基于深度学习的大规模MIMO信道状态信息反馈

doi:10.11959/j.issn.2096-3750.2020.00157

Abstract

Abstract:

The massive multiple-input multiple-output (MIMO) technology is considered to be one of the core technologies of the next generation communication system.To fully utilize the potential gains of MIMO systems,the base station should accurately acquire the channel state information (CSI).Due to the significant increase in the number of antennas,the traditional feedback schemes based on the codebook or vector quantization are faced with great technical challenges.Recently,deep learning (DL) has provided a new idea for solving CSI feedback problems in massive MIMO systems.It was focused on the key technologies of the CSI feedback for massive MIMO systems.Firstly,the background and significance of the CSI feedback were expounded.Then,a model for the massive MIMO system was established and the sparse nature of CSI was analyzed.Several schemes of introducing DL into the CSI feedback mechanism were introduced and compared in detail.Finally,a further prospect on the development trend of the CSI feedback based on DL was made.

Key words: massive MIMO, deep learning, CSI feedback

CLC Number:

TN929.5

Muhan CHEN,Jiajia GUO,Xiao LI,Shi JIN. An overview of the CSI feedback based on deep learning for massive MIMO systems[J]. Chinese Journal on Internet of Things, 2020, 4(1): 33-44.

Figures/Tables 12

References 29

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场景 γ	LASSO	TVAL3	BM3D-AMP	CsiNet
1/4	-7.59	-14.87	-4.33	-17.36
室内1/16	-2.72	-2.61	0.26	-8.65
1/32	-1.03	-0.27	24.72	-6.24
1/64	-0.14	0.63	26.22	-5.84
1/4	-5.08	-6.90	-1.33	-8.75
室外1/16	-1.01	-0.43	0.55	-4.51
1/32	-0.24	0.46	22.66	-2.81
1/64	-0.06	0.76	25.45	-1.93

场景		γ	LASSO	TVAL3	BM3D-AMP	CsiNet	CsiNet-LSTM
		1/16	-2.96	-3.20	0.25	-10.59	-23.06
室内	NMSE/dB	1/32	-1.18	-0.46	20.85	-7.35	-22.33
		1/64	-0.18	0.60	26.66	-6.09	-21.24
		1/16	0.247 1	0.314 8	0.345 4	0.000 1	0.000 3
	运行时间/s	1/32	0.213 7	0.314 8	0.555 6	0.000 1	0.000 3
		1/64	0.247 9	0.286 0	0.604 7	0.000 1	0.000 3
		1/16	-1.09	-0.53	0.40	-3.60	-9.86
室外	NMSE/dB	1/32	-0.27	0.42	18.99	-2.14	-9.18
		1/64	-0.06	0.74	24.42	-1.65	-8.83
		1/16	0.212 2	0.314 5	0.421 0	0.000 1	0.000 3
	运行时间/s	1/32	0.240 9	0.298 5	0.603 1	0.000 1	0.000 3
		1/64	0.016 6	0.285 0	0.598 0	0.000 1	0.000 3

方法	γ
方法	1/16	1/32	1/64
CsiNet	530 656	268 448	137 344
CsiNet-LSTM	102 009 892	101 354 404	101 026 660
RecCsiNet	19 478 584	18 118 392	17 450 584
PR-RecCsiNet	793 144	333 816	153 304

测量矩阵	m=20	m=25	m=30	m=35	m=40
Φ?	95.90%	98.70%	99.60%	100%	100%
F	0.90%	7.85%	89.20%	99.80%	99.75%
S	5.30%	30.15%	72.70%	90.00%	98.45%
B	5.90%	26.80%	63.10%	87.70%	99.10%
G	2.15%	13.45%	58.50%	84.75%	97.70%
P	0	0	0.45%	1.00%	6.85%

An overview of the CSI feedback based on deep learning for massive MIMO systems

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