Channel Estimation for Double-BD-RIS-Assisted Multi-User MIMO Communication

2026-02-20

AI Summary

This work addresses the excessive channel estimation overhead in dual bistatic double-reflective intelligent surface (BD-RIS)-assisted multi-user MIMO systems. It first reveals that the cascaded channel can be decomposed into five low-dimensional matrices and proposes an efficient sequential recovery framework leveraging channel correlations. This approach substantially reduces estimation complexity, bringing the overhead down to the same order as that of conventional diagonal RIS systems while preserving the performance benefits of BD-RIS. Theoretical analysis and simulations demonstrate that the proposed scheme achieves near-Cramér–Rao bound performance under both ideal and noisy conditions, offering a favorable combination of low overhead, high accuracy, and robustness.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Deploying multiple beyond diagonal reconfigurable intelligent surfaces (BD-RISs) can potentially improve the communication performance thanks to inter-element connections of each BD-RIS and inter-surface cooperative beamforming gain among BD-RISs. However, a major issue for multi-BD-RISassisted communication lies in the channel estimation overhead - the channel coefficients associated with the off-diagonal elements in each BD-RIS's scattering matrix as well as those associated with the reflection links among BD-RISs have to be estimated. In this paper, we propose an efficient channel estimation framework for double-BD-RIS-assisted multi-user multipleinput multiple-output (MIMO) systems. Specifically, we reveal that high-dimensional cascaded channels are characterized by five low-dimensional matrices by exploiting channel correlation properties. Based on this novel observation, in the ideal noiseless case, we develop a channel estimation scheme to recover these matrices sequentially and characterize the closed-form overhead required for perfect estimation as a function of the numbers of users and each BD-RIS's elements and channel ranks, which is with the same order as that in double-diagonal-RIS-aided communication systems. This exciting result implies the superiority of cooperative BD-RIS-aided communication over the diagonal- RIS counterpart even when channel estimation overhead is considered. We further extend the proposed scheme to practical noisy scenarios and provide extensive numerical simulations to validate its effectiveness.

Problem

• Channel Estimation
• BD-RIS
• Multi-User MIMO
• RIS-Assisted Communication
• Estimation Overhead

Innovation

• BD-RIS
• channel estimation
• low-dimensional representation
• cooperative beamforming
• MIMO