Optimal Resource Allocation for Successful Task Transmission Probability Maximization in UAV Swarm Networks

Abstract

—Unmanned aerial vehicle (UAV) swarm networks (USNTs) are a crucial component of the emerging low-altitude intelligent networks. For supporting low-altitude economic activities, this paper explores successful task transmission probability (STP) maximization, while maintaining the performance fairness of each UAV swarm for USNTs with resource limitations (e.g., frequency, power). Towards this goal, this paper formulates STP maximization and its fairness as a nonlinear non-convex optimization problem. To solve the complex optimization problem, we first propose an adaptive frequency block sharing algorithm to determine whether different-sized UAV swarms use the same frequency blocks or not, providing an efficient initial solution for inter-swarm resource allocation. Then we develop a double deep Q-network-based algorithm for inter-swarm resource allocation to guarantee fairness among swarms. Based on the inter-swarm allocation results, we propose a genetic algorithm-based method for intra-swarm resource allocation to achieve the STP maximization, which ensures reliable transmission of high-priority tasks. Extensive simulation results are presented to validate the efficiency of our proposed algorithms, and also to illustrate the impact of system parameters on STP and fairness.