{"title":"边缘和中心云计算与无线MIMO回程的协同作用","authors":"Xiaoyan Hu, Lifeng Wang, Kai‐Kit Wong, M. Tao, Yangyang Zhang, Zhongbin Zheng","doi":"10.1109/GLOBECOM38437.2019.9014044","DOIUrl":null,"url":null,"abstract":"In this paper, the synergy of combining the edge and central cloud computing is studied in heterogeneous cellular networks (HetNets). Multi-antenna small base stations (SBSs) equipped with edge cloud servers offer computing services for user equipment (UEs) proximally, whereas a macro base station (MBS) provides central cloud computing services for UEs via wireless multiple-input multiple-output (MIMO) backhaul allocated to their associated SBSs. With task processing latency constraints for UEs, the network energy consumption is minimized through jointly optimizing the cloud selection, the UEs' transmit powers, the SBSs' receive beamformers, and the SBSs' transmit covariance matrices. A mixed integer and non-convex optimization problem is formulated, and a decomposition algorithm is proposed to obtain a tractable solution iteratively. The simulation results confirm that great performance improvement can be achieved compared with the traditional scheme with central cloud computing only.","PeriodicalId":6868,"journal":{"name":"2019 IEEE Global Communications Conference (GLOBECOM)","volume":"4 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"The Synergy of Edge and Central Cloud Computing with Wireless MIMO Backhaul\",\"authors\":\"Xiaoyan Hu, Lifeng Wang, Kai‐Kit Wong, M. Tao, Yangyang Zhang, Zhongbin Zheng\",\"doi\":\"10.1109/GLOBECOM38437.2019.9014044\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, the synergy of combining the edge and central cloud computing is studied in heterogeneous cellular networks (HetNets). Multi-antenna small base stations (SBSs) equipped with edge cloud servers offer computing services for user equipment (UEs) proximally, whereas a macro base station (MBS) provides central cloud computing services for UEs via wireless multiple-input multiple-output (MIMO) backhaul allocated to their associated SBSs. With task processing latency constraints for UEs, the network energy consumption is minimized through jointly optimizing the cloud selection, the UEs' transmit powers, the SBSs' receive beamformers, and the SBSs' transmit covariance matrices. A mixed integer and non-convex optimization problem is formulated, and a decomposition algorithm is proposed to obtain a tractable solution iteratively. The simulation results confirm that great performance improvement can be achieved compared with the traditional scheme with central cloud computing only.\",\"PeriodicalId\":6868,\"journal\":{\"name\":\"2019 IEEE Global Communications Conference (GLOBECOM)\",\"volume\":\"4 1\",\"pages\":\"1-6\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE Global Communications Conference (GLOBECOM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GLOBECOM38437.2019.9014044\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE Global Communications Conference (GLOBECOM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GLOBECOM38437.2019.9014044","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Synergy of Edge and Central Cloud Computing with Wireless MIMO Backhaul
In this paper, the synergy of combining the edge and central cloud computing is studied in heterogeneous cellular networks (HetNets). Multi-antenna small base stations (SBSs) equipped with edge cloud servers offer computing services for user equipment (UEs) proximally, whereas a macro base station (MBS) provides central cloud computing services for UEs via wireless multiple-input multiple-output (MIMO) backhaul allocated to their associated SBSs. With task processing latency constraints for UEs, the network energy consumption is minimized through jointly optimizing the cloud selection, the UEs' transmit powers, the SBSs' receive beamformers, and the SBSs' transmit covariance matrices. A mixed integer and non-convex optimization problem is formulated, and a decomposition algorithm is proposed to obtain a tractable solution iteratively. The simulation results confirm that great performance improvement can be achieved compared with the traditional scheme with central cloud computing only.