Deep Joint CSI Feedback and Multiuser Precoding for MIMO OFDM Systems
Abstract
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 information 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 information. 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.
Cite
@article{arxiv.2404.16289,
title = {Deep Joint CSI Feedback and Multiuser Precoding for MIMO OFDM Systems},
author = {Yiran Guo and Wei Chen and Jialong Xu and Lun Li and Bo Ai},
journal= {arXiv preprint arXiv:2404.16289},
year = {2024}
}