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

Xiaokun Tuo, Ming-Min Zhao, Xiang Wang, Changsheng You, Min-Jian Zhao

TL;DR

This work tackles non-stationary cascaded channel estimation in RIS-assisted mmWave systems under hybrid-field propagation with visible-region non-stationarity. It introduces a reduced-dimensional sparse bilinear representation that compresses the coupled near-field/far-field dictionaries via a visibility matrix, and develops a turbo-structured joint Bayesian estimator (TS-JBE) to simultaneously infer channel gains, VRs, and off-grid parameters. The proposed method leverages a 3LHS prior and Markov VR modeling to achieve joint, iterative inference with limited error propagation, outperforming competing approaches in NMSE across varying SNRs, pilot counts, and VR conditions. This approach offers robust, scalable channel estimation for large RIS deployments, enabling more reliable RIS-aided 6G mmWave communications.

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.

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

TL;DR

This work tackles non-stationary cascaded channel estimation in RIS-assisted mmWave systems under hybrid-field propagation with visible-region non-stationarity. It introduces a reduced-dimensional sparse bilinear representation that compresses the coupled near-field/far-field dictionaries via a visibility matrix, and develops a turbo-structured joint Bayesian estimator (TS-JBE) to simultaneously infer channel gains, VRs, and off-grid parameters. The proposed method leverages a 3LHS prior and Markov VR modeling to achieve joint, iterative inference with limited error propagation, outperforming competing approaches in NMSE across varying SNRs, pilot counts, and VR conditions. This approach offers robust, scalable channel estimation for large RIS deployments, enabling more reliable RIS-aided 6G mmWave communications.

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.
Paper Structure (30 sections, 1 theorem, 61 equations, 8 figures, 1 algorithm)

This paper contains 30 sections, 1 theorem, 61 equations, 8 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Channel Model
  4. User-RIS Channel
  5. RIS-BS Channel
  6. Hybrid-field Cascaded Channel
  7. Sparse Channel Representation and Prior Modeling
  8. Sparse Representation and Model Transformation
  9. Sparse Representation
  10. Model Transformation
  11. Off-Grid Effect Correction
  12. Hierarchical Sparse Prior Model
  13. Markov Stationary Clustering Prior Model
  14. Joint Probabilistic Model of the Received Signal
  15. Turbo-Structured Joint Bayesian Estimation Algorithm
  16. ...and 15 more sections

Key Result

Proposition 1

The row-wise Khatri-Rao product between the near-field dictionary considering VR indicator $\bar{\mathbf{W}}$ and the conjugate of the far-field dictionary $\mathbf{F}_N^\ast$, i.e., $\bar{\mathbf{W}} \ast \mathbf{F}_N^{\ast}$ is equivalent to the Hadamard product between a near-field dictionary $\m where $\Leftrightarrow$ denotes that the cascaded channel $\overset{\circ}{\mathbf{h}}$ preserves t

Figures (8)

  • Figure 1: Considered RIS-aided mmWave system model.
  • Figure 2: Joint probabilistic model of the received signal.
  • Figure 3: Flow chart of the proposed algorithm.
  • Figure 4: Factor graph of the subarray VR for the $q$-th column.
  • Figure 5: Convergence behavior of the considered algorithms
  • ...and 3 more figures

Theorems & Definitions (3)

  • Proposition 1
  • proof
  • proof