Leveraging Bi-Directional Channel Reciprocity for Robust Ultra-Low-Rate Implicit CSI Feedback with Deep Learning

TL;DR

This paper introduces Dual-ImRUNet, a deep learning framework that significantly reduces CSI feedback overhead in massive MIMO systems by leveraging bi-directional correlation and robust data alignment, achieving 85% reduction and environmental robustness.

Contribution

The paper presents a novel deep learning-based CSI feedback method with two plug-in modules for ultra-low-rate feedback and improved robustness across diverse environments.

Findings

Achieves 85% reduction in feedback overhead compared to state-of-the-art.

Demonstrates robustness against unseen environmental variations.

Utilizes transformer-based network to exploit channel sparsity.

Abstract

Deep learning-based implicit channel state information (CSI) feedback has been introduced to enhance spectral efficiency in massive MIMO systems. Existing methods often show performance degradation in ultra-low-rate scenarios and inadaptability across diverse environments. In this paper, we propose Dual-ImRUNet, an efficient uplink-assisted deep implicit CSI feedback framework incorporating two novel plug-in preprocessing modules to achieve ultra-low feedback rates while maintaining high environmental robustness. First, a novel bi-directional correlation enhancement module is proposed to strengthen the correlation between uplink and downlink CSI eigenvector matrices. This module projects highly correlated uplink and downlink channel matrices into their respective eigenspaces, effectively reducing redundancy for ultra-low-rate feedback. Second, an innovative input format alignment module is designed to maintain consistent data distributions at both encoder and decoder sides without extra transmission overhead, thereby enhancing robustness against environmental variations. Finally, we develop an efficient transformer-based implicit CSI feedback network to exploit angular-delay domain sparsity and bi-directional correlation for ultra-low-rate CSI compression. Simulation results demonstrate successful reduction of the feedback overhead by 85% compared with the state-of-the-art method and robustness against unseen environments.