联邦学习驱动的低空通感节点自适应切换OA

With the continuous evolution of integrated sensing and communications(ISAC)technology and the rapid development of the low-altitude economy,there is an increasingly urgent demand for continuous and accurate sensing of highly maneuverable targets in low-altitude heterogeneous networks.To address the challenges of sensing continuity and high precision in complex electromagnetic environments,this paper proposes a federated learning-driven adaptive handover mechanism for sensing nodes in low-altitude ISAC networks by leveraging existing communication infrastructure.First,a hierarchical hybrid-frequency base station deployment and a multi-transmit single-receive sensing architecture based on separate transmission and reception are employed to construct the foundation for cooperative sensing.Second,in view of the highly dynamic spatial trajectories of aerial targets,a long short-term memory network is utilized to predict target motion.Furthermore,a federated learning framework is introduced to collaboratively train a global"node scheduling decision model"among distributed nodes.This model takes environmental states and target prediction information as input and outputs an optimal transceiver node combination strategy in real time.Finally,through a dynamically elected central node,rapid adaptive handover of sensing nodes is achieved,dynamically aligning with service requirements and enabling persistent target tracking.Simulation results demonstrate that,during full-course tracking of high-speed maneuvering targets,the proposed algorithm significantly outperforms conventional sensing schemes in terms of perception coverage rate and resource utilization efficiency,verifying the effectiveness and superiority of the proposed approach.