{"title":"认知物联网中协同频谱感知优化的深度学习","authors":"Hind Boukhairat, M. Koulali","doi":"10.1109/ISCC55528.2022.9912823","DOIUrl":null,"url":null,"abstract":"Spectrum sensing is a critical component of Cognitive Internet of Things. It allows Secondary Users(SUs) to access underutilized frequency bands licensed to Primary Users (PUs) opportunistically without causing harmful interference to them. How-ever, accurate individual spectrum sensing solutions are complex to deploy. Thus, Cooperative Spectrum Sensing (CSS) techniques have flourished. These techniques combine individual sensing through a weighting mechanism at a fusion center to assess the channel status. The fusion process depends heavily on the indi-vidual detection thresholds at each SU and the weights attributed to their sensing results by the Fusion Center. In this paper, we propose to use Deep Neural Net-work to compute the optimal energy detection thresh-old and fusion weights. Our goal is to develop a solution that optimally adapts to the time-varying wireless channel conditions. Furthermore, our DNN-based so-lution eliminates the need to solve hard optimization problems, thus significantly reducing computational complexity, especially in large networks.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep-Learning for Cooperative Spectrum Sensing Optimization in Cognitive Internet of Things\",\"authors\":\"Hind Boukhairat, M. Koulali\",\"doi\":\"10.1109/ISCC55528.2022.9912823\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Spectrum sensing is a critical component of Cognitive Internet of Things. It allows Secondary Users(SUs) to access underutilized frequency bands licensed to Primary Users (PUs) opportunistically without causing harmful interference to them. How-ever, accurate individual spectrum sensing solutions are complex to deploy. Thus, Cooperative Spectrum Sensing (CSS) techniques have flourished. These techniques combine individual sensing through a weighting mechanism at a fusion center to assess the channel status. The fusion process depends heavily on the indi-vidual detection thresholds at each SU and the weights attributed to their sensing results by the Fusion Center. In this paper, we propose to use Deep Neural Net-work to compute the optimal energy detection thresh-old and fusion weights. Our goal is to develop a solution that optimally adapts to the time-varying wireless channel conditions. Furthermore, our DNN-based so-lution eliminates the need to solve hard optimization problems, thus significantly reducing computational complexity, especially in large networks.\",\"PeriodicalId\":309606,\"journal\":{\"name\":\"2022 IEEE Symposium on Computers and Communications (ISCC)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE Symposium on Computers and Communications (ISCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCC55528.2022.9912823\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE Symposium on Computers and Communications (ISCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCC55528.2022.9912823","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Deep-Learning for Cooperative Spectrum Sensing Optimization in Cognitive Internet of Things
Spectrum sensing is a critical component of Cognitive Internet of Things. It allows Secondary Users(SUs) to access underutilized frequency bands licensed to Primary Users (PUs) opportunistically without causing harmful interference to them. How-ever, accurate individual spectrum sensing solutions are complex to deploy. Thus, Cooperative Spectrum Sensing (CSS) techniques have flourished. These techniques combine individual sensing through a weighting mechanism at a fusion center to assess the channel status. The fusion process depends heavily on the indi-vidual detection thresholds at each SU and the weights attributed to their sensing results by the Fusion Center. In this paper, we propose to use Deep Neural Net-work to compute the optimal energy detection thresh-old and fusion weights. Our goal is to develop a solution that optimally adapts to the time-varying wireless channel conditions. Furthermore, our DNN-based so-lution eliminates the need to solve hard optimization problems, thus significantly reducing computational complexity, especially in large networks.