Joint Optimization Of Resources For Residual Energy Of Multiple Mobile Devices In MEC-WPT System

With the rapid development of mobile devices and mobile applications,the types of mobile devices are becoming more and more abundant,and their performance is constantly enhanced;along with it,mobile applications are becoming more and more diverse,and the iteration speed is steadily increasing.With the rapid development of mobile networks,applications such as virtual reality,webcasting,online games,and video conferencing have emerged,bringing people a rich and colorful daily life.However,these novel applications also occupy most of the computing,storage,network,and battery resources of mobile devices,which place higher requirements on the computing power and energy consumption of mobile devices.The emergence of mobile edge computing provides an effective solution for mobile devices,in which tasks are offloaded to the edge server through computational offloading,and then the very powerful computing power of the edge server is used to make up for the problem that mobile device resources cannot satisfy users.Mobile edge computing is to sink the computing power of the data center to the edge of the network.Mobile devices can interact with edge servers at close range to meet the requirements of mobile devices and mobile applications for low latency and low power consumption.With the technological development of the Internet of Things,artificial intelligence,5G network,big data,and other fields,mobile edge computing will cause extensive research in academia and industry.Mobile edge computing can effectively help mobile devices improve computing capabilities and reduce power consumption.This is because mobile edge computing needs to take into account the close integration of computing and communication resources,and jointly allocate computing and communication resources to achieve an optimal allocation scheme.However,relying solely on the optimal allocation of resources is difficult to meet the needs of existing mobile applications for complex data processing functions,and the continuous use of the battery has a great impact on the user quality of experience.How to provide sustainable,low-cost energy supply for mobile devices is a powerful challenge.The development of radio frequency-based wireless power transmission technology provides a new solution to the problem of insufficient battery power.In this article,our research focuses on combining wireless power transmission technology in mobile edge computing to extend the battery life of mobile devices by maximizing the harvested energy and minimizing energy consumption,which is formulated as a residual energy maximization problem and also a non-convex optimization problem.On the basis of study on maximizing the residual energy under multi-users and multi-time blocks,we propose an effective jointly optimization method(i.e.,jointly optimize the energy harvesting time,task-offloading time,task-offloading size and the mobile devices' CPU frequency),which combines the convex optimization method and the augmented Lagrangian method to solve the residual energy maximum problem.We use time division multiple access and non-orthogonal multiple access modes to coordinate calculation offloading respectively,and compare the two modes of schemes in simulation experiments.The results show that non-orthogonal multiple access is not computationally efficient than time division multiple access under the current communication model.The simulation results show that our proposed scheme has better performance than other benchmark schemes in maximizing the residual energy.In particular,the solution we proposed is excellent in the failure rate of multiple mobile devices,and can be well adapted to the task size to ensure a minimum failure rate.In general,for the research questions raised,this article gives a mobile edge computing framework combined with wireless power supply technology.In the framework,the formalization process of the problem is elaborated,and the system models involved in computing offloading and local computing are analyzed.The simplification and solution process of the formal problem are given accurately and in detail,and the simulation experiment is used to ensure the accuracy and efficiency of the algorithm.