SALDR: Joint Self-Attention Learning and Dense Refine for Massive MIMO CSI Feedback with Multiple Compression Ratio

Xuan Song, Jun Wang, Jie Wang, Guan Gui, Tomoaki Ohtsuki, Haris Gacanin, Hikmet Sari

研究成果: Article査読

4 被引用数 (Scopus)

抄録

The advantages of massive multiple-input multiple-output (MIMO) techniques depend heavily on the accuracy of channel state information (CSI). In frequency division duplexing (FDD) massive MIMO systems, the user equipment (UE) needs to feed downlink CSI back to the base station (BS) through the feedback link. The excessive feedback overheads and low reconstruction accuracy are the main obstacles for actual deployment of FDD massive MIMO systems. In recent years, deep learning (DL) has been widely used to address the above problems. In this letter, we propose a neural network by utilizing the self-attention learning and dense refine (SALDR), which improves the accuracy of CSI feedback. Furthermore, a unified decoder named SALDR-U is designed to realize different compression ratios for CSI feedback without changing any parameter. Simulation results show that the proposed SALDR and SALDR-U outperform the state-of-the-art network in terms of accuracy and overhead of CSI feedback. The source code for all the experiments is available at GitHub.The code of this letter can be downloaded from GitHub link: https://github.com/XS96/SALDR.

本文言語English
論文番号9445070
ページ(範囲)1899-1903
ページ数5
ジャーナルIEEE Wireless Communications Letters
10
9
DOI
出版ステータスPublished - 2021 9月
外部発表はい

ASJC Scopus subject areas

  • 制御およびシステム工学
  • 電子工学および電気工学

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