Joint Optimization of Unloading Decision and Power Allocation for UAV-assisted Mobile Edge Computing
Due to the limited computing power of Mobile Device(MD),there is immense pressure when executing real-time and computationally intensive tasks.Therefore,this heterogeneous network based on Non-Orthogonal Multiple Access(NOMA)technology with double-layer Unmanned Aerial Vehicle(UAV)assisted Mobile Edge Computing(MEC)is studied.UAV equipped with edge server can cooperate with base stations to provide computing and unloading service for MD.A communication model and a computational model are established for this heterogeneous network.Then,the energy consumption and time weighting and minimization problems are proposed,and a joint optimization algorithm for mission unloading and MD transmitting power allocation is developed.The original problem is divided into two subproblems,then an Improved Binary Particle Swarm Optimization(IBPSO)algorithm is used to solve the mission unloading decision subproblem,and an Improved Atomic Orbit Search Algorithm(IAOS)is used to solve the MD transmitting power allocation subproblem.The simulation results show that compared with several benchmark algorithms,the weighted sum of time and energy consumption of the proposed algorithm decreases by 29.2％,29.2％and 33.2％respectively in the circumstances of different maximal transmitting power,computing cycle per bit and MD task size.At the same time,the algorithm converges within 20 iterations and has preeminent ability of convergence.
国家科技期刊平台
NSTL
万方数据
