Edge-Enabled UAV Swarm Deployment for Rapid Post-Disaster Search and Rescue

TL;DR

Problem: enable rapid, robust post-disaster search and rescue by maximizing radar detection quality across a UAV swarm while guaranteeing communication rates. Approach: a distributed DJRC framework that decomposes the optimization into a global FBS location problem and local UAV placement/power-split decisions, using gradient-based updates for the FBS and reward-driven UAV actions, yielding polynomial-time complexity. Key findings: DJRC consistently outperforms fixed-radar or fixed-communication baselines (FROC and ORFC) in total SNR $\eta_t$ while maintaining $R_{\min}$, converging within roughly 40 iterations. Significance: delivers scalable, real-time capable deployment for multi-UAV JRC in disaster zones with limited ground infrastructure and challenging environments.

Abstract

This paper presents an optimized Joint Radar-Communication (JRC) system utilizing multiple Unmanned Aerial Vehicles (UAVs) to simultaneously achieve sensing and communication objectives. By leveraging UAVs equipped with dual radar and communication capabilities, the proposed framework aims to maximize radar sensing performance across all UAVs in challenging environments. The proposed approach focuses on formulating and solving a UAV positioning and power allocation problem to optimize multi-UAV sensing and communications performance over multiple targets within designated zones. Due to the NP-hard and combinatorial nature of the problem, we propose a Distributed JRC-based (DJRC) solution. This solution employs an efficient reward for potential actions and consistently selects the best action that maximizes the reward while ensuring both communications and sensing performance. Simulation results demonstrate significant performance improvements of the proposed solution over state-of-the-art radar- or communication-centric trajectory planning methods, with polynomial complexity dependent on the number of UAVs and linear dependence on the iteration count.

Figures (5)

• Figure 1: The considered multi-UAV JRC system model.
• Figure 2: 3D trajectory of UAVs during the execution of the DJRC algorithm.
• Figure 3: Convergence of the DJRC algorithm: evolution of total received SNR ($\eta_t$) and sum data rate (${R}_t$) over iterations.
• Figure 4: Performance comparison of DJRC, FROC, and ORFC solutions in terms of: (a) total received SNR $\eta_t$, and (b) sum of achieved data rates ${R}_t$, under varying numbers of targets.
• Figure 5: Performance comparison of DJRC, FROC, and ORFC solutions in terms of: (a) total received SNR $\eta_t$, and (b) sum of achieved data rates ${R}_t$, under varying total power per UAV.