—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.
Zhuojia Yang, Wei Su, Bin Yang et al.· IEEE Transactions on Mobile...· 0 citations
—Unmanned aerial vehicle (UAV) swarm-assisted integrated sensing and communication (ISAC) networks are a crucial technology for providing communication and sensing services in emergency rescue scenarios without base station support. However, the strong coupling between communication and sensing resources in such networks fundamentally limits the communication and sensing performance of ISAC systems. This paper jointly optimizes spectrum allocation, UAV association and deployment to maximize average system throughput while ensuring localization accuracy in such networks, where sensing is realized through localization. We begin by deriving an analytical expression for localization accuracy, which explicitly captures the joint effects of link quality and anchor geometry under shared communication-localization spectrum resources. We then formulate average system throughput maximization as a mixed-integer nonlinear and non-convex optimization problem with the constraints of localization accuracy, sub-channels, UAV association, UAV deployment and signal-to-interference-plus-noise ratio. We further develop an alternating iterative optimization method to solve this complex optimization problem. Within this method, a particle swarm optimization-based method is developed to jointly optimize spectrum allocation and UAV association, and a dueling double deep Q-network-based method is further employed for UAV deployment optimization. Finally, extensive simulation results are presented to validate the efficiency of our optimization method, and also to illustrate how key parameters influence average system throughput and localization accuracy.
Zhuo-Jia Yang, Wei Su, Bin Yang et al.· IEEE Transactions on Mobile...· 0 citations
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