Data Offloading Optimization Of UAV-Aided Mobile Edge Computing System

Unmanned aerial vehicles have been widely used in military,commercial,communications and other fields because of their high mobility and portability.At the same time,due to the rapid development of artificial intelligence,people's demand for computing is getting higher and higher.But the computing capability of existing mobile computing devices(computers,mobile phones)does not fully meet the needs of users for communication quality.Therefore,mobile edge computing technology has been proposed to improve the computing capability of the system to meet users' communication needs.This paper studies the UAV-aided mobile edge computing system,the details are as follows:(1)For the scenario of UAV-aided mobile edge computing.This reserach considers a single UAV with an MEC server to provide assisted computing services for multiple users on the ground.This research aim to maximize the sum of bits of offloading data among all users by jointly optimizing the UAV trajectory,user transmission power,bits of user local computing data,bits of UAV-aided compution task data,and bits of offloading data among all users.Aiming at the formed non-convex problem,an iterative optimization method is proposed to solve this non-convex problem.Specifically,the fixed non-convex optimization problem is solved by fixing other variables to optimize the UAV trajectory and fixing the UAV trajectory to optimize other variables.The simulation result shows that the proposed scheme is better than the benchmark scheme,which verifies the effectiveness of the algorithm.(2)Considering the scenario where a single UAV with an MEC server provides auxiliary computing services for multiple users on the ground.This research aims to minimize the total system energy consumption by jointly optimizing the UAV trajectory,binary user scheduling variables and offloaddig ratio.For the formed non-convex optimization problem,we use the penalty dual decomposition algorithm to solve it.The equality constraints in the constraints are written to the objective function to solve by introducing the Lagrange multiplier factor and the penalty factor.Simulation results show that the performance of the proposed penalty dual decomposition algorithm is better than the norm algorithm and the performance of the proposed scheme is better than the two benchmark schemes.(3)Considering the scenario where the ground base station with an MEC server provides auxiliary computing services for multiple UAVs in the air.This research aims to minimize the total delay of the UAV performing the computing task by jointly optimizing the trajectory of the unmanned aerial vehicle,the transmission power and the time allocation.In order to solve the formed non-convex problem,we use a multi-variable fixed iterative algorithm to transform the original problem into a convex optimization problem.And we propose an algorithm to get the solution of the problem.The simulation result shows that the obtained solution is still better than the benchmark schemes.