一种基于深度学习的物联网信道状态信息获取算法

doi:10.11959/j.issn.2096-3750.2019.00085

Abstract

Abstract:

To solve the problem of high feedback overhead when the user sends channel state information (CSI) to the base station in massive multiple input multiple output (MIMO) based on Internet of things system,a CSI feedback network based on deep learning was proposed to feedback CSI.Firstly,the proposed network used convolutional neural network (CNN) to extract channel feature vectors and maxpooling to compress the data.Then the compressed CSI was decompressed by using full connection and CNN to restore the original channel.The simulation results show that compared with the existing CSI feedback methods,the CSI recovered by the proposed CSI feedback network is closer to the original channel,and the reconstruction quality is improved significantly.

Key words: massive MIMO, Internet of things, CSI feedback, deep learning

CLC Number:

TN929.5

Yong LIAO,Haimei YAO,Yuanxiao HUA. Channel state information acquisition algorithm based on deep learning for IoT[J]. Chinese Journal on Internet of Things, 2019, 3(1): 8-13.

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References 12

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仿真参数	数值设置
发射天线数	M=32/64
接收天线数	1
用户数	8
双工模式	FDD
信道模型	单环MIMO信道^[12]
工作频率	2.6 GHz
天线间距	0.5 λ

数据压缩率	发射天线数/根	PCA	DCT	本文所提方法
1/4	32	-11.22	-10.85	-18.32
	64	-11.01	-10.68	-18.08
1/8	32	-8.13	-7.97	-16.21
	64	-7.89	-7.63	-15.96

方法	M=32运行时间/s	M=64运行时间/s
DCT	0.012 401	0.017 579
PCA	0.013 687	0.018 010
本文所提方法	0.001 040	0.001 512

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Channel state information acquisition algorithm based on deep learning for IoT

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