Variational Bayesian Autoencoder for Channel Compression and Feedback in Massive MIMO Systems

被引:0
作者
Zheng, Xuanyu [1 ,2 ]
Bi, Yuanyuan [1 ]
Guo, Huayan [1 ]
Lau, Vincent [1 ]
机构
[1] Hong Kong Univ Sci & Technol, Dept Elect & Comp Engn, Hong Kong, Peoples R China
[2] Huawei Technol Co Ltd, Labs 2012, Theory Lab, Cent Res Inst, Hong Kong Sci Pk, Hong Kong, Peoples R China
来源
ICC 2023-IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS | 2023年
基金
中国国家自然科学基金;
关键词
Massive MIMO; deep learning; CSI compression; variational Bayesian autoencoder;
D O I
10.1109/ICC45041.2023.10278811
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
In this paper, we propose a Variational Bayesian Autoencoder (VBA)-based channel state information (CSI) compression and feedback scheme for massive multiple-input multiple-output (MIMO) systems. The proposed scheme incorporates the model-assisted knowledge of low-dimensional feedback features and the sparsity of channel to achieve enhanced compression efficiency. We also design a CsiVBA architecture that outputs distributions of the feedback features and the channel at the encoder and decoder, respectively, which facilitates a Bayesian training formulation exploiting the underlying channel sparsity. In addition, we also propose a low-complexity training scheme for new networks of different bit rates, significantly reducing the retraining cost for new compression requirements. Simulation results show that the proposed scheme achieves better rate-distortion trade-offs than the state-of-the-art solutions.
引用
收藏
页码:6349 / 6354
页数:6
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