Trajectory Optimization and Power Allocation for Cell-Free Satellite-UAV Internet of Things

Abstract

Massive access outside the coverage of terrestrial cellular networks will be the main feature of the sixth generation (6G) networks. In order to cope with it, the cognitive satellite-UAV network (CSUN) has drawn a lot of attentions. In this paper, we investigate the UAV trajectory optimization and power allocation for the cell-free CSUN consisting of one satellite and a swarm of UAVs. Indeed, due to the on-board energy constraints of UAVs, both the trajectory optimization and the power allocation can significantly save the energy to improve the energy efficiency. The joint trajectory optimization and power allocation problem is formulated as a mixed-integer non-convex optimization problem which is extremely difficult to solve. In order to reduce the computational complexity, we decompose the original optimization problem into two subproblems in terms of the trajectory optimization and power allocation. For the trajectory optimization subproblem, we model it as a Traveling Salesman Problem (TSP), and the PSO is adopted to solve it. When the trajectory variables are fixed, the power allocation subproblem is still difficult to tackle due to its non-convexity and large scale. Firstly, we present a kind of centralized algorithm in which the DC (difference of two convex functions) algorithm is applied to optimize it, and then a distributed algorithm based on auxiliary variables is proposed to reduce the signaling overhead and computational complexity. The simulation results demonstrate the effectiveness of the proposed joint trajectory optimization and power allocation algorithm for the cell-free CSUN.