CSI feedback method for dynamically adjusting compression rate based on model pruning

被引：0
|
作者：
Shao K. [1 ,2 ,3 ]
Du Z. [1 ]
Wang G. [1 ,3 ]
机构：
[1] School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing
[2] Engineering Research Center of Mobile Communications of the Ministry of Education, Chongqing
[3] Chongqing Key Laboratory of Mobile Communications Technology, Chongqing
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2023年 / 45卷 / 08期
关键词：
channel attention; channel state information; deep learning; model pruning;
D O I：
10.12305/j.issn.1001-506X.2023.08.37
中图分类号：
TB18 [人体工程学]; Q98 [人类学];
学科分类号：
030303 ; 1201 ;
摘要：
Aiming at the problem that the existing channel state information (CSI) feedback scheme based on deep learning (DL) can only perform compression feedback at a fixed compression rate (CR), a CSI feedback scheme for dynamically adjusting CR based on model pruning is proposed. Firstly, a residual channel characteristic attention (RCCA) mechanism is designed, and a CSI feedback network RCCA-Net is built, which makes full use of the amplitude-phase dependence relationship between the real parts and imaginary parts of the complex channel matrix to further improve CSI feedback-reconstruction quality. Secondly, the model RCCA-Prune is designed to dynamically adjust the CR, which uses the ℓ2norm to evaluate the contribution of each neuron to the final result, and deletes the neurons with less contribution through the model structured pruning technology to realize the dynamic adjustment of the compression rate. The simulation results show that the proposed dynamic adjustment CR scheme has better feedback performance under different CR, and has good generalization in different test environments. © 2023 Chinese Institute of Electronics. All rights reserved.
引用
收藏
页码：2615 / 2622
页数：7
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