Deep Joint CSI Feedback and Multiuser Precoding for MIMO OFDM Systems

The design of precoding plays a crucial role in achieving a high downlink sum rate in multiuser multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. In this correspondence, we propose a deep learning based joint CSI feedback and multiuser precoding method...

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Vydáno v:IEEE transactions on vehicular technology Ročník 74; číslo 1; s. 1730 - 1735
Hlavní autoři: Guo, Yiran, Chen, Wei, Xu, Jialong, Li, Lun, Ai, Bo
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.01.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
CSI feedback
deep joint source-channel coding
Downlink
Downlinking
Eigenvalues and eigenfunctions
Eigenvectors
Feedback
Feedback control systems
Frequency division duplexing
MIMO communication
Modules
OFDM
Orthogonal Frequency Division Multiplexing
power allocation
Precoding
Signal to noise ratio
Task analysis
Uplink
Vectors
ISSN:0018-9545, 1939-9359
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Shrnutí:The design of precoding plays a crucial role in achieving a high downlink sum rate in multiuser multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. In this correspondence, we propose a deep learning based joint CSI feedback and multiuser precoding method in frequency division duplex systems, aiming at maximizing the downlink sum-rate performance in an end-to-end manner. Specifically, the eigenvectors of the CSI matrix are compressed using deep joint source-channel coding techniques. This compression method enhances the resilience of the feedback CSI against degradation in the feedback channel. A joint multiuser precoding module and a power allocation module are designed to adjust the precoding direction and the precoding power for users based on the feedback CSI. Experimental results demonstrate that the downlink sum-rate can be significantly improved by using the proposed method, especially in scenarios with low signal-to-noise ratio and low feedback overhead.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2024.3452409