{"title":"基于特征值的认知无线电多目标仿真分析","authors":"Amit Khandelwal, Chhagan Charan","doi":"10.1109/ETCT.2016.7882959","DOIUrl":null,"url":null,"abstract":"In cognitive radio spectrum sensing is a fundamental problem. Under the case of uncorrelated noise different methods are used for spectrum sensing. In this paper we compare the performance of MP and eigenvalue based methods. In eigenvalue based method we find the threshold using random matrix theory (RMT). Marchenko-Pastur (MP) method use standard condition number (SCN) to find the threshold. Here we find the SCN by using eigenvalue parameters. Simulation shows that MP Law statistics improve the sensing performance.","PeriodicalId":340007,"journal":{"name":"2016 International Conference on Emerging Trends in Communication Technologies (ETCT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Simulation analysis of MP and eigenvalue based method for cognitive radio\",\"authors\":\"Amit Khandelwal, Chhagan Charan\",\"doi\":\"10.1109/ETCT.2016.7882959\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In cognitive radio spectrum sensing is a fundamental problem. Under the case of uncorrelated noise different methods are used for spectrum sensing. In this paper we compare the performance of MP and eigenvalue based methods. In eigenvalue based method we find the threshold using random matrix theory (RMT). Marchenko-Pastur (MP) method use standard condition number (SCN) to find the threshold. Here we find the SCN by using eigenvalue parameters. Simulation shows that MP Law statistics improve the sensing performance.\",\"PeriodicalId\":340007,\"journal\":{\"name\":\"2016 International Conference on Emerging Trends in Communication Technologies (ETCT)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Conference on Emerging Trends in Communication Technologies (ETCT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ETCT.2016.7882959\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Emerging Trends in Communication Technologies (ETCT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ETCT.2016.7882959","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Simulation analysis of MP and eigenvalue based method for cognitive radio
In cognitive radio spectrum sensing is a fundamental problem. Under the case of uncorrelated noise different methods are used for spectrum sensing. In this paper we compare the performance of MP and eigenvalue based methods. In eigenvalue based method we find the threshold using random matrix theory (RMT). Marchenko-Pastur (MP) method use standard condition number (SCN) to find the threshold. Here we find the SCN by using eigenvalue parameters. Simulation shows that MP Law statistics improve the sensing performance.