Beamforming-Based Achievable Rate Maximization in ISAC System for Multi-UAV Networking

Abstract

Integrated Sensing and Communication (ISAC) combined with Uncrewed Aerial Vehicle (UAV) networking offers a promising solution for resilient wireless connectivity in post-disaster scenarios. In such settings, UAVs must rapidly explore unknown environments while providing continuous sensing-aided communication services. This paper proposes a novel ISAC framework based on a temporal-assisted frame structure that integrates search, tracking, and communication functions for multi-UAV networks. We formulate a joint optimization problem that maximizes the total achievable rate per slot through the coordinated design of UAV beamforming, load management, and UAV direction planning, while accounting for realistic sensing uncertainties. The NP-hard problem is decomposed into three sub-problems and solved through a combination of an enhanced parallel Successive Convex Approximation (SCA)-Iterative Rank Minimization (IRM) algorithm, a coalition game-based load allocation strategy, and a Fermat point-based UAV path planning method. Simulation results demonstrate that the proposed scheme significantly improves communication capacity, fairness, and sensing accuracy compared to conventional feedback-based communication designs. This work provides practical design insights for robust and efficient ISAC-based UAV networks.