Comparative Analysis of Phase-Shift Design Strategies for Reconfigurable Intelligent Surface (RIS)-Aided 6G Wireless Networks

Abstract

 Reconfigurable Intelligent Surfaces (RIS) have emerged as a pivotal physical-layer technology for beyond-5G and 6G wireless communication networks. By utilizing a large array of passive, programmable meta-surface elements, an RIS can dynamically reshape the radio propagation environment with minimal power consumption, effectively mitigating non-line-of-sight (NLoS) blockages and path loss. However, the joint optimization of base station (BS) beamforming vectors and the high-dimensional phase-shift configuration of the RIS presents a highly non-convex and computationally intense challenge. While advanced learning-based control policies like deep reinforcement learning (DRL) offer adaptive solutions for high-dimensional,  ynamic settings, evaluating baseline phase-shift strategies on a fully reproducible scale provides crucial structural insights into the behavior of RIS systems. This paper implements and evaluates three fundamental RIS phase-shift design strategies—random configuration, closed-form heuristic alignment, and gradient-based optimization—within a simplified single-user RIS-aided Multiple-Input Single-Output (MISO) downlink simulation.

Our experimental evaluations demonstrate that intelligent phase control strategies consistently outperform random configurations, yielding the largest relative performance gains in low Signal-to-Noise Ratio (SNR) regimes and the highest absolute rate gains within large RIS arrays. Furthermore, hardware ablation studies indicate that a modest phase-shift resolution of 2 to 4 bits is sufficient to capture nearly all the performance benefits offered by ideal, continuous-phase control. These results systematically validate on a manageable, fully reproducible scale the core motivations behind deploying complex adaptive control policies for larger and more dynamic multi-user 6G environments.