Computation Rate Maximum for Mobile Terminals in UAV-assisted Wireless Powered MEC Networks with Fairness Constraint

TL;DR

This paper proposes a reinforcement learning-based method for UAV trajectory and resource management in wireless powered MEC networks, maximizing computation rate while maintaining fairness among mobile terminals.

Contribution

It introduces a SAC-based algorithm that efficiently optimizes UAV paths and resource allocation considering fairness and dynamic terminal movements.

Findings

SAC-TR outperforms benchmark algorithms in various scenarios.

The proposed method adapts quickly to changing network conditions.

It effectively balances computation rate and fairness constraints.

Abstract

This paper investigates an unmanned aerial vehicle (UAV)-assisted wireless powered mobile-edge computing (MEC) system, where the UAV powers the mobile terminals by wireless power transfer (WPT) and provides computation service for them. We aim to maximize the computation rate of terminals while ensuring fairness among them. Considering the random trajectories of mobile terminals, we propose a soft actor-critic (SAC)-based UAV trajectory planning and resource allocation (SAC-TR) algorithm, which combines off-policy and maximum entropy reinforcement learning to promote the convergence of the algorithm. We design the reward as a heterogeneous function of computation rate, fairness, and reaching of destination. Simulation results show that SAC-TR can quickly adapt to varying network environments and outperform representative benchmarks in a variety of situations.