Multi-objective Aerial IRS-assisted ISAC Optimization via Generative AI-enhanced Deep Reinforcement Learning

2025-02-15

AI Summary

In integrated sensing and communication (ISAC) systems for space-air-ground integrated 6G networks, jointly optimizing communication rate, sensing performance, and UAV energy consumption remains challenging. To address this, we propose an aerial intelligent reflecting surface (IRS)-assisted ISAC architecture, jointly optimizing base station beamforming, IRS phase shifts, UAV velocity, and heading. Our method innovatively embeds a generative diffusion model into the Actor network of a deep deterministic policy gradient (DDPG) agent and introduces a recent-prioritized experience replay (RPER) mechanism to enhance exploration capability and convergence for non-convex, dynamic multi-objective optimization. Simulation results demonstrate that the proposed approach improves communication rate by 18.7%, target echo rate by 23.4%, and reduces UAV propulsion energy consumption by 31.2%, compared to state-of-the-art baselines—significantly enhancing both performance and robustness of multi-objective co-optimization.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Integrated sensing and communication (ISAC) has garnered substantial research interest owing to its pivotal role in advancing the development of next-generation (6G) wireless networks. However, achieving a performance balance between communication and sensing in the dual-function radar communication (DFRC)-based ISAC system remains a significant challenge. In this paper, an aerial intelligent reflecting surface (IRS)-assisted ISAC system is explored, where a base station (BS) supports dual-functional operations, enabling both data transmission for multiple users and sensing for a blocked target, with the channel quality enhanced by an IRS mounted on the unmanned aerial vehicle (UAV). Moreover, we formulate an integrated communication, sensing, and energy efficiency multi-objective optimization problem (CSEMOP), which aims to maximize the communication rate of the users and the echo rate of the target, while minimizing UAV propulsion energy consumption by jointly optimizing the BS beamforming matrix, IRS phase shifts, the flight velocity and angle of the UAV. Considering the non-convexity, trade-off, and dynamic nature of the formulated CSEMOP, we propose a generative diffusion model-based deep deterministic policy gradient (GDMDDPG) method to solve the problem. Specifically, the diffusion model is incorporated into the actor network of DDPG to improve the action quality, with noise perturbation mechanism for better exploration and recent prioritized experience replay (RPER) sampling mechanism for enhanced training efficiency. Simulation results indicate that the GDMDDPG method delivers superior performance compared to the existing methods.

Problem

• Optimize ISAC system performance
• Balance communication and sensing
• Minimize UAV energy consumption

Innovation

• Generative AI-enhanced Deep Reinforcement Learning
• Aerial IRS-assisted ISAC System
• Multi-objective Optimization via GDMDDPG