Multi-Tier UAV Swarm Deployment for JRC Systems: Distributed Optimization With Learning-Based Adaptation

This paper proposes a multi-tier coordination framework for autonomous Unmanned Aerial Vehicle (UAV) swarm deployment in joint radar-communication (JRC)-enabled post-disaster assessment. The proposed framework adopts a distributed/coordinated optimization approach, where analytical updates are derived for the initial 3D UAV positioning and bandwidth-power allocation, jointly optimizing sensing quality and communication performance under resource constraints. Building on this foundation, a Deep Reinforcement Learning (DRL) agent is also developed to dynamically refine UAVs' positions in real time, adapting to environmental uncertainties and mission dynamics. The proposed hybrid optimization-learning framework targets the balance between optimality and complexity by enabling adaptive and intelligent decision-making for real-time and efficient deployment of distributed UAV swarms. The DRL policy adapts well to dynamic, mobile-target scenarios, while the distributed optimization enables rapid and pre-training-free deployment, making it ideal for time-critical missions. Unlike existing approaches that either rely on centralized control or neglect the interplay between sensing and communication, our framework enables distributed, infrastructure-free coordination. Simulation results show that the proposed framework achieves up to 13% and 27% higher average sensing SNR when varying the number of targets and total available power per UAV, respectively, compared to communication-centric, radar-centric, and learning-based baselines. These results confirm the effectiveness of distributed optimization and DRL-based coordination for scalable, resilient, and adaptable UAV deployment in disaster response and other mission-critical scenarios.