Generalizable Learning for Frequency-Domain Channel Extrapolation under Distribution Shift

TL;DR

This paper introduces a physics-inspired, environment-agnostic deep learning approach for frequency-domain channel extrapolation in massive MIMO systems, effectively handling distribution shifts across different wireless environments.

Contribution

It proposes a novel distribution alignment strategy based on physics principles, enabling deep learning models to generalize to unseen environments for channel extrapolation.

Findings

Reduces extrapolation error by over 6 dB in unseen environments.

Addresses distribution shift of multipath structure and single-path response.

Demonstrates improved robustness over state-of-the-art methods.

Abstract

Frequency-domain channel extrapolation is effective in reducing pilot overhead for massive multiple-input multiple-output (MIMO) systems. Recently, Deep learning (DL) based channel extrapolator has become a promising candidate for modeling complex frequency-domain dependency. Nevertheless, current DL extrapolators fail to operate in unseen environments under distribution shift, which poses challenges for large-scale deployment. In this paper, environment generalizable learning for channel extrapolation is achieved by realizing distribution alignment from a physics perspective. Firstly, the distribution shift of wireless channels is rigorously analyzed, which comprises the distribution shift of multipath structure and single-path response. Secondly, a physics-based progressive distribution alignment strategy is proposed to address the distribution shift, which includes successive path-oriented design and path alignment. Path-oriented DL extrapolator decomposes multipath channel extrapolation into parallel extrapolations of the extracted path, which can mitigate the distribution shift of multipath structure. Path alignment is proposed to address the distribution shift of single-path response in path-oriented DL extrapolators, which eventually enables generalizable learning for channel extrapolation. In the simulation, distinct wireless environments are generated using the precise ray-tracing tool. Based on extensive evaluations, the proposed path-oriented DL extrapolator with path alignment can reduce extrapolation error by more than 6 dB in unseen environments compared to the state-of-the-arts.