{"title":"Distributed discrete resource optimization in heterogeneous networks","authors":"C. Gaie, M. Assaad, M. Muck, P. Duhamel","doi":"10.1109/SPAWC.2008.4641670","DOIUrl":null,"url":null,"abstract":"Nowadays, the emergence of many radio technologies has increased the research interest towards Radio Resources Management (RRM) in heterogeneous systems. In this context, we seek a distributed scheme for discrete resource allocation. The algorithm proposed should reduce computation and signalling overhead, compared to centralized optimization or distributed solutions based on Game Theory. Therefore, the solution proposed here consists in splitting the resource allocation problem into two parts. First, Users and Base Stations negotiate a mean allocation using a Multisystem Minimum Mean Rate Scheduling (M3RS) algorithm presented here. Then, Base Stations allocate power independantly and instantaneously in order to cope with changing radio conditions. This problem is widely known in literature and is not developped here.","PeriodicalId":197154,"journal":{"name":"2008 IEEE 9th Workshop on Signal Processing Advances in Wireless Communications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE 9th Workshop on Signal Processing Advances in Wireless Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPAWC.2008.4641670","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Nowadays, the emergence of many radio technologies has increased the research interest towards Radio Resources Management (RRM) in heterogeneous systems. In this context, we seek a distributed scheme for discrete resource allocation. The algorithm proposed should reduce computation and signalling overhead, compared to centralized optimization or distributed solutions based on Game Theory. Therefore, the solution proposed here consists in splitting the resource allocation problem into two parts. First, Users and Base Stations negotiate a mean allocation using a Multisystem Minimum Mean Rate Scheduling (M3RS) algorithm presented here. Then, Base Stations allocate power independantly and instantaneously in order to cope with changing radio conditions. This problem is widely known in literature and is not developped here.