DUGC-VRNet: Joint VR Recognition and Channel Estimation for Spatially Non-Stationary XL-MIMO

TL;DR

This paper introduces DUGC-VRNet, a novel deep learning framework combining deep unfolding and graph convolution to jointly recognize visibility regions and estimate channels in XL-MIMO systems with spatial non-stationarity.

Contribution

The paper proposes a new joint VR recognition and channel estimation network that leverages graph structures and deep unfolding, improving accuracy and efficiency in XL-MIMO systems.

Findings

DUGC-VRNet outperforms existing methods in channel estimation accuracy.

The pruned DUGC-VRNet maintains high performance with reduced complexity.

Simulation results confirm superior VR recognition in non-stationary environments.

Abstract

In this letter, we address spatially non-stationary near-field channel estimation for extremely large-scale multiple-input multiple-output (XL-MIMO) systems with a hybrid combining architecture. One key challenge in the considered problem lies in that conventional channel estimation algorithms typically struggle to effectively identify and adapt to the partial antenna visibility caused by varying visibility regions (VRs), thereby compromising estimation accuracy. To perform joint VR recognition and channel estimation, we integrate a deep unfolding network (DUN) with a graph convolution network (GCN), leading to a Deep Unfolding and Graph Convolution coupled, Visibility Region Aware Network (DUGC-VRNet). By leveraging the channel's graph structure, the GCN infers and feeds back VR information to dynamically guide the DUN's updates, thereby enhancing reliable channel estimation under spatial non-stationarity. To reduce DUGC-VRNet's complexity, we apply weight pruning to obtain a lightweight network. Simulation results demonstrate that the DUGC-VRNet and its pruned variant achieve superior channel estimation and more accurate VR recognition under spatially non-stationary conditions.