Research on APSO-based Task Offloading in Multi-user MIMO-MEC Networks
The incorporation of Multiple Input Multiple Output(MIMO)and data compression into the Mobile Edge Computing(MEC)system can reduce data redundancy and improve data transmission rate,so that the task execution latency and energy consumption can be reduced.For multi-user MIMO-MEC networks with data compression,the problem of computation task offloading is studied The total delay of the system can be minimized by jointly optimizing the user's task offloading ratio,data compression ratio,transmission power,computational frequency and channel bandwidth.Firstly,the problem is formulated as a non-convex optimization problem under the constraints of energy consumption,power and bandwidth,etc.Then,due to the complexity of the energy constraint,a penalty function is constructed to incorporate the constraint into the objective function.Thus,a relatively simple equivalent problem is obtained.Then,viewing all optimization variables as one particle,a multi-user task offloading method is proposed based on the Adaptive Particle Swarm Optimization(APSO)framework.Because the updating of particles may violate the constraint conditions,the proposed method deals with the out-of-bounds situation specially.The method can adjust the inertia weight adaptively to improve the searching ability and convergence,and finally obtain the optimal or suboptimal solution by iterations.Simulation experiments are conducted to evaluate the performance of the proposed offloading method.The effects of the number of users,task calculation intensity,etc.on the system performance are analyzed.The results show that,the proposed method is superior to a few benchmark schemes,e.g.the local computation and conventional Particle Swarm Optimization(PSO),and can effectively reduce the task execution delay of the system.
万方数据
维普
