Task offloading and resource allocation for UAV-assisted mobile edge computing with imperfect channel estimation over Rician fading channels

Abstract

AbstractIn this paper, we develop task offloading and resource allocation scheme for unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system with channel estimation errors over Rician fading channels. The objective is to maximize the system utility with constrained network stability, transmit power, and data arrival rate. We consider a general multi-user UAV-assisted MEC system based on frequency division multiple access (FDMA), and we assume that the computation tasks are split into separate tasks and offloaded to the server for computing. We study stochastic computational resource management based on the Lyapunov optimization algorithm. The optimal transmit power and bandwidth allocation for computation offloading are obtained alternately, and the optimal computation task admission at each time slot and the optimal value of the auxiliary variable are derived. Simulation results verify the effectiveness of the proposed scheme in the paper and evaluate the influence of various parameters to the system performance.