Mode Switching-based STAR-RIS with Discrete Phase Shifters

2025-10-20

AI Summary

This work addresses the sum-rate maximization problem for STAR-RIS-aided multi-antenna access point networks in 6G Internet-of-Things (IoT), balancing hardware efficiency and low-cost deployment. To comply with practical hardware constraints, we propose a STAR-RIS architecture integrating mode switching (binary transmission/reflection selection) and discrete-phase shifters. A mixed-integer nonlinear programming (MINLP) model is formulated to jointly optimize active beamforming, user power allocation, and STAR-RIS phase configurations. An efficient block coordinate descent (BCD)-based algorithm is developed, leveraging convex-concave procedure (CCP) and combinatorial optimization techniques to iteratively solve the coupled subproblems. Simulation results demonstrate that the proposed scheme significantly outperforms benchmark methods in sum rate while exhibiting strong scalability. It establishes a novel paradigm for energy-efficient and spectrum-efficient 6G RIS deployment.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

The increasing demand for cost-effective, high-speed Internet of Things (IoT) applications in the coming sixth-generation (6G) networks has driven research toward maximizing spectral efficiency and simplifying hardware designs. In this context, we investigate the sum rate maximization problem for a mode-switching discrete-phase shifters simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided multi-antenna access point network, emphasizing hardware efficiency and reduced cost. A mixed-integer nonlinear optimization framework is formulated for joint optimization of the active beamforming matrix, user power allocation, and STAR-RIS phase shift vectors, including binary transmission/reflection amplitudes and discrete phase shifters. To solve the formulated problem, we employ a block coordinate descent method, dividing it into three subproblems tackled using difference-of-concave programming and combinatorial optimization techniques. Numerical results validate the effectiveness of the proposed joint optimization approach, consistently achieving superior sum rate performance compared to partial optimization methods, thereby underscoring its potential for efficient and scalable 6G IoT systems.

Problem

• Maximizing spectral efficiency in 6G IoT networks
• Optimizing joint beamforming and discrete phase shifts
• Enhancing sum rate performance with hardware efficiency

Innovation

• Mode switching-based STAR-RIS with discrete phase shifters
• Joint optimization of beamforming, power allocation, and phase shifts
• Block coordinate descent with difference-of-concave and combinatorial optimization