Near-Field Channel Estimation for Extremely Large-Scale Circular RIS-Aided mmWave MIMO-NOMA System with Beam Squint Effect

TL;DR

This paper proposes a novel near-field channel estimation method for large-scale circular RIS-assisted mmWave MIMO-NOMA systems, addressing beam squint effects with tensor modeling and a multi-stage framework.

Contribution

It introduces an extremely large-scale circular RIS architecture and a tensor-based multi-stage channel estimation framework for near-field mmWave systems.

Findings

The proposed method effectively estimates channels under beam squint effects.

Numerical results show improved accuracy over existing methods.

Theoretical CRB benchmark validates the estimation performance.

Abstract

Near-field channel estimation under beam squint effect is critical to future 6G millimeter-wave (mmWave) systems equipped with reconfigurable intelligent surfaces (RIS). In this paper, firstly, we design an extremely large-scale circular RIS (XL-CRIS) architecture to construct an angle-invariant near-field region for MIMO-NOMA system, which can maintain a constant effective aperture, allowing for a unified channel modeling framework. Then, to enable efficient parameter extraction, we model the received wideband MIMO-NOMA signal as a third-order tensor which is used to develop a multi-stage channel estimation framework. Accordingly, we decompose the multi-variable problem into several low-dimensional sub-problems, while naturally preserving path-wise parameter pairing through the shared permutation matrix. Finally, we derive a vector-form CRB as a theoretical performance benchmark. To illustrate the effectiveness of the proposed method, numerical experiments are carried out and compared with the discussed methods.