UDN中MEC的资源分配和任务卸载联合优化  

Resource Allocation and Task Offloading Joint Optimization for MEC in UDN

作　　者：魏明亮 耿绥燕 赵雄文[1,2] 胡玮 范静怡 WEI Mingliang;GENG Suiyan;ZHAO Xiongwen;HU Wei;FAN Jingyi(State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources,North China Electric Power University,Beijing 102206,China;Hebei Key Laboratory of Power Internet of Things Technology,North China Electric Power University,Baoding 071003,China)

机构地区：[1]华北电力大学新能源电力系统国家重点实验室,北京102206 [2]华北电力大学河北省电力物联网技术重点实验室,保定071003

出　　处：《北京邮电大学学报》2023年第2期50-56,共7页Journal of Beijing University of Posts and Telecommunications

基　　金：国家自然科学基金项目(61931001,61771194)。

摘　　要：在移动边缘计算(MEC)场景下的超密集网络(UDN)中,基础设施的密集部署会产生严重的信道干扰问题。对此,将优化资源分配和任务卸载联合,可使任务计算成本最小化,并降低系统开销。由于决策变量耦合复杂,将原始问题拆分为资源分配和任务卸载子问题,提出了一种结合凸函数和自适应粒子群(CF-APSO)的优化算法。仿真实验结果表明,在微基站密集部署环境中采用CF-APSO算法可以大幅减少时延和能耗,有效提升系统的性能。针对UDN-MEC网络中联合优化多用户资源分配和任务卸载的问题,可通过降低计算时间和能耗使任务的计算成本最低。In the mobile edge computing(MEC)scenario of the ultra-dense network(UDN),the intensive deployment of network infrastructures will cause serious channel interference problems.Therefore,joint optimization of resource allocation and task offloading is considered to minimize the task computing costs and reduce the system overhead.Due to the complex coupling of decision variables,the original problem is decomposed into a resource allocation subproblem and a task offloading subproblem.An optimization algorithm combining convex function adaptive particle swarm optimization is further proposed.The simulation results show that in micro base stations'dense deployment environment,the proposed algorithm can significantly reduce the delay and energy consumption and improve the system performance effectively.

关 键 词：移动边缘计算 超密集组网 资源分配 任务卸载 

分 类 号：TN929.5[电子电信—通信与信息系统]