Li Wang, Sissi Xiaoxiao Wu, Shibing Zhang, Guodong Zhang, Zhihua Bao
{"title":"认知云网络中基于相位补偿的协同频谱感知算法","authors":"Li Wang, Sissi Xiaoxiao Wu, Shibing Zhang, Guodong Zhang, Zhihua Bao","doi":"10.1109/ICUFN.2018.8436733","DOIUrl":null,"url":null,"abstract":"Spectrum sensing is the key technology in cognitive radio networks. This paper focused on the spectrum sensing in cognitive cloud networks and proposed a cooperative spectrum sensing algorithm based on phase compensation to improve the spectrum sensing performance. In the algorithm, the received signals in the sensing nodes are sent to the cloud, compensated in phase and combined into one signal. Then, the combined signal is used to test the hypothesis, whether the primary user is present. It uses all available information of all sensing nodes and improves the performance of spectrum detection effectively. The simulation results show that the proposed algorithm has 2–4 dB advantage in signal-to-noise ratio over other algorithms.","PeriodicalId":224367,"journal":{"name":"2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cooperative Spectrum Sensing Algorithm Based on Phase Compensation in Cognitive Cloud Networks\",\"authors\":\"Li Wang, Sissi Xiaoxiao Wu, Shibing Zhang, Guodong Zhang, Zhihua Bao\",\"doi\":\"10.1109/ICUFN.2018.8436733\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Spectrum sensing is the key technology in cognitive radio networks. This paper focused on the spectrum sensing in cognitive cloud networks and proposed a cooperative spectrum sensing algorithm based on phase compensation to improve the spectrum sensing performance. In the algorithm, the received signals in the sensing nodes are sent to the cloud, compensated in phase and combined into one signal. Then, the combined signal is used to test the hypothesis, whether the primary user is present. It uses all available information of all sensing nodes and improves the performance of spectrum detection effectively. The simulation results show that the proposed algorithm has 2–4 dB advantage in signal-to-noise ratio over other algorithms.\",\"PeriodicalId\":224367,\"journal\":{\"name\":\"2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICUFN.2018.8436733\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICUFN.2018.8436733","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Cooperative Spectrum Sensing Algorithm Based on Phase Compensation in Cognitive Cloud Networks
Spectrum sensing is the key technology in cognitive radio networks. This paper focused on the spectrum sensing in cognitive cloud networks and proposed a cooperative spectrum sensing algorithm based on phase compensation to improve the spectrum sensing performance. In the algorithm, the received signals in the sensing nodes are sent to the cloud, compensated in phase and combined into one signal. Then, the combined signal is used to test the hypothesis, whether the primary user is present. It uses all available information of all sensing nodes and improves the performance of spectrum detection effectively. The simulation results show that the proposed algorithm has 2–4 dB advantage in signal-to-noise ratio over other algorithms.