Three-Dimensional Trajectory Optimization for Energy-Constrained UAV-Enabled IoT System in Probabilistic LoS Channel

Unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) system will play an essential role in future wireless networks, owing to the flexible deployment of the UAVs and the Line-of-Sight (LoS) dominant channels. In this article, we take the limited onboard energy into account and investigate the three-dimensional (3-D) trajectory of the UAV and the transmission scheduling of the ground devices (GDs) for the UAV-enabled IoT system, where multiple GDs transmit data to the UAV in a time-division multiple access fashion. Specifically, taking the angle-dependent probabilistic LoS channel into account, we derive the mathematical expression of the amount of the transmitted data of the GDs and model the energy consumption of the UAV. Next, considering the fairness among the GDs, we aim to maximize the minimum expected amount of the transmitted data of the GDs, and formulate it as an optimization problem, which is further discretized to a problem with a finite number of variables. Due to the nonconvexity of the discretized problem, we transform the problem into a tractable form, and develop an effective iterative algorithm to solve it by using the block coordinate descent and successive convex approximation methods. The convergence and the complexity of the developed algorithm are analytically evaluated. Simulation results demonstrate that our designed 3-D UAV trajectory can effectively improve the minimum expected amount of the transmitted data of the GDs.

Automated summary

Unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) system is crucial for future wireless networks due to the flexibility of UAV deployment and the dominance of Line-of-Sight (LoS) channels. This article investigates the 3-D trajectory of the UAV and transmission scheduling of ground devices (GDs) in the UAV-enabled IoT system. The goal is to maximize the minimum expected amount of transmitted data while considering fairness among GDs. An effective iterative algorithm is proposed to solve the optimization problem.