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...

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Vydané v:	IEEE wireless communications letters Ročník 14; číslo 8; s. 2481 - 2485
Hlavní autori:	Zhang, Hengwei, Wu, Minghui, Qiao, Li, Liu, Ling, Han, Ziqi, Gao, Zhen
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.08.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
On-line prístup:	Získať plný text
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