OTFS-ISAC Enabled UAV Network for High Speed Trains

This paper proposes a novel UAV network framework designed for high speed train, where orthogonal time frequency space is employed to mitigate doppler shifts, and integrated sensing and communication is utilized for train carriage positioning and acquisition train status information. To maximize communication rates, a joint optimization problem for resource allocation and trajectory planning is formulated. Due to the non-convexity and high-dimensional nature of the optimization problem, we propose a hybrid metaheuristic algorithm that effectively combines the local exploitation capability of the grey wolf optimizer with the global exploration ability of the whale optimization algorithm. The simulation results validate that the proposed algorithm performance advantages in terms of transmission rates across diverse scenarios with varying numbers of users, bandwidth allocations, and train speeds.