{"title":"Recent Advances on Artificial Intelligence in Cognitive Radio Networks","authors":"B. Benmammar","doi":"10.4018/ijwnbt.2020010102","DOIUrl":null,"url":null,"abstract":"Cognitive radio is a form of wireless communication that makes decisions about allocating and managing radio resources after detecting its environment and analyzing the parameters of its radio frequency environment. Decision making in cognitive radio can be based on optimization techniques. In this context, machine learning and artificial intelligence are to be used in cognitive radio networks in order to reduce complexity, obtain resource allocation in a reasonable time and improve the user's quality of service. This article presents recent advances on artificial intelligence in cognitive radio networks. The article also categorizes the techniques presented according to the type of learning—supervised or unsupervised—and presents their applications and challenges according to the tasks of the cognitive radio.","PeriodicalId":422249,"journal":{"name":"Int. J. Wirel. Networks Broadband Technol.","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Wirel. Networks Broadband Technol.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijwnbt.2020010102","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Cognitive radio is a form of wireless communication that makes decisions about allocating and managing radio resources after detecting its environment and analyzing the parameters of its radio frequency environment. Decision making in cognitive radio can be based on optimization techniques. In this context, machine learning and artificial intelligence are to be used in cognitive radio networks in order to reduce complexity, obtain resource allocation in a reasonable time and improve the user's quality of service. This article presents recent advances on artificial intelligence in cognitive radio networks. The article also categorizes the techniques presented according to the type of learning—supervised or unsupervised—and presents their applications and challenges according to the tasks of the cognitive radio.