Structured Tensor Decomposition Based Channel Estimation and Double Refinements for Active RIS Empowered Broadband Systems

2024-11-25

arXiv.org

AI Summary

This work addresses high-accuracy channel parameter estimation for broadband multi-antenna systems empowered by active reconfigurable intelligent surfaces (RIS) in strongly reverberant environments with both dominant line-of-sight and rich multipath components. Method: We propose a novel tensor-domain estimation framework: first, we formulate a fifth-order Vandermonde-structured CP tensor model, circumventing conventional Kruskal uniqueness constraints; second, we design a three-stage closed-form algorithm enabling coarse estimation followed by two optional refinement stages, achieving parameter decoupling and one-dimensional algebraic solution via linear algebra. Contribution/Results: We derive a structured Cramér–Rao lower bound (CRLB) incorporating active RIS noise covariance. Experiments demonstrate that the proposed method significantly outperforms passive RIS-based schemes and achieves estimation accuracy approaching the newly derived CRLB, validating its effectiveness and superiority in complex broadband channels.

Technology Category

• Machine Learning: Matrix & Tensor Methods
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Channel parameter recovery is critical for the next-generation reconfigurable intelligent surface (RIS)-empowered communications and sensing. Tensor-based mechanisms are particularly effective, inherently capturing the multi-dimensional nature of wireless channels. However, existing studies assume either a line-of-sight (LOS) scenario or a blocked TX-RX channel. This paper solves a novel problem: tensor-based channel parameter estimation for active RIS-aided multiple-antenna broadband connections in fully multipath environments with the TX-RX link. System settings are customized to construct a fifth-order canonical polyadic (CP) signal tensor that matches the five-dimensional channel. Four tensor factors contain redundant columns, rendering the classical Kruskal's condition for decomposition uniqueness unsatisfied. The fifth-order Vandermonde structured CP decomposition (VSCPD) is developed to address this challenge, making the tensor factorization problem solvable using only linear algebra and offering a relaxed general uniqueness condition. With VSCPD as a perfect decoupling scheme, a sequential triple-stage channel estimation algorithm is proposed based on one-dimensional parameter estimation. The first stage enables multipath identification and algebraic coarse estimation. The following two stages offer optional successive refinements at the cost of increased complexity. The closed-form Cramer-Rao lower bound (CRLB) is derived to assess the estimation performance. Herein, the noise covariance matrix depends on multipath parameters in our active-RIS scenario. Numerical results are provided to verify the effectiveness of proposed algorithms under various evaluation metrics. Our results also show that active RIS can significantly improve channel estimation performance compared to passive RIS.

Problem

• Reconfigurable Intelligent Surface (RIS)
• Tensor-based Channel Estimation
• Broadband Systems

Innovation

• VSCPD
• RIS-assisted MIMO
• Tensor Decomposition