Residual Cross-Attention Transformer-Based Multi-User CSI Feedback With Deep Joint Source-Channel Coding

This letter proposes a deep-learning (DL)-based multi-user channel state information (CSI) feedback framework for massive multiple-input multiple-output systems, where the deep joint source-channel coding (DJSCC) is utilized to improve the CSI reconstruction accuracy. Specifically, we design a multi...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE wireless communications letters Vol. 14; no. 8; pp. 2481 - 2485
Main Authors:	Zhang, Hengwei, Wu, Minghui, Qiao, Li, Liu, Ling, Han, Ziqi, Gao, Zhen
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.08.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Coding Correlation Decoding deep joint source-channel coding (DJSCC) Delays Downlink Encoding Feature extraction Feedback massive multiple-input multiple-output (MIMO) multi-user CSI feedback Noise levels Residual cross-attention Training Transformers Uplink Vectors
ISSN:	2162-2337, 2162-2345
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!