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

2026-02-06

AI Summary

This work addresses the challenge of joint channel and visible-region estimation in reconfigurable intelligent surface (RIS)-assisted millimeter-wave communications, where spatial non-stationarity—arising from mixed far-field and near-field propagation along with random blockages—renders conventional channel models ineffective. Focusing on a hybrid scenario wherein the base station–RIS link operates in the far field while the user–RIS link resides in the near field with partial visibility, the authors propose a reduced-dimension sparse bilinear model. This model leverages a polar-domain dictionary with visibility-aware weighting to compress the parameter space and introduces a Turbo-structured joint Bayesian inference algorithm that simultaneously recovers off-grid channel gains, visible regions, and angular parameters, thereby circumventing error propagation inherent in sequential estimation. Simulations demonstrate that the proposed approach significantly enhances joint estimation accuracy over existing methods.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Multi-instance/Multi-view Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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.

Problem

• RIS-assisted communications
• hybrid-field propagation
• visible regions
• non-stationary channel
• channel estimation

Innovation

• Hybrid-field propagation
• Visible region estimation
• Sparse bilinear representation
• Dictionary compression
• Turbo-structured joint Bayesian estimation