{"title":"Scaled offset PSO based PTS for PAPR reduction in OFDM systems","authors":"S. Prasad, R. Scholar, Ramesh Jayabalan","doi":"10.1109/UEMCON.2017.8248982","DOIUrl":null,"url":null,"abstract":"In this paper, an improved scheme called Scaled Offset Particle Swarm Optimization based Partial Transmit Sequence (SOPSO-PTS) for reducing Peak-to-Average Power Ratio (PAPR) in wireless communications is presented. The main focus of this paper is to reduce PAPR as well as the computational complexity. The proposed scheme SOPSO-PTS has salient features such as faster convergence to the optimum value and provides a good control mechanism to the particle's velocity which makes it unique from the other conventional PSO-PTS schemes. The proposed scheme performs better when compared to Simulated Annealing (SA) PTS and Particle Swarm Optimization (PSO) PTS techniques.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UEMCON.2017.8248982","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, an improved scheme called Scaled Offset Particle Swarm Optimization based Partial Transmit Sequence (SOPSO-PTS) for reducing Peak-to-Average Power Ratio (PAPR) in wireless communications is presented. The main focus of this paper is to reduce PAPR as well as the computational complexity. The proposed scheme SOPSO-PTS has salient features such as faster convergence to the optimum value and provides a good control mechanism to the particle's velocity which makes it unique from the other conventional PSO-PTS schemes. The proposed scheme performs better when compared to Simulated Annealing (SA) PTS and Particle Swarm Optimization (PSO) PTS techniques.