Research On Algorithms Of Wireless Powered Mobile Edge Computing System

The rapid development of the Internet of Things has greatly enhanced the generation of computing intensive applications,such as augmented reality,smart home,and online games.In order to support resource constrained mobile devices,mobile edge computing(MEC)technology with ultra-low latency and ultra-high bandwidth has received widespread attention in recent years.It sinks cloud services close to the edge of mobile devices,greatly improving the service experience of users.In addition to limited computing resources,mobile devices also face the challenge of limited battery capacity.To overcome this bottleneck,wireless power transfer(WPT)technology emerged,which can continuously provide stable energy to devices with limited power using radio frequency signals.In addition,unmanned aerial vehicle(UAV)is widely used in wireless powered MEC communication networks due to its mobility and cost advantages.The mobile edge system based on WPT-UAV can effectively utilize the powerful computing resources of the server to complete efficient calculation of input data.With the development of information technology and the continuous growth of energy consumption,the development of green communication has become the main topic of wireless power MEC systems based on UAVs.However,this system is susceptible to the "double far near" effect.Due to the long distance loss of equipment that is farther away from the energy transmitter,it collects less energy and transmits less information,inevitably resulting in a waste of system resources and low energy utilization.Therefore,this article proposes a mobile device collaborative offloading technology,in which devices closer to the UAV can act as relays to transmit offload data from themselves and devices farther away from the energy transmitter to the UAV.This algorithm can not only improve the number of information transmission rates of mobile devices,effectively utilize resources,but also minimize the energy transmission of UAVs,reducing system energy consumption.The main work and research results of this article are as follows:1.In order to achieve the goal of green communication,we propose a mobile edge cooperative offloading algorithm based on wireless power supply.The algorithm is mainly implemented in the following three stages.In the first stage,the UAV transmits energy to two mobile devices through the WPT;In the second stage,devices that are farther away from the UAV use the collected energy to send their computing tasks that need to be unloaded to devices that are closer to the UAV;The equipment closer to the UAV allocates its own energy in the third stage,and sends unloading tasks to the UAV for itself and the equipment farther away from the UAV.After that,the transmission power of the two mobile devices and the power division factor of the closer devices are jointly optimized to achieve the calculation of mobile device input tasks.Through the comparative analysis of simulation experiments,the effectiveness of the algorithm proposed in this paper is verified.Both the resource utilization efficiency of mobile devices and the energy transmission efficiency of wireless powered mobile edge computing systems have been significantly improved.2.Due to the fact that devices closer to the UAV use the same bandwidth to transmit their own and remote device’s offload tasks,inevitably generating signal interference that affects data transmission and energy utilization.Therefore,we propose a mobile edge subcarrier power optimization algorithm based on wireless power supply.The algorithm is mainly implemented in the following three stages.In the first stage,the UAV transmits energy to two mobile devices through the WPT;Devices that are farther away from the UAV use the collected energy to send their partial offload tasks to devices that are closer to the UAV in the second stage;Devices that are closer to the UAV allocate subcarriers in the third stage,respectively completing data task offloading for themselves and for devices that are farther away.After that,the transmission power of the two mobile devices is jointly optimized,and the optimal solution to achieve the minimum transmission of UAV energy is obtained.Due to the different subcarriers used to transmit the unloading data tasks of two mobile devices,mutual interference can be effectively resolved.Through the comparative analysis of simulation experiments,the effectiveness of the algorithm proposed in this paper is verified.Both the information transmission rate of mobile devices and the minimum transmission power of UAVs in wireless powered mobile edge computing systems are greatly improved compared with those before the improvement.