Mode Switching-based STAR-RIS with Discrete Phase Shifters

TL;DR

This work tackles sum-rate maximization in a STAR-RIS aided uplink network with mode-switching discrete phase shifters. It formulates a mixed-integer nonlinear program to jointly optimize user powers $p_u$, STAR-RIS phase/amplitude configurations, and the AP's active beamforming matrix $ extbf{W}$, and solves it via a block coordinate descent framework that combines DC programming for power, fractional programming for RIS and beamforming, and MILP for discrete amplitudes. The proposed method, backed by convergence guarantees, demonstrates superior sum-rate performance over several benchmarks and highlights the effectiveness of jointly optimizing transmission and reflection resources under hardware-friendly MS constraints. The results indicate that MS discrete STAR-RIS designs are both practical and beneficial for scalable 6G IoT deployments, achieving a favorable balance between performance and hardware complexity.

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.

Figures (5)

• Figure 1: STAR-RIS aided uplink system.
• Figure 2: Flowchart of the proposed optimization method.
• Figure 3: Sum rate vs. iteration index.
• Figure 4: Sum rate versus different system parameters.
• Figure 5: Sum rate vs. quantization level $Q$.