Menglei Sheng | Searcharxiv

arXiv2026

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

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.

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arXiv2025

Double Low-Rank 4D Tensor Decomposition for Circular RIS-Aided mmWave MIMO-NOMA System Channel Estimation in Mobility Scenarios

Channel estimation is not only essential to highly reliable data transmission and massive device access but also an important component of the integrated sensing and communication (ISAC) in the sixth-generation (6G) mobile communication systems. In this paper, we consider a downlink channel estimation problem for circular reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) system in mobility scenarios. First, we propose a subframe partitioning scheme to facilitate the modeling of the received signal as a fourth-order tensor satisfying a canonical polyadic decomposition (CPD) form, thereby formulating the channel estimation problem as tensor decomposition and parameter extraction problems. Then, by exploiting both the global and local low-rank properties of the received signal, we propose a double low-rank 4D tensor decomposition model to decompose the received signal into four factor matrices, which is efficiently solved via alternating direction method of multipliers (ADMM). Subsequently, we propose a two-stage parameter estimation method based on the Jacobi-Anger expansion and the special structure of circular RIS to uniquely decouple the angle parameters. Furthermore, the time delay, Doppler shift, and channel gain parameters can also be estimated without ambiguities, and their estimation accuracy can be efficiently improved, especially at low signal-to-noise ratio (SNR). Finally, a concise closed-form expression for the Cram\'er-Rao bound (CRB) is derived as a performance benchmark. Numerical experiments are conducted to demonstrate the effectiveness of the proposed method compared with the other discussed methods.

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