{"title":"Energy-Efficient Resource Allocation for Wireless Powered Cognitive Mobile Edge Computing","authors":"Boyang Liu, Jing Bai, Yujiao Ma, Jin Wang, G. Lu","doi":"10.1109/ICCW.2019.8756664","DOIUrl":null,"url":null,"abstract":"In this paper, a framework for mobile edge computing (MEC) in cognitive radio (CR) networks is proposed, which integrates three technologies: MEC, cooperative relaying and wireless power transfer (WPT). The purpose of this paper is to minimize the energy cost of the WD in both partial offloading and local computing scenarios. A two-phase algorithm is proposed to solve the optimization problems. Semi-closed and closed-form solutions are derived by using Lagrangian dual decomposition and successive pseudo-convex approximation (SPCA) algorithm. Simulation results show the effects of the different parameters on the system performance and demonstrate the validity of our proposed algorithm.","PeriodicalId":426086,"journal":{"name":"2019 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Conference on Communications Workshops (ICC Workshops)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCW.2019.8756664","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this paper, a framework for mobile edge computing (MEC) in cognitive radio (CR) networks is proposed, which integrates three technologies: MEC, cooperative relaying and wireless power transfer (WPT). The purpose of this paper is to minimize the energy cost of the WD in both partial offloading and local computing scenarios. A two-phase algorithm is proposed to solve the optimization problems. Semi-closed and closed-form solutions are derived by using Lagrangian dual decomposition and successive pseudo-convex approximation (SPCA) algorithm. Simulation results show the effects of the different parameters on the system performance and demonstrate the validity of our proposed algorithm.