Game-Theoretic Optimization for Multi-UAV Integrated Sensing and Communication Networks

With the rapid advancement of unmanned aerial vehicle (UAV) technology, its high mobility and ease of deployment have demonstrated tremendous potential in integrated sensing and communication (ISAC) systems. However, as user demands diversify, network architectures become increasingly distributed, and as the number of UAVs grows rapidly, ISAC systems face significant challenges in optimizing communication and sensing resources. In particular, UAV communication in collaborative UAV missions is highly susceptible to hostile signal disruptions, leading to degraded communication quality, weakened sensing performance, resource wastage, and increased energy consumption. To address these challenges, this paper proposes a game-theoretic optimization method for multi-UAV ISAC networks. First, a multi-UAV communication-sensing network model is constructed to characterize the impact of interference sources on communication and sensing performance. Based on this model, under energy constraints, a joint optimization of transmission power and UAV trajectory is performed. The problem is formulated as a utility maximization framework for the ISAC network and modeled as a game-theoretic approach. The proposed model is rigorously proven to be an exact potential game, ensuring the existence of at least one pure-strategy Nash equilibrium. To solve for the equilibrium, a distributed optimization algorithm—the Electric Eel Foraging Optimization (EEFO) algorithm is developed. Simulation results validate the effectiveness of the proposed method, showing that it significantly enhances the communication and sensing performance of multi-UAV networks while effectively reducing energy consumption. This work provides a novel solution to the resource optimization challenges in multi-UAV ISAC networks, offering both theoretical and practical contributions to advance ISAC technology.