIMO CSI feedback[J]. IEEE Wireless Communications Letters, 2017, 7(5): 748‑751.
|
10 |
廖勇, 姚海梅, 花远肖, 等. 一种基于深度学习的FDD大规模MIMO系统CSI反馈方法[J]. 电子学报, 2020, 48(6): 1182‑1189.
|
|
LIAO Yong, YAO Hai‑mei, HUA Yuan‑xiao, et al. CSI feedback method based on deep learning for FDD massive MIMO systems[J]. Acta Electronica Sinica, 2020, 48(6): 1182‑1189. (in Chinese)
|
11 |
GUO J, WEN C K, JIN S, et al. Convolutional neural network‑based multiple‑rate compressive sensing for massive MIMO CSI feedback: design, simulation, and analysis[J]. IEEE Transactions on Wireless Communications, 2020, 19(4): 2827‑2840.
|
12 |
LIANG P, FAN J, SHEN W, et al. Deep learning and compressive sensing‑based CSI feedback in FDD massive MIMO systems[J]. IEEE Transactions on Vehicular Technology, 2020, 69(8): 9217‑9222.
|
13 |
FRANOIS C. Xception: Deep learning with depthwise separable convolutions[C]//IEEE International Conference on Computer Vision and Pattern Recognition. Piscataway, New Jersey, USA: IEEE, 2017: 1251‑1258.
|
14 |
HE K M. Deep residual learning for image recognition[C]//ZHANG X Y. IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, New Jersey, USA: IEEE, 2016: 770‑778.
|
15 |
LIU L. The COST 2100 MIMO channel model[J]. IEEE Wireless Communications, 2012, 19(6): 92‑99.
|
16 |
LI C, YIN W, ZHANG Y. User's guide for TVAL3: TV minimization by augmented lagrangian and alternating direction algorithms[R]. Houston: Department of Computational and Applied Mathematics, Rice University, 2009: 46‑47.
|
17 |
NOURI N. A compressed CSI estimation approach for FDD massive MIMO systems[C]//AZUZUPOUR M J. Iranian Conference on Electrical Engineering. Piscataway, New Jersey, USA: IEEE, 2020: 1‑6.
|