{"title":"A novel OVSF-based multi-rate “OCDMA-PON and its optimal configuration”","authors":"Yih-Fuh Wang","doi":"10.1109/ISIC.2012.6449725","DOIUrl":null,"url":null,"abstract":"A flexible optical metropolitan-area network (OMAN) structured with code division multiple access-passive optical network (CDMA-PON) linkage is proposed to support multimedia services with multi-rate or various quality of service (QoS) by equipped OCDMA-OVSF code. For the optimally lightpath placement, a GMGA (Genetically Modified Genetic Algorithm) [1] scheme is involved and a MGMGA (Modified GMGA) is developed to quickly process the optimization of configuration. For evaluating performance, we compared GMGA and MGMGA with simple genetic algorithm (SGA) as running placement. Simulation results reveal that the performance of both GMGA and MGMGA can converge to near optimal solutions. However, MGMGA is very efficient even though it is not faster than SGA.","PeriodicalId":393653,"journal":{"name":"2012 International Conference on Information Security and Intelligent Control","volume":"71 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Conference on Information Security and Intelligent Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISIC.2012.6449725","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
A flexible optical metropolitan-area network (OMAN) structured with code division multiple access-passive optical network (CDMA-PON) linkage is proposed to support multimedia services with multi-rate or various quality of service (QoS) by equipped OCDMA-OVSF code. For the optimally lightpath placement, a GMGA (Genetically Modified Genetic Algorithm) [1] scheme is involved and a MGMGA (Modified GMGA) is developed to quickly process the optimization of configuration. For evaluating performance, we compared GMGA and MGMGA with simple genetic algorithm (SGA) as running placement. Simulation results reveal that the performance of both GMGA and MGMGA can converge to near optimal solutions. However, MGMGA is very efficient even though it is not faster than SGA.