Enhancing Environment Generalizability for Deep Learning-Based CSI Feedback

TL;DR

This paper introduces EG-CsiNet, a novel deep learning framework designed to improve the environment generalizability of CSI feedback in FDD MIMO systems by addressing distribution shifts across different environments.

Contribution

EG-CsiNet is the first framework to explicitly model and mitigate distribution shifts in CSI feedback, enhancing robustness in unseen environments.

Findings

EG-CsiNet significantly outperforms existing methods in unseen environments.

The framework effectively addresses distribution shifts of multipath and single-path structures.

Robustness is especially improved in challenging single-source environments.

Abstract

Accurate and low-overhead channel state information (CSI) feedback is essential to boost the capacity of frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems. Deep learning-based CSI feedback significantly outperforms conventional approaches. Nevertheless, current deep learning-based CSI feedback algorithms exhibit limited generalizability to unseen environments, which obviously increases the deployment cost. In this paper, we first model the distribution shift of CSI across different environments, which is composed of the distribution shift of multipath structure and a single-path. Then, EG-CsiNet is proposed as a novel CSI feedback learning framework to enhance environment-generalizability. Explicitly, EG-CsiNet comprises the modules of multipath decoupling and fine-grained alignment, which can address the distribution shift of multipath structure and a single path. Based on extensive simulations, the proposed EG-CsiNet can robustly enhance the generalizability in unseen environments compared to the state-of-the-art, especially in challenging conditions with a single source environment.