支持MEC的D2D多播网络中的任务卸载与资源分配  

Task Offloading and Resource Allocation for D2D Multicast Networks with MEC-Enable

作　　者：陈雷[1] CHEN Lei(College of Public Security Information Technology and Intelligence,Criminal Investigation Police University of China,Shenyang 110854,Liaoning,China)

机构地区：[1]中国刑事警察学院公安信息技术与情报学院,辽宁沈阳110854

出　　处：《武汉大学学报（理学版）》2023年第6期827-836,共10页Journal of Wuhan University:Natural Science Edition

基　　金：辽宁省自然科学基金(20180550046);辽宁省教育厅科学研究项目(ZGXJ2020005);辽宁省社会科学基金(L20BGL008);2023年度中国刑事警察学院重大培育项目(D2023002)。

摘　　要：在支持移动边缘计算(mobile edge computing,MEC)的D2D(device-to-device)多播网络中,考虑到资源受限时的任务卸载与资源分配问题。首先,提出联合用户社会属性、可用能量和传输速率的D2D多播簇首选择策略和分簇策略。其次,在考虑用户选择、任务卸载和资源分配的条件下,将最大化用户的收益作为最优化问题进行了建模。为了求解最优化问题,将其分解为用户选择最优化(user selection optimization,USO)和资源分配最优化(resource allocation optimization,RAO)两个子问题,并采用贪婪算法对USO问题进行求解,采用拉格朗日乘数法得到RAO问题的最优解。通过仿真实验表明了本文提出的算法与其他算法相比,能有效提升用户的收益。In this paper,we investigate the task offloading and resource allocation in mobile edge computing(MEC)for enabling deviceto-device(D2D)multicast networks,exceptionally when the resources are constrained.Firstly,we propose a D2D multicast cluster head selection strategy and a clustering strategy that combines user social attributes,available energy,and transmission rate.Subsequently,a maximization optimization problem of users’revenues is formulated,in which user association,computation offloading strategy policy,and computation resource scheduling are all considered.Furthermore,we transform this optimization problem into two distinct sub-problems:the user selection optimization(USO)problem and resource allocation optimization(RAO)problem.The greedy algorithm is used to solve the USO problem,and the Lagrange multiplier method is used to get the optimal solution to the RAO problem.In conclusion,the simulation results show that our proposed schemes can effectively increase the users’revenues in comparison to other algorithms.

关 键 词：移动边缘计算 任务卸载 资源分配 D2D多播 

分 类 号：TN929.53[电子电信—通信与信息系统]