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

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.