Investigation On Joint Communication And Computation Optimization For Wireless Powered Mobile Edge Computing

At present,the world is accelerating into the Internet of Things era where all things are connected,tens of billions of wireless devices are connected to the network,forming massive amounts of data.A number of new Io T applications such as augmented/virtual reality,smart home,smart city,self-driving,and Internet of vehicles have emerged.The successful realization of these applications requires high-speed communication,low-latency computing,and continuous power supply for a large number of low-power wireless user equipment.Therefore,how to provide sustainable energy supply and low-latency computing capabilities for wireless devices is a key issue that needs to be solved urgently in the industry and academia,and has very important practical significance and theoretical value.In this context,this article mainly studies the wireless power edge computing system that combines mobile edge computing(MEC)and wireless power transfer(WPT)technology.The system can effectively solve the problems of massive low-power equipment energy supply and low-latency calculations.On the one hand,by offloading computing tasks from mobile devices to MEC servers,this systems can greatly improve the quality of user device computing experience,reduce device energy consumption and execution waiting time.On the other hand,wireless power transfer can make user no longer limited to battery or wired charging,avoiding expensive labor and wiring costs.Within a certain electromagnetic wave far field,the receiver is used to receive electromagnetic wave energy and store energy in the battery.Specifically,the main research content of this article is described as follows:(1)First,this article studies the single-user collaborative computing system of the wireless energy-supplied edge computing system.Consider a multi-user collaborative edge computing system based on wireless energy transfer.The system includes a wireless access point integrated with a multi-antenna wireless power transmitter(ET),a single computing user and several helpers.Specifically,in this system,the wireless access point uses beamforming technology to transmit wireless energy to the user equipment to perform wireless charging for user and helpers.By using the harvested energy,user send its computing tasks to neighboring helpers through the device-to-device(D2D)wireless communication link,which assists in the calculation.Fix the allowed time budget of the calculation task.It can be seen that all user equipment is subject to energy causal constraints,that is,its energy consumption for calculation and transmission does not exceed the corresponding energy collected from ET.Therefore,this article jointly optimizes the design of the ET transmission energy beam,the user and helpers’ own computing and communication resources,and maximizes the achivable computing tasks(computation bits calculated in a given time).Using the Lagrangian duality method,this thesis derives and analyzes the optimal closed-form solution of the problem.The simulation results show that,compared with the traditional scheme without such cooperation,the proposed user-cooperative computer mechanism can significantly improve the computing performance of the system.(2)Secondly,this article studies the multi-user collaborative computing system of wireless energy-supplied edge computing system.Consider the wireless power edge computing system of multiple computing users,assisting users,and multiple energy transmitters.In particular,the more difficult problems to be solved in this article are: the matching between the users and the helpers(when there are multiple users,how to determine the assisting object of the helper),the bandwidth allocation between multiple groups of users and helpers,and energy beam coordination of multiple energy transmitters,etc.At the same time,it provides users with more accurate and effective solutions to enhance the computing experience.The paper proves that the computation bits maximization problem is a non-convex mixed integer programming optimization problem,and the algorithm of the global optimal solution has exponential computational complexity.To this end,the article proposes an alternate iterative optimization method to calculate an algorithm scheme with low computational complexity.On the one hand,combined with the resource optimization configuration structure of single-user collaborative computing in the first part of the paper,the alternate iterative optimization method is applied to obtain the sub-optimal solution of the multi-user computing bit maximization problem.On the other hand,in order to further reduce the degree of computational responsibility,this paper adopts the Greedy algorithm,combined with the resource optimization configuration structure of single-user collaborative computing in the first part of the paper.Finally,the simulation results show the relationship between the system’s computing performance and the number of users,user channel status,user distribution status,etc.,and verify the performance gain of the joint optimization algorithm proposed in this paper.