{"title":"SDMA-OFDM系统中多用户检测的遗传算法实现","authors":"Mohammed Alansi, I. Elshafiey, A. Al-Sanie","doi":"10.1109/ISSPIT.2011.6151580","DOIUrl":null,"url":null,"abstract":"Number of supported users in the orthogonal frequency division multiplexing (OFDM) systems can be increased considerably using powerful multi-user detector (MUD) combined with space division multiple access (SDMA) techniques. This paper presents the results of implementing MUD in SDMA-OFDM systems based on an advanced genetic-algorithm (GA) optimization tool. The hardware implementation is performed using Field Programmable Gate array (FPGA) devices which allow the real time performance of the proposed tool. Results show that the GA scheme enhances the performance and provides BER near to that attained using maximum likelihood (ML) detector at considerably lower computation complexity. Investigation of the GA population size is presented and FPGA implementation is described based on the shared memory approach.","PeriodicalId":288042,"journal":{"name":"2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Genetic algorithm implementation of multi-user detection in SDMA-OFDM systems\",\"authors\":\"Mohammed Alansi, I. Elshafiey, A. Al-Sanie\",\"doi\":\"10.1109/ISSPIT.2011.6151580\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Number of supported users in the orthogonal frequency division multiplexing (OFDM) systems can be increased considerably using powerful multi-user detector (MUD) combined with space division multiple access (SDMA) techniques. This paper presents the results of implementing MUD in SDMA-OFDM systems based on an advanced genetic-algorithm (GA) optimization tool. The hardware implementation is performed using Field Programmable Gate array (FPGA) devices which allow the real time performance of the proposed tool. Results show that the GA scheme enhances the performance and provides BER near to that attained using maximum likelihood (ML) detector at considerably lower computation complexity. Investigation of the GA population size is presented and FPGA implementation is described based on the shared memory approach.\",\"PeriodicalId\":288042,\"journal\":{\"name\":\"2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)\",\"volume\":\"105 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-12-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSPIT.2011.6151580\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSPIT.2011.6151580","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Genetic algorithm implementation of multi-user detection in SDMA-OFDM systems
Number of supported users in the orthogonal frequency division multiplexing (OFDM) systems can be increased considerably using powerful multi-user detector (MUD) combined with space division multiple access (SDMA) techniques. This paper presents the results of implementing MUD in SDMA-OFDM systems based on an advanced genetic-algorithm (GA) optimization tool. The hardware implementation is performed using Field Programmable Gate array (FPGA) devices which allow the real time performance of the proposed tool. Results show that the GA scheme enhances the performance and provides BER near to that attained using maximum likelihood (ML) detector at considerably lower computation complexity. Investigation of the GA population size is presented and FPGA implementation is described based on the shared memory approach.