Computation Rate Maximization in UAV-Enabled Wireless Powered Mobile-Edge Computing Systems

TL;DR

This paper investigates maximizing computation rate in UAV-enabled wireless powered MEC systems by proposing algorithms for resource allocation under energy and speed constraints, demonstrating improved performance and low complexity.

Contribution

It introduces novel algorithms for computation rate maximization in UAV-enabled MEC systems with wireless power transfer, addressing non-convex challenges and providing closed-form solutions.

Findings

Proposed algorithms outperform benchmarks in simulation.

Resource allocation schemes converge quickly.

Achieved low computational complexity.

Abstract

Mobile edge computing (MEC) and wireless power transfer (WPT) are two promising techniques to enhance the computation capability and to prolong the operational time of low-power wireless devices that are ubiquitous in Internet of Things. However, the computation performance and the harvested energy are significantly impacted by the severe propagation loss. In order to address this issue, an unmanned aerial vehicle (UAV)-enabled MEC wireless powered system is studied in this paper. The computation rate maximization problems in a UAV-enabled MEC wireless powered system are investigated under both partial and binary computation offloading modes, subject to the energy harvesting causal constraint and the UAV's speed constraint. These problems are non-convex and challenging to solve. A two-stage algorithm and a three-stage alternative algorithm are respectively proposed for solving the formulated problems. The closed-form expressions for the optimal central processing unit frequencies, user offloading time, and user transmit power are derived. The optimal selection scheme on whether users choose to locally compute or offload computation tasks is proposed for the binary computation offloading mode. Simulation results show that our proposed resource allocation schemes outperforms other benchmark schemes. The results also demonstrate that the proposed schemes converge fast and have low computational complexity.