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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Veröffentlicht in:IEEE transactions on vehicular technology Jg. 74; H. 1; S. 1730 - 1735
Hauptverfasser: Guo, Yiran, Chen, Wei, Xu, Jialong, Li, Lun, Ai, Bo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.01.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN:0018-9545, 1939-9359
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2024.3452409