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

Research output: Contribution to journalArticlepeer-review

Abstract

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 paper, 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 GitHubThe code of this paper can be downloaded from GitHub link: https://github.com/XS96/SALDR.

Original languageEnglish
JournalIEEE Wireless Communications Letters
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • CSI feedback
  • Computer architecture
  • Correlation
  • Downlink
  • Feature extraction
  • Kernel
  • Massive MIMO
  • Massive MIMO
  • Wireless communication
  • frequency division duplex
  • multiple compression ratio.
  • self-attention learning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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