Joint trajectory optimization and computation offloading strategy for space-air-ground integrated networks

As a new type of network architecture, the space-air-ground integrated network can effectively improve the network coverage and service quality, and is the key support for the realization of the Internet of everything in the future 6G. Addressing the issues of inefficient computational resource allocation and underutilization of unmanned aerial vehicle(UAV) within such networks,a joint trajectory optimization and computation offloading strategy was proposed for the space-air-ground integrated network. Firstly, the correlation relationship between the UAV's hovering model and the ground terminal was analyzed. Secondly, a system energy minimization problem was established by jointly considering the UAV trajectory, the matching factor between the UAV and the ground terminal, the task offloading ratio and the computational resource allocation. Finally, a particle swarm optimization algorithm combined with a genetic algorithm operator was used to jointly optimize the UAV trajectory and the computational offloading scheme. The simulation results show that the proposed algorithm has good convergence and can reduce the system energy consumption by about 33.5% with good performance.