Joint Beamforming and Trajectory Optimization for Multi-UAV-Assisted Integrated Sensing and Communication Systems
Yan Kyaw Tun, Nway Nway Ei, Sheikh Salman Hassan, Cedomir Stefanovic, Nguyen Van Huynh, Madyan Alsenwi, Choong Seon Hong
TL;DR
The paper tackles the non-convex problem of jointly designing beamforming and UAV trajectories for a multi-UAV ISAC system, aiming to maximize user sum rate while satisfying sensing accuracy constraints expressed via $CRB(\phi_{k,n}^u) \le \Gamma_{k,n}^u$. It introduces a block coordinated descent framework that alternates between fractional programming-based beamforming (communication and sensing) and a DDPG-based trajectory optimization, leveraging Lagrangian and quadratic transforms to handle rate expressions and constraints. Key contributions include a detailed system model, FP-based beamforming solutions with rank-relaxation guarantees, a CVX-solved sensing-beamforming subproblem, and a DDPG-based trajectory policy with state-action-reward design aligned to ISAC objectives. Simulations show substantial gains in both sum rate and sensing accuracy over separated designs, with rapid convergence and effective multi-UAV coordination, underscoring the practical viability of ISAC-enabled UAV networks.
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.