{"title":"认知无线网络跨层优化的模糊逻辑","authors":"N. Baldo, M. Zorzi","doi":"10.1109/MCOM.2008.4481342","DOIUrl":null,"url":null,"abstract":"The search for the ultimate architecture for cross-layer optimization in cognitive radio networks is characterized by challenges such as modularity, interpretability, imprecision, scalability, and complexity constraints. In this article we propose fuzzy logic as an effective means of meeting these challenges, as far as both knowledge representation and control implementation are concerned.","PeriodicalId":166361,"journal":{"name":"2007 4th IEEE Consumer Communications and Networking Conference","volume":"79 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"112","resultStr":"{\"title\":\"Fuzzy Logic for Cross-layer Optimization in Cognitive Radio Networks\",\"authors\":\"N. Baldo, M. Zorzi\",\"doi\":\"10.1109/MCOM.2008.4481342\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The search for the ultimate architecture for cross-layer optimization in cognitive radio networks is characterized by challenges such as modularity, interpretability, imprecision, scalability, and complexity constraints. In this article we propose fuzzy logic as an effective means of meeting these challenges, as far as both knowledge representation and control implementation are concerned.\",\"PeriodicalId\":166361,\"journal\":{\"name\":\"2007 4th IEEE Consumer Communications and Networking Conference\",\"volume\":\"79 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"112\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 4th IEEE Consumer Communications and Networking Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MCOM.2008.4481342\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 4th IEEE Consumer Communications and Networking Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MCOM.2008.4481342","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fuzzy Logic for Cross-layer Optimization in Cognitive Radio Networks
The search for the ultimate architecture for cross-layer optimization in cognitive radio networks is characterized by challenges such as modularity, interpretability, imprecision, scalability, and complexity constraints. In this article we propose fuzzy logic as an effective means of meeting these challenges, as far as both knowledge representation and control implementation are concerned.
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