{"title":"PAPR reduction with phase factors suboptimization for OFDM systems","authors":"Jing Gao, Jinkuan Wang, Bin Wang","doi":"10.1109/ICAL.2010.5585297","DOIUrl":null,"url":null,"abstract":"Partial transmit sequence (PTS) is an effective technique to reduce the peak-to-average power ratio (PAPR) for orthogonal frequency division multiplexing (OFDM) transmitter. However, selecting the optimal parameters for the PTS is very complex since it requires an exhaustive search of all possible weighting factors whose number grows exponentially with the number of subblocks. An improved particle swarm optimization (PSO) based PTS algorithm is proposed to reduce the complexity by choosing the weighting factors suboptimally. The evaluation shows that the proposed algorithm performs very closely to the optimal PTS in many cases with much lower complexity.","PeriodicalId":393739,"journal":{"name":"2010 IEEE International Conference on Automation and Logistics","volume":"209 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE International Conference on Automation and Logistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAL.2010.5585297","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Partial transmit sequence (PTS) is an effective technique to reduce the peak-to-average power ratio (PAPR) for orthogonal frequency division multiplexing (OFDM) transmitter. However, selecting the optimal parameters for the PTS is very complex since it requires an exhaustive search of all possible weighting factors whose number grows exponentially with the number of subblocks. An improved particle swarm optimization (PSO) based PTS algorithm is proposed to reduce the complexity by choosing the weighting factors suboptimally. The evaluation shows that the proposed algorithm performs very closely to the optimal PTS in many cases with much lower complexity.