基于深度展开的大规模MIMO系统CSI反馈算法

doi:10.11959/j.issn.1000-436x.2022237

Abstract

Abstract:

In order to solve the problem that the channel state information (CSI) feedback algorithm based on deep learning in massive MIMO systems at present had too many parameters to be trained and could not be explained well, two CSI feedback algorithms based on depth expansion were proposed.The first one was approximate message delivery (AMP) algorithm based on learnable parameters.The learnable parameters in deep learning were used to replace the threshold value of the threshold function in the AMP algorithm and the parameters of the Onsage correction term.The nonlinear ability of threshold function in dealing with non-strict sparse data was enhanced.The other was the AMP algorithm based on convolutional network, which replaced the threshold function module with the convolutional residual learning module, and used the module to remove the Gaussian random noise generated by each iteration of the AMP algorithm.Simulation results show that the proposed two algorithms have better CSI feedback performance than AMP algorithm, and the AMP algorithm based on convolutional network has better CSI reconstruction performance than the representative method based on deep learning.

Key words: CSI feedback, deep learning, deep unfolding, approximate message passing, learnable parameter, convolutional network

CLC Number:

TN929.5

Yong LIAO, Gang CHENG, Yujie LI. CSI feedback algorithm based on deep unfolding for massive MIMO systems[J]. Journal on Communications, 2022, 43(12): 77-88.

Figures/Tables 14

压缩率	编/解码器		参数量/个
压缩率	编/解码器	CsiNet	CsiNetPlus	LP-AMP-3	CNN-AMP-5
$\frac{1}{4}$	编码器	1 048 612	1 049 264	1 048 576	1 048 576
$\frac{1}{4}$	解码器	1 051 780	1 052 486	1 054 723	1 052 901
$\frac{1}{8}$	编码器	524 324	524 986	524 288	524 288
$\frac{1}{8}$	解码器	527 492	527 548	530 435	528 613
$\frac{1}{16}$	编码器	262 180	262 424	262 144	262 144
$\frac{1}{16}$	解码器	265 384	265 846	268 291	266 469
$\frac{1}{32}$	编码器	131 108	131 342	131 072	131 072
$\frac{1}{32}$	解码器	134 312	134 462	137 219	135 397

压缩率	编/解码器			乘法次数/次
压缩率	编/解码器	AMP	CsiNet	CsiNetPlus	LP-AMP-3	CNN-AMP-5
$\frac{1}{4}$	编码器	1 048 576	1 085 440	1 089 642	1 048 576	1 048 576
$\frac{1}{4}$	解码器	210 917 376	4 329 472	4 338 284	7 270 912	15 960 576
$\frac{1}{8}$	编码器	524 288	561 152	567 254	524 288	524 288
$\frac{1}{8}$	解码器	105 458 688	3 805 184	3 823 128	3 672 320	10 192 128
$\frac{1}{16}$	编码器	262 144	299 008	308 238	262 144	262 144
$\frac{1}{16}$	解码器	52 729 344	3 543 040	3 583 462	1 836 160	7 307 904
$\frac{1}{32}$	编码器	131 072	167 936	179 928	131 072	131 072
$\frac{1}{32}$	解码器	26 364 672	3 411 968	3 472 842	918 080	5 865 792

References 22

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训练轮次	LP-AMP-3	LP-AMP-7	CNN-AMP-3	CNN-AMP-7
10	5.930×10^-5	5.931×10^-5	2.008×10^-5	1.015×10^-5
30	5.929×10^-5	5.716×10^-5	1.271×10^-5	7.029×10^-6
50	5.928×10^-5	5.599×10^-5	1.162×10^-5	5.993×10^-6
90	5.933×10^-5	5.580×10^-5	1.112×10^-5	5.309×10^-6
110	5.936×10^-5	5.553×10^-5	1.089×10^-5	5.032×10^-6
200	5.933×10^-5	5.561×10^-5	1.078×10^-5	5.118×10^-6
300	5.931×10^-5	5.559×10^-5	1.074×10^-5	5.125×10^-6

仿真参数	设置
双工模式	FDD
发射天线数	32
接收天线数	1
子载波数	1 024
信道模型	COST2100
调制方式	正交相移键控
室内场景频段/GHz	5.3
室外场景频段/MHz	300
系统带宽/MHz	20

CSI feedback algorithm based on deep unfolding for massive MIMO systems

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