Exploiting RIS Optimization Limits for Multi-User Beamforming and Signal Suppression

2026-06-15

AI Summary

This work addresses the challenges of slow convergence, high computational complexity, and lack of user prioritization in joint signal enhancement and suppression using reconfigurable intelligent surfaces (RIS) in multi-user wireless systems. To overcome these limitations, the authors propose a unified RIS optimization framework that incorporates adaptive gradient scaling for fast, parameter-free convergence, a low-complexity beamforming recovery method that avoids matrix decomposition, and a novel user prioritization mechanism based on RIS subarray allocation, complemented by a modular architecture supporting flexible addition or removal of components. Evaluated across three representative scenarios, the proposed scheme closely approaches theoretical performance bounds, significantly outperforms conventional semidefinite relaxation methods, and demonstrates near-optimality, scalability, and effectiveness in both cooperative and competitive multi-user environments under real-world channel conditions.

Abstract

This paper presents a unified framework for exploiting the boundaries of reconfigurable intelligent surfaces (RIS) joint optimization in multi-user wireless systems, where a single RIS accommodates diverse objectives.We first propose an adaptive gradient-scaling mechanism that accelerates the convergence of the underlying optimization algorithm while maintaining stable performance across varying channel and system parameters. The proposed mechanism enables the solver to reach a reasonably good solution rapidly without requiring manual tuning of step sizes or algorithmic hyperparameters when system inputs change. We then propose a low-complexity beamformer recovery method tailored for single-user scenarios, which circumvents the full matrix decomposition required by traditional approaches, thereby significantly reducing computational overhead. Building on these foundations, we develop an element allocation strategy that enables user-specific prioritization through assignment of RIS subsets. This is further extended by a modular add-drop mechanism that supports partial-panel optimization in general multi-user settings. The framework is evaluated across three representative scenarios: (i) signal amplification for all users, (ii) signal suppression for all users, and (iii) selective amplification and suppression. To characterize performance limits, we derive power trade-off boundaries using scalarized joint optimization, which closely align with Monte Carlo simulations. Our unified joint optimization method consistently yield solutions near these boundaries, confirming its near-optimality. Extensive simulations under realistic channel models demonstrate that the proposed approach outperforms conventional semidefinite relaxation techniques, offering a scalable and effective RIS control strategy for cooperative and competitive multi-user environments.

Problem

• Reconfigurable Intelligent Surface
• Multi-User Beamforming
• Signal Suppression
• Joint Optimization
• Performance Limits

Innovation

• Reconfigurable Intelligent Surface (RIS)
• adaptive gradient scaling
• low-complexity beamforming
• element allocation strategy
• joint optimization