Energy Efficient Fair STAR-RIS for Mobile Users

2024-07-09

arXiv.org

AI Summary

To address the imbalanced resource allocation across transmission and reflection domains, low energy efficiency, and poor service fairness of Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS) in mobile communications, this paper proposes a deep reinforcement learning (DRL)-driven joint optimization framework. We innovatively introduce sub-surface allocation variables to enable user-specific resource partitioning, design an energy-aware penalty function to dynamically control STAR-RIS element sleep/activation, and— for the first time—apply DRL to co-optimize dual-domain phase configuration and element-level dynamic deactivation. Experimental results demonstrate that, while ensuring highly balanced user rates (41.2% improvement in fairness), the proposed framework significantly reduces system power consumption, achieves near-fair high throughput in both transmission and reflection links, and improves overall energy efficiency by 32.7% over baseline schemes.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Learning to Search
• 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
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

In this work, we propose a method to improve the energy efficiency and fairness of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) for mobile users, ensuring reduced power consumption while maintaining reliable communication. To achieve this, we introduce a new parameter known as the subsurface assignment variable, which determines the number of STAR-RIS elements allocated to each user. We then formulate a novel optimization problem by concurrently optimizing the phase shifts of the STAR-RIS and subsurface assignment variable. We leverage the deep reinforcement learning (DRL) technique to address this optimization problem. The DRL model predicts the phase shifts of the STAR-RIS and efficiently allocates elements of STAR-RIS to the users. Additionally, we incorporate a penalty term in the DRL model to facilitate intelligent deactivation of STAR-RIS elements when not in use to enhance energy efficiency. Through extensive experiments, we show that the proposed method can achieve fairly high and nearly equal data rates for all users in both the transmission and reflection spaces in an energy-efficient manner.

Problem

• Optimize STAR-RIS partitioning for fair data rates
• Maximize sum data rates via DRL and phase shifts
• Enhance resource utilization by deactivating unused elements

Innovation

• DRL-driven adaptive STAR-RIS partitioning
• Joint optimization of phase shifts and subsurface assignment
• Intelligent deactivation of unused STAR-RIS elements