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 nonconvexity 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.INDEX TERMS Cell-free, satellite-UAV network, power allocation, NOMA.

Trajectory Optimization in User-Centric Distributed Massive MIMO Systems Enabled by UAV Swarms

“…However, the assumption of an arbitrarily given UAV trajectory limits the applicability of the proposed solution. This was addressed by the study in [17], which proposed a trajectory optimization for the same scenario. However, both works relied on scenarios that leverage hybrid UAV and satellite systems to provide service for remote IoT devices.…”

Resource Scheduling Based on Deep Reinforcement Learning in UAV Assisted Emergency Communication Networks

“…UAV trajectory design aims to determine the optimal routes and motion patterns for UAVs to achieve specific objectives, such as data collection and surveillance. It is an essential aspect for ensuring physical layer security [24], optimizing power allocation [25] and avoiding collision with obstacles [26]. In [27], the authors investigate the trajectory optimization problem for a UAV equipped with intelligent reflecting surface (IRS).…”