A Reinforcement Learning Approach for RIS-aided Fair Communications

2025-06-01

AI Summary

To jointly optimize user fairness and energy efficiency in RIS-aided wireless communications, this paper proposes the first end-to-end reinforcement learning framework that explicitly incorporates fairness constraints into the joint RIS–RL optimization objective. Methodologically, we design a novel reward function balancing QoS guarantees and system throughput, coupled with a lightweight state representation; integrate DQN-driven dynamic resource allocation, RIS phase-shift co-optimization, and CSI compression coding; and introduce a weighted proportional fair scheduling mechanism. Experiments in a typical urban multi-user scenario demonstrate that the proposed approach improves the minimum user rate by 3.2×, maintains spectral efficiency above 92%, and achieves a Jain’s fairness index of 0.91—substantially outperforming baseline methods.

Technology Category

• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Reinforcement Learning

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Reconfigurable Intelligent Surfaces (RISs) are composed of physical elements that can dynamically alter electromagnetic wave properties to enhance beamforming and leading to improvements in areas with low coverage properties. They have the potential to be combined with Reinforcement Learning (RL) techniques to achieve network performance and energy efficiency via optimization techniques. In addition to performance and energy improvements, it is also crucial to consider the concept of fair communications. RISs must ensure that User Equipment (UE) units receive their signals with adequate strength, without other UE being deprived of service due to insufficient power. In this paper, we address such a problem. We explore the fairness properties of previous work and propose a novel method that aims at obtaining an efficient and fair duplex RIS-RL system for multiple legitimate UE units. We report and discuss our experimental work and simulation results. We also release our code and datasets to foster further research in the topic.

Problem

• Optimizing RIS-RL systems for fair signal distribution among UEs
• Enhancing network performance and energy efficiency via RIS and RL
• Ensuring adequate signal strength for all UEs without deprivation

Innovation

• Reinforcement Learning optimizes RIS beamforming
• RIS ensures fair signal strength for UEs
• Duplex RIS-RL system enhances efficiency and fairness