I. Kanno, K. Horihata, Akio Hasegawa, T. Maeyama, Y. Takeuchi
{"title":"Efficient white space boundary estimation with heterogeneous types of sensors","authors":"I. Kanno, K. Horihata, Akio Hasegawa, T. Maeyama, Y. Takeuchi","doi":"10.1109/ISANP.2014.7026583","DOIUrl":null,"url":null,"abstract":"This paper proposes a novel framework for white space (WS) boundary estimation which utilizes heterogeneous types of sensors. The proposed framework utilizes sensors not for spectrum sensing, but for estimating the propagation parameters that characterize the boundary to the incumbent radio systems (IRSs) to identify WS efficiently. The position of IRS emitter (transmission source), its transmission power, and pathloss around it are estimated to identify the boundary from collected sensing data. The former 2 parameters and latter one are estimated with sparsely deployed long-range sensors and densely deployed low-end small sensors, respectively. In addition, its result of preliminary feasibility study is described.","PeriodicalId":354277,"journal":{"name":"2014 International Symposium on Antennas and Propagation Conference Proceedings","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Symposium on Antennas and Propagation Conference Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISANP.2014.7026583","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
This paper proposes a novel framework for white space (WS) boundary estimation which utilizes heterogeneous types of sensors. The proposed framework utilizes sensors not for spectrum sensing, but for estimating the propagation parameters that characterize the boundary to the incumbent radio systems (IRSs) to identify WS efficiently. The position of IRS emitter (transmission source), its transmission power, and pathloss around it are estimated to identify the boundary from collected sensing data. The former 2 parameters and latter one are estimated with sparsely deployed long-range sensors and densely deployed low-end small sensors, respectively. In addition, its result of preliminary feasibility study is described.