Research On Joint Optimization Of Computation Capability And Residual Energy In WPT-MEC System

With the rapid advancement of 5G network and Internet of Things(IoT),a large number of Internet of Things devices such as wearable devices,smart phones,smart wireless sensors,and the amount of data processed by IoT devices has increased dramatically.All of these have put forward higher requirements for the real-time communication and computation capabilities of mobile devices.Some researchers have combined cloud computing with mobile computing to solve the problem of limited mobile device resources.However,the disadvantages of this architecture are high overhead and long backhaul delay.To solve this problem,a computing model called mobile edge computing(MEC)has received wide-spread attention from both industry and academia.The MEC server provides computation and storage resources for mobile devices at the edge of the network,and computation tasks can be offloaded to the edge of the network for execution,which can significantly reduce latency and reduce battery energy consumption.Although MEC provides powerful computation resources for mobile devices,it still faces the challenge of limited battery energy supply.The emergence of wireless power transfer(WPT)technology has effectively solved the problem of insufficient energy supply of the devices.The technology broadcasts energy through radio frequency(RF)signals provided by energy transmitters to provide sustainable and permanent energy supply for mobile devices.The new technology is called wireless powered MEC(WPTMEC).This paper adopts a MEC system that integrates the MEC server,AP and multiuser.AP can deliver energy to users through radio frequency(RF).The user converts the harvested energy,stores it in the battery,and uses the harvested energy to execute corresponding computation-intensive tasks.We achieve data and energy transmission in the same frequency band through Time Division Duplex(TDD)protocol.In this paper,the performance of WPT-MEC system is evaluated from multiple perspectives with average computation capability maximization and residual energy maximization,we study the multi-user dynamic joint optimization under multiple time blocks in the average computation capability maximization problem and the residual energy maximization problem.In this paper,two formal problems are proposed for each of the two research objectives.Under the constraints of computation and energy resources,corresponding resource allocation strategies are proposed,and good experimental results are obtained.First of all,this paper studies the problem of maximizing the average computation capability of wireless-powered mobile edge computing.We have jointly optimized the number of offloaded bits,the number of local computing bits,the local computing frequency,transmission power and energy harvesting time.The primary model is simplified through mathematical analysis and deduction,and the Lagrangian duality method and Karush–Kuhn–Tucker(KKT)conditions are adopted to solve the problem.The simulation results show that the proposed joint optimization scheme can obtain the optimal solution in polynomial time,and the performance outperforms other baseline schemes proposed previously.Insufficient energy supply of mobile devices has been an important challenge in the field of mobile edge computing in recent years,so next we will study the residual energy maximization problem of mobile edge computing with wireless power supply,and measure the performance of the WPT-MEC system from another perspective.We jointly optimize the number of offloaded bits,energy harvesting time,and transmission bandwidth,and design a combined method of semi-closed form and sequential unconstrained minimization based on Reconstruction Linearization Technology(RLT).Firstly,we adopt the RLT technique to transform the non-convex problem into convex problem.Then the problem variables are reduced by Bisection method and semi-closed technology,and finally the combined method of sequential unconstrained minimization is adopted to achieve the solution.The simulation results show that the proposed scheme not only outperforms other baseline schemes,but also proves that our algorithm achieves significant performance improvements,obtains a lower computation complexity,obtains a better solution,and can get the optimal solution.