Large-scale MIMO system CSI feedback method based on long-short-term attention mechanism

A kind of attention and large-scale technology, applied in the field of CSI feedback of massive MIMO system, can solve the problems of low channel feedback accuracy and complex network model, and achieve the effect of improving representation ability, avoiding iterative calculation, and reducing computational complexity

Active Publication Date: 2020-03-24
ZHONGYUAN ENGINEERING COLLEGE
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Problems solved by technology

[0005] Aiming at the deficiencies in the above-mentioned background technology, the present invention proposes a large-scale MIMO system CSI feedback method based on the long-shor

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[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0046] Such as figure 1 As shown, the embodiment of the present invention provides a large-scale MIMO system CSI feedback method based on the long-short-term attention mechanism. The channel is fed back to the base station, and the base station restores the CSI through decompression. The specific steps are as follows:

[0047] S1. Using literature [Liu L, Poutanen J, The COST2100 channel model proposed by Quitin, et al.The COST 2100MIMOchannel model[J].IEEE...

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Abstract

The invention provides a large-scale MIMO system CSI feedback method based on a long-short-term attention mechanism. The method comprises the following steps: firstly, generating sample data of a space-frequency domain channel matrix by using a COST 2100 channel model, and preprocessing the sample data through discrete Fourier transform and truncation operation to obtain a training set and a testset; secondly, establishing an ALSTM-CsiNet channel state information feedback reconstruction model by utilizing an LSTM and an attention mechanism; inputting the training set into an ALSTM-CsiNet channel state information feedback reconstruction model, and performing iterative training on the model by adopting a mean square error and an adaptive estimation gradient descent algorithm to obtain anoptimized ALSTM-CsiNet model; and finally, directly inputting the test set into the ALSTM-CsiNet model to carry out channel state information reconstruction. According to the invention, the LSTM is used to learn the time correlation of the channel, and the attention mechanism is used to perceive the local information and the automatic weighting feature information, so that the channel feedback precision is improved.

Description

technical field [0001] The invention relates to the technical field of broadband wireless communication, in particular to a CSI feedback method for a massive MIMO system based on a long-short-term attention mechanism. Background technique [0002] In recent years, massive MIMO (multiple-input multiple-out) technology has been recognized by academia and industry as a key technology for next-generation networks. Multiple independent transmission channels greatly improve system energy efficiency, system capacity and system robustness. The potential gain of a massive MIMO system mainly depends on accurate downlink CSI (Channel State Information), and eliminates interference among multiple users through precoding. Currently, in a MIMO system using FDD (frequency division duplexity), downlink channel state information is obtained at the user end and returned to the base station through a feedback link. However, as the number of antennas increases, the overhead of uplink channel ...

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Application Information

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IPC IPC(8): H04B7/06H04B7/0417G06N3/04G06N3/08
CPCH04B7/0626H04B7/0658H04B7/0417G06N3/049G06N3/08G06N3/045
Inventor 张爱华李琪宁冰李春雷贺博鑫
Owner ZHONGYUAN ENGINEERING COLLEGE
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