{"title":"使用低功耗物联网设备的宽带频谱传感","authors":"Hyeongyun Kim, Junil Ahn, Haewoon Nam","doi":"10.1109/ICTC49870.2020.9289566","DOIUrl":null,"url":null,"abstract":"We consider a compressive wideband spectrum sensing for low-power IoT devices as secondary users(SUs). We present the proposed scheme for cost-effective compressive sensing for wideband spectrum sensing with a large number of distributed SUs. SUs have a single RF-chain for the compressive sensing and the measurement samples obtained at each SU are sent to the fusion center. The fusion center performs the proposed algorithm which estimates the minimum measurement samples for the reconstruction process. Among the total measurement samples by SUs, the rest of the samples except for the minimum number of samples are used for cooperative spectrum sensing. The original signal vector with the minimum measurement samples is reconstructed and cooperative gain is obtained by using the remainder of measurement samples effectively. We compare the performance of the proposed algorithm with the conventional compressive sensing scheme and the result shows that the proposed algorithm has better performance specially at the high sparsity order region.","PeriodicalId":282243,"journal":{"name":"2020 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"2007 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Wideband spectrum sensing using low-power IoT device\",\"authors\":\"Hyeongyun Kim, Junil Ahn, Haewoon Nam\",\"doi\":\"10.1109/ICTC49870.2020.9289566\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We consider a compressive wideband spectrum sensing for low-power IoT devices as secondary users(SUs). We present the proposed scheme for cost-effective compressive sensing for wideband spectrum sensing with a large number of distributed SUs. SUs have a single RF-chain for the compressive sensing and the measurement samples obtained at each SU are sent to the fusion center. The fusion center performs the proposed algorithm which estimates the minimum measurement samples for the reconstruction process. Among the total measurement samples by SUs, the rest of the samples except for the minimum number of samples are used for cooperative spectrum sensing. The original signal vector with the minimum measurement samples is reconstructed and cooperative gain is obtained by using the remainder of measurement samples effectively. We compare the performance of the proposed algorithm with the conventional compressive sensing scheme and the result shows that the proposed algorithm has better performance specially at the high sparsity order region.\",\"PeriodicalId\":282243,\"journal\":{\"name\":\"2020 International Conference on Information and Communication Technology Convergence (ICTC)\",\"volume\":\"2007 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Conference on Information and Communication Technology Convergence (ICTC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICTC49870.2020.9289566\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Information and Communication Technology Convergence (ICTC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTC49870.2020.9289566","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Wideband spectrum sensing using low-power IoT device
We consider a compressive wideband spectrum sensing for low-power IoT devices as secondary users(SUs). We present the proposed scheme for cost-effective compressive sensing for wideband spectrum sensing with a large number of distributed SUs. SUs have a single RF-chain for the compressive sensing and the measurement samples obtained at each SU are sent to the fusion center. The fusion center performs the proposed algorithm which estimates the minimum measurement samples for the reconstruction process. Among the total measurement samples by SUs, the rest of the samples except for the minimum number of samples are used for cooperative spectrum sensing. The original signal vector with the minimum measurement samples is reconstructed and cooperative gain is obtained by using the remainder of measurement samples effectively. We compare the performance of the proposed algorithm with the conventional compressive sensing scheme and the result shows that the proposed algorithm has better performance specially at the high sparsity order region.