{"title":"Rate adaptive resource allocation for multiuser OFDM using NSGA - II","authors":"N. Sharma, A. Rao, A. Dewan, M. Safdari","doi":"10.1109/WCSN.2008.4772703","DOIUrl":null,"url":null,"abstract":"This paper presents a new rate adaptive resource allocation technique for multiuser orthogonal frequency division multiplexing (OFDM) systems. We optimize both bit and subcarrier allocation by considering rate maximization and total power constraint satisfaction. We solve them effectively by combining them into a multi-objective optimization problem. We propose using a non dominated sort genetic algorithm (NSGA-II) - a multi-objective optimization using genetic algorithm. Instead of combining many conflicting objectives into a single function, the NSGA-II uses multiple objective optimizations and brings out solutions which provide a better trade-off taking all conditions into consideration. The simulation results and their marked improvement over previous algorithms provide the basis to this.","PeriodicalId":338962,"journal":{"name":"2008 Fourth International Conference on Wireless Communication and Sensor Networks","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Fourth International Conference on Wireless Communication and Sensor Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WCSN.2008.4772703","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 18
Abstract
This paper presents a new rate adaptive resource allocation technique for multiuser orthogonal frequency division multiplexing (OFDM) systems. We optimize both bit and subcarrier allocation by considering rate maximization and total power constraint satisfaction. We solve them effectively by combining them into a multi-objective optimization problem. We propose using a non dominated sort genetic algorithm (NSGA-II) - a multi-objective optimization using genetic algorithm. Instead of combining many conflicting objectives into a single function, the NSGA-II uses multiple objective optimizations and brings out solutions which provide a better trade-off taking all conditions into consideration. The simulation results and their marked improvement over previous algorithms provide the basis to this.