Hybrid-Field Joint Channel and Visible Region Estimation for RIS-Assisted Communications

TL;DR

This paper introduces a novel joint channel estimation method for RIS-assisted mmWave systems that accounts for hybrid-field effects and partial visibility, significantly improving accuracy in complex propagation environments.

Contribution

It develops a reduced-dimensional sparse bilinear model and a turbo-structured Bayesian estimator to effectively estimate channels and visible regions in hybrid-field RIS scenarios.

Findings

Enhanced estimation accuracy over existing methods

Effective handling of near-field and far-field propagation coexistence

Reduced computational complexity through dictionary compression

Abstract

In reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) communication systems, the large-scale RIS introduces pronounced geometric effects that lead to the coexistence of far-field and near-field propagation. Furthermore, random blockages induce spatial non-stationarity across the RIS array, causing signals from different scatterers to illuminate only partial regions, referred to as visible regions (VRs). This renders conventional far-field and fully visible array-based channel models inadequate and makes channel estimation particularly challenging. In this paper, we investigate the non-stationary cascaded channel estimation problem in a hybrid-field propagation environment, where the RIS-base station (BS) link operates in the far-field, while the user-RIS link exhibits near-field characteristics with partial visibility. To address the resulting high-dimensional and coupled estimation problem, a reduced-dimensional sparse bilinear representation is developed by exploiting the structural characteristics of the cascaded channel. In particular, a dictionary compression technique is proposed to represent the high-dimensional coupled dictionary using a low-dimensional polar-domain dictionary weighted by a visibility matrix, thereby significantly reducing the problem scale. Based on this representation, a turbo-structured joint Bayesian estimation (TS-JBE) approach is proposed to simultaneously estimate the channel gains, VRs, and off-grid parameters, thereby avoiding error propagation inherent in existing sequential methods. Simulation results demonstrate that the proposed method significantly improves the estimation accuracy compared with existing approaches.