Problem
Research questions and friction points this paper is trying to address.
Near-field
Channel Estimation
XL-RIS
mmWave MIMO
Spherical-wave
Near-field Channel Estimation for XL-RIS-aided mmWave MIMO Systems
🤖 AI Summary
This work addresses the challenge of channel estimation in millimeter-wave multi-user MIMO systems with extra-large reconfigurable intelligent surfaces (XL-RISs), where near-field effects invalidate the conventional far-field planar wave assumption and angular-domain sparsity. To overcome this, a low-overhead two-stage cascaded channel estimation scheme is proposed. The method first decomposes the shared base station–RIS link by virtually treating each user as a single-antenna entity, then leverages spherical wave modeling for near-field propagation, polar-domain sparse recovery, and alternating least squares refinement to exploit the joint sparsity of RIS–user channels in the polar coordinate system. This approach achieves efficient channel estimation in mixed far- and near-field scenarios while significantly reducing pilot overhead, outperforming existing near-field benchmark schemes as demonstrated by simulation results.
📝 Abstract
Extremely large-scale reconfigurable intelligent surfaces (XL-RISs) have emerged as a promising technology for millimeter-wave (mmWave) communications. However, the exceedingly large aperture of XL-RISs renders the RIS-user links likely to operate in the near-field region, where the conventional planar-wave assumption and angular-domain sparse representation become invalid, thus making channel estimation significantly more challenging. In this paper, we investigate cascaded channel estimation for an XL-RIS-aided multi-user multiple-input multiple-output (MU-MIMO) system, in which the BS-RIS channel is modeled in the far field, while the RIS-user channels exhibit near-field spherical-wave characteristics. To tackle the resulting hybrid-field estimation problem, we propose a low-overhead two-stage channel estimation scheme by jointly exploiting the common BS-RIS link shared by all users and the polar-domain sparsity of the RIS-user channels. Specifically, the multi-antenna users are firstly decomposed into multiple virtual single-antenna users, based on which the common BS-RIS parameters are extracted from a typical virtual user and the RIS-user channels are initialized via compensated polar-domain sparse recovery. Then, an alternating least-squares refinement procedure is developed to jointly improve the common BS-RIS operator and the user-specific RIS-side channels. Simulation results show that the proposed scheme achieves competitive channel estimation performance with substantially reduced pilot overhead compared with the existing near-field benchmarks.
Innovation
Methods, ideas, or system contributions that make the work stand out.
near-field channel estimation
XL-RIS
polar-domain sparsity
hybrid-field modeling
alternating least squares
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