Joint Computation Offloading and Resource Allocation for Mobile-Edge Computing Assisted Ultra-Dense Networks

作     者：Ya Gao Haoran Zhang Fei Yu Yujie Xia Yongpeng Shi 

作者机构：School of Physics and Electronic InformationLuoyang Normal UniversityLuoyang 471934China 

出 版 物：《Journal of Communications and Information Networks》 (通信与信息网络学报（英文）)

年 卷 期：2022年第7卷第1期

页      面：96-106页

核心收录：

学科分类：08[工学] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0713[理学-生态学] 

基　　金：the Natural Science Foundation of Henan(202300410292) the Key Scientific Projects of Henan Higher Education Institutions(19A510018) the Key Scientific Projects of Henan Higher Education Institutions(20A510008) the Key Scientific Projects of Henan Higher Education Institutions(21A510008) the Key Scientific and Technological Projects(202102210120) the Key Scientific and Technological Projects(212102210553) the Foundation for Young Backbone Teachers in Higher Education Institutions(2018GGJS126) Henan key Laboratory for Big Data Processing and Analytics of Electronic Commerce(2020-KF-6)。 

主　　题：mobile-edge computing(MEC) ultra-dense networks(UDN) task offloading power allocation 

摘      要：Mobile-edge computing(MEC),enabling to offload computing tasks on mobile devices towards edge servers,can reduce the terminals cost.However,a single MEC sever usually has limited computing capabilities,which can not meet a large number of terminals’requirements.In this paper,we consider an ultra-dense networks(UDN)scenario where the macro base stations(MBSs)are assisted by MEC severs.In particular,we first construct system model for MEC assisted UDN,and build the system overhead minimization.Next,in order to solve the problem,we transform the problem into three sub-problems,i.e.,offloading strategies subproblem,channel assignments subproblem,and power allocation subproblem.Then,employing joint offloading and resource allocation algorithms,we obtain the optimal joint strategy for the MEC assisted UDNs.Finally,simulations are conducted to evaluate the performance of our proposed algorithms.Numerical results show that obtained algorithms can effectively reduce the energy consumption of the system and improve the overall performance of the system.