| The Internet of Things(Io T)is an indispensable and important technology in today’s era,and it is also one of the key technologies for the future development of 5G wireless communication.Its development has promoted the new development of many industries.But meanwhile,massive intelligent sensor nodes have been deployed and many latency-sensitive and computation-intensive applications or services are generated at these nodes.As one of the emerging technique for Io T,mobile edge computing(MEC)is promising to address this issue by enabling the Io T nodes to offload their computation tasks to servers with adequate computing resources.Intelligent reflecting surface(IRS)is a cost-effective and energyefficient technique that can achieve high spectral efficiency in wireless communications.Wireless-powered communication technology is a way which supplies radio frequency power for wireless nodes with long-distance,which can effectively resolve power supply problem of IRS deployments.Based on the wireless-powered IRS-assisted mobile edge computing systems,this thesis studies the relationship among system model,joint of energy and task scheduling scheme,energy consumption and so on.Firstly,considering the temporal causality of energy and task queues,for joint optimization of energy and task scheduling in wireless-powered IRS-assisted mobile edge computing systems based on dynamic programming theory.The goal is to minimize the computing and unloading energy consumption of the client by combining the energy collected by the intelligent reflector with the computing task scheduling generated by the client.In order for the IRS to increase the amount of energy collected and use the collected energy efficiently,we first propose a novel MEC protocolin which the system is enabled to operate in three modes,namely an EH mode,an IRS-assisted task offloading mode,and an IRS-inactive task offloading mode,so that the energy at IRS and the tasks generated at user can be flexibly scheduled within a finite time horizon,depending on channel conditions,IRS energy states and user ’ s task queue states.Under this protocol,considering the task execution delay constraints in a finite long time,the corresponding system optimization problem is established.Due to the randomness of wireless channels and task arrivals,the optimization problem is a stochastic programming.To solve this problem,we first transform it into a deterministic one by assuming that noncausal channel state information(CSI)and task state information(TSI)are available,and then derive a practical algorithm where only causal CSI and TSI are required.Simulation results verify that our proposed design can save at most 80% energy consumption as compared with existing baseline schemes.Secondly,considering the stability of IRS rechargeable battery energy queue and user side task queue,wireless powered IRS-assisted mobile edge computing systems were further studied based on Lyapunov optimization theory.Then,based on the above proposed protocol,an optimization problem is formulated,which aims at minimizing the amount of consumed energy at the user by optimizing the selection of system operation mode and the resource allocation in each mode.Lyapunov optimization framework is employed to solve the problem to achieve a low-complexity and efficient optimization algorithm.Simulation results show that the proposed design can save 50 to 90 percent of energy consumption for the MEC system as compared with the existing baseline schemes. |
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