Two-Stage Coded-Sliding Beam Training and QoS-Constrained Sum-Rate Maximization for SIM-Assisted Wireless Communications

2026-02-02

IEEE Transactions on Wireless Communications

AI Summary

This work addresses the high complexity associated with channel state information acquisition and phase optimization in stacked intelligent metasurface (SIM)-assisted communications by proposing a unified low-complexity algorithmic framework. The approach decouples two-dimensional beamforming into two one-dimensional subproblems and enhances angular resolution and robustness through a two-stage coded sliding beam training (TSCSBT) strategy. Leveraging algorithms such as variance-driven block successive upper-bound minimization (VD-BSUM) and proximal dual multipliers method (PDMM), the framework enables closed-form iterative solutions for sum-rate maximization under quality-of-service (QoS) constraints. Simulation results demonstrate that the proposed method significantly outperforms existing schemes in terms of beam pattern fidelity, training accuracy, angular resolution, and system sum rate.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: State Estimation
• Machine Learning: Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search

Abstract

Stacked intelligent metasurfaces (SIM) provide a cost-effective and scalable solution for large-scale antenna communications.However, efficient channel state information acquisition and phase shift optimization remain critical challenges. In this paper, we develop a unified framework of low-complexity algorithms for SIM-assisted communication systems to address these issues. Specifically, we propose a generalized two-step codebook construction (TSCC) method that leverages two-dimensional angular-domain decoupling to transform planar array beamformer design into two independent one-dimensional linear array beamformer design problems, efficiently solved via the Gerchberg-Saxton algorithm and our proposed majorization-minimization-based proximal distance (PDMM) algorithm. We further develop a two-stage coded-sliding beam training (TSCSBT) method for low-overhead and high-accuracy beam training, where error-correcting codes are embedded in the first-stage training to enhance robustness against noise, and sliding sampling is subsequently performed around the matched angular samples to improve angular resolution. The proposed framework is further extended to multi-path user channels. Finally, a variable decoupling-based block successive upper bound minimization (VD-BSUM) algorithm is proposed to directly solve the QoS-constrained sum-rate maximization problem through closed-form iterative updates with substantially reduced computational complexity. Simulation results demonstrate the effectiveness of the proposed methods in achieving precise beam pattern realization, improved beam training accuracy and angular resolution, and enhanced sum-rate performance.

Problem

• intelligent metasurfaces
• channel state information
• beam training
• phase shift optimization
• sum-rate maximization

Innovation

• Two-stage coded-sliding beam training
• Generalized two-step codebook construction
• Variable decoupling-based BSUM
• Angle-domain decoupling
• QoS-constrained sum-rate maximization