Deep Reinforcement Learning for Energy Efficiency Maximization in RSMA-IRS-Assisted ISAC System

TL;DR

This paper develops a 3D channel model for IRS-enhanced ISAC networks with RSMA, formulating an EE maximization problem solved via deep reinforcement learning, demonstrating improved energy efficiency in urban environments.

Contribution

It introduces a novel 3D channel model for IRS-assisted ISAC systems with RSMA and applies DRL to optimize energy efficiency considering practical constraints.

Findings

DRL effectively improves energy efficiency in IRS-assisted ISAC systems.

System EE decreases at higher frequencies, especially under double-Rayleigh fading.

Proposed model accurately captures urban IRS-assisted communication environments.

Abstract

This paper proposes a three-dimensional (3D) geometry-based channel model to accurately represent intelligent reflecting surfaces (IRS)-enhanced integrated sensing and communication (ISAC) networks using rate-splitting multiple access (RSMA) in practical urban environments. Based on this model, we formulate an energy efficiency (EE) maximization problem that incorporates transceiver beamforming constraints, IRS phase adjustments, and quality-of-service (QoS) requirements to optimize communication and sensing functions. To solve this problem, we use the proximal policy optimization (PPO) algorithm within a deep reinforcement learning (DRL) framework. Our numerical results confirm the effectiveness of the proposed method in improving EE and satisfying QoS requirements. Additionally, we observe that system EE drops at higher frequencies, especially under double-Rayleigh fading.