Hybrid-Field Sparse Channel Representation and Recovery for XL-RIS-Assisted mmWave MIMO Systems

2026-07-26

AI Summary

This work addresses the challenges of high-dimensional cascaded modeling and excessive computational and storage overhead in hybrid near- and far-field channel estimation for extra-large reconfigurable intelligent surface (XL-RIS)-assisted millimeter-wave MIMO systems. To tackle these issues, a two-timescale sparse recovery framework is proposed. Leveraging the quasi-static nature of the base station–RIS channel, a Dirichlet kernel-based off-grid dictionary compression (DK-ODC) mechanism is devised to effectively reduce dimensionality and mitigate angle-off-grid errors. For the dynamic user-side channel, a subspace-aware incremental variational Bayesian learning (SI-VBL) algorithm is introduced, enabling efficient sparse recovery driven by low-dimensional subspaces. The proposed approach significantly reduces computational complexity and memory requirements while maintaining high estimation accuracy, thereby achieving an effective balance among performance, efficiency, and resource consumption.

Abstract

Extremely large-scale reconfigurable intelligent surface (XL-RIS)-assisted communication is regarded as a key enabling technology for future 6G networks. However, hybrid-field channel estimation for XL-RIS-assisted systems is challenging due to the high-dimensional cascaded channel and the coexistence of far-field and near-field propagation. In this case, traditional full-dimensional sparse recovery methods require a large cascaded dictionary and suffer from severe computational and storage burdens. To address these challenges, we develop a double-timescale channel estimation framework that decouples sparse dictionary representation and recovery. Then, by exploiting the quasi-static property of the channel at the base station (BS) and RIS side, we propose a Dirichlet kernel-based off-grid dictionary compression (DK-ODC) scheme for sparse representation, which reduces the dimension of the corresponding dictionary as well as mitigates BS-side angular off-grid error. Furthermore, for the dynamic channel at the user equipment (UE) and RIS side, we propose a subspace-aware incremental variational Bayesian learning (SI-VBL) algorithm, which enables incremental learning of sparse channels by exploiting the identified low-dimensional subspace and pruning threshold. Analysis and simulation results confirm that the proposed framework avoids full-dimensional Bayesian recovery and achieves a favorable tradeoff among estimation accuracy, computational complexity, and storage overhead.

Problem

• XL-RIS
• hybrid-field channel estimation
• sparse recovery
• mmWave MIMO
• off-grid error

Innovation

• XL-RIS
• hybrid-field channel estimation
• off-grid dictionary compression
• incremental variational Bayesian learning
• double-timescale framework