Research On Resource Allocation And Task Offloading Scheme In 5G Cellular Heterogeneous Networks

With the explosive growth of smart devices,the demand for data traffic has increased dramatically,resulting in a higher demand for network throughput.Cellular heterogeneous networks consist of small cells and macro cells can improve spectrum utilization and increase network throughput.Combining cognitive radio technology with small cells to form cognitive small cellular networks can effectively solve the interference problem and further improve spectrum utilization.In addition,5G network will deploy a large number of edge clouds,and the tasks of user equipment can offload to the edge cloud server for computing to ensure low latency and high data rate requirements.This thesis mainly focuses on the interference management,the spectrum resource allocation and the computing resource allocation in 5G cellular heterogeneous networks.This thesis studies the interference management and the spectrum resource allocation problem in cognitive small cell networks.Firstly,the associated range of the channel reuse radius could be obtained with a given outage probability threshold.Secondly,in order to guarantee the fairness spectrum resource allocation,a fairness based distributed spectrum resource allocation algorithm is proposed,which preferentially allocate spectrum resource to the base station with lower satisfaction degree and higher traffic requirement level.At the same time,in order to maximize the network throughput,the channel with better channel quality is preferentially assigned to its corresponding base station.Finally,considering the tidal effect in communication,the base station in the hot spot area could reuse part of spectrum resources to further improve the throughput.Simulation results demonstrate that the proposed algorithm could significantly improve the network throughput,guarantee the average outage probability of the network,while ensuring the fairness resource allocation between base stations.Based on the above results,in order to minimize the total system cost(including task computation time cost and energy cost),a joint optimization task offloading and resource allocation scheme is proposed based on the mobile edge computing.The task offloading decision,resource block allocation and computing resource allocation are jointly optimized to minimize the system cost.The original nonconvex optimization problem is decomposed into three suboptimal problem.Firstly,considering the different delay sensitivity of user equipment in the real network,a user equipment delay priority based distributed resource block allocation is proposed,which divides the user equipment into different priorities according to their delay sensitivity.The user equipment with highpriority could obtain resource blocks preferentially,while the communication quality of low-priority user equipments could be ensured.Secondly,each user equipment makes offloading decision based on the task local computing cost and offloading cost.Finally,the computing resource is allocated to the offloading user equipment with the goal of minimizing the total computing time of the total offloading task in the mobile edge computing server.Theoretical analysis and simulation results demonstrate that the proposed algorithm can effectively reduce the total system cost with low complexity.