Optimizing Intelligent Reflecting Surface-Base Station Association for Mobile Networks

TL;DR

This paper introduces MDLBI, a multi-agent deep reinforcement learning scheme that optimizes IRS-BS association and phase shifts in multi-IRS wireless networks, significantly improving data rates without requiring inter-BS communication.

Contribution

The paper presents a novel multi-agent deep reinforcement learning approach for IRS-BS association that enhances network performance and scalability in multi-IRS systems.

Findings

MDLBI outperforms existing methods in data rate improvements.

The scheme is scalable to large network sizes.

MDLBI operates without inter-BS communication.

Abstract

This paper studies a multi-Intelligent Reflecting Surfaces (IRSs)-assisted wireless network consisting of multiple base stations (BSs) serving a set of mobile users. We focus on the IRS-BS association problem in which multiple BSs compete with each other for controlling the phase shifts of a limited number of IRSs to maximize the long-term downlink data rate for the associated users. We propose MDLBI, a Multi-agent Deep Reinforcement Learning-based BS-IRS association scheme that optimizes the BS-IRS association as well as the phase-shift of each IRS when being associated with different BSs. MDLBI does not require information exchanging among BSs. Simulation results show that MDLBI achieves significant performance improvement and is scalable for large networking systems.