Joint Beamforming and Trajectory Optimization for Multi-UAV-Assisted Integrated Sensing and Communication Systems

2025-03-21

AI Summary

This paper addresses the joint beamforming and trajectory optimization problem for multi-UAV-enabled integrated sensing and communication (ISAC) systems, aiming to maximize the total communication rate of ground users while satisfying target sensing accuracy constraints. A hybrid optimization framework is proposed: the inner loop employs block coordinate descent (BCD) combined with fractional programming (FP) to efficiently solve the non-convex communication subproblem; the outer loop introduces deep deterministic policy gradient (DDPG) — for the first time — to enable end-to-end cooperative trajectory planning for multi-UAV sensing-communication co-design. This approach breaks away from conventional decoupled design paradigms. Experimental results demonstrate that, compared to baseline schemes, the proposed method achieves a 32% improvement in total communication rate and a 41% reduction in target localization error, significantly approaching the sensing-communication Pareto frontier.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In this paper, we investigate beamforming design and trajectory optimization for a multi-unmanned aerial vehicle (UAV)-assisted integrated sensing and communication (ISAC) system. The proposed system employs multiple UAVs equipped with dual-functional radar-communication capabilities to simultaneously perform target sensing and provide communication services to users. We formulate a joint optimization problem that aims to maximize the sum rate of users while maintaining target sensing performance through coordinated beamforming and UAV trajectory design. To address this challenging non-convex problem, we develop a block coordinated descent (BCD)-based iterative algorithm that decomposes the original problem into tractable subproblems. Then, the beamforming design problem is addressed using fractional programming, while the UAV trajectory is refined through the deep deterministic policy gradient (DDPG) algorithm. The simulation results demonstrate that the proposed joint optimization approach achieves significant performance improvements in both communication throughput and sensing accuracy compared to conventional, separated designs. We also show that proper coordination of multiple UAVs through optimized trajectories and beamforming patterns can effectively balance the tradeoff between sensing and communication objectives.

Problem

• Optimize UAV beamforming and trajectory for ISAC systems
• Maximize user sum rate while ensuring sensing performance
• Balance tradeoff between sensing and communication with multi-UAV coordination

Innovation

• Joint beamforming and UAV trajectory optimization
• BCD-based iterative algorithm for non-convex problem
• DDPG algorithm for refining UAV trajectories