Deep Joint CSI Feedback and Multiuser Precoding for MIMO OFDM Systems

TL;DR

This paper introduces a deep learning approach for joint CSI feedback and multiuser precoding in MIMO OFDM systems, significantly improving downlink sum-rate performance especially under low SNR and feedback constraints.

Contribution

It presents a novel end-to-end deep learning framework that compresses CSI feedback and optimizes multiuser precoding and power allocation simultaneously.

Findings

Enhanced downlink sum-rate in low SNR scenarios.

Improved feedback resilience through deep joint source-channel coding.

Significant performance gains with reduced feedback overhead.

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.