{"title":"一种用于异构无线网络识别的功率传感器网络","authors":"Goran Ivkovic, P. Spasojevic, I. Seskar","doi":"10.1109/CISS.2007.4298411","DOIUrl":null,"url":null,"abstract":"We consider a scenario with multiple radio sources operating in the 2.4GHz ISM band and emitting signals that may overlap in time and frequency. Each source is characterized by an on/off signal, which represents its activity in time. Sources are modelled as statistically independent if they belong to different networks or statistically dependent if they belong to the same network, in which case they produce signals that do not overlap in time. A network of sensors performs measurements, where each sensor measures average received power with some time granularity. We show how source activity signals can be recovered from the power measurements using blind signal separation techniques. Recovered signals are partitioned into groups where each group is formed of non overlapping signals in time that belong to the same network.","PeriodicalId":151241,"journal":{"name":"2007 41st Annual Conference on Information Sciences and Systems","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Power Sensor Network for Identification of Heterogeneous Wireless Networks\",\"authors\":\"Goran Ivkovic, P. Spasojevic, I. Seskar\",\"doi\":\"10.1109/CISS.2007.4298411\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We consider a scenario with multiple radio sources operating in the 2.4GHz ISM band and emitting signals that may overlap in time and frequency. Each source is characterized by an on/off signal, which represents its activity in time. Sources are modelled as statistically independent if they belong to different networks or statistically dependent if they belong to the same network, in which case they produce signals that do not overlap in time. A network of sensors performs measurements, where each sensor measures average received power with some time granularity. We show how source activity signals can be recovered from the power measurements using blind signal separation techniques. Recovered signals are partitioned into groups where each group is formed of non overlapping signals in time that belong to the same network.\",\"PeriodicalId\":151241,\"journal\":{\"name\":\"2007 41st Annual Conference on Information Sciences and Systems\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-03-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 41st Annual Conference on Information Sciences and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CISS.2007.4298411\",\"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 41st Annual Conference on Information Sciences and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CISS.2007.4298411","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Power Sensor Network for Identification of Heterogeneous Wireless Networks
We consider a scenario with multiple radio sources operating in the 2.4GHz ISM band and emitting signals that may overlap in time and frequency. Each source is characterized by an on/off signal, which represents its activity in time. Sources are modelled as statistically independent if they belong to different networks or statistically dependent if they belong to the same network, in which case they produce signals that do not overlap in time. A network of sensors performs measurements, where each sensor measures average received power with some time granularity. We show how source activity signals can be recovered from the power measurements using blind signal separation techniques. Recovered signals are partitioned into groups where each group is formed of non overlapping signals in time that belong to the same network.
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