Self-attention mechanism-based CSI eigenvector feedback for massive MIMO

Self-attention mechanism-based CSI eigenvector feedback for massive MIMO

Massive multiple-input multiple-output (MIMO) system can provide satisfying gain of spectrum efficiency for 5G and future wireless communication systems.In frequency-division duplex (FDD) mode, downlink channel state information (CSI) needs to be accurately fed back to the base station side to obtai...

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Bibliographic Details
Main Authors: Bei YANG, Xin LIANG, Hang YIN, Zheng JIANG, Xiaoming SHE
Format: Article
Language:zho
Published: Beijing Xintong Media Co., Ltd 2023-11-01
Series:Dianxin kexue
Subjects:
channel feedback
massive MIMO
self-attention mechanism
deep learning
Online Access:http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023247/
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Summary:Massive multiple-input multiple-output (MIMO) system can provide satisfying gain of spectrum efficiency for 5G and future wireless communication systems.In frequency-division duplex (FDD) mode, downlink channel state information (CSI) needs to be accurately fed back to the base station side to obtain this gain.To improve the feedback accuracy of downlink CSI eigenvector, a self-attention mechanism-based CSI feedback method named SA-CsiNet was proposed.SA-CsiNet respectively deployed self-attention modules at the encoder and the decoder to achieve feature extraction and reconstruction of CSI.Experimental results show that compared with codebook-based and conventional deep learning-based CSI feedback approaches, SA-CsiNet provides higher reconstruction accuracy of CSI.
ISSN:1000-0801

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