{"title":"认知无线电网络中的节能智能协同频谱感知算法","authors":"Tangsen Huang, Xiangdong Yin, Xiaowu Li","doi":"10.1177/15501329221125119","DOIUrl":null,"url":null,"abstract":"Green communication is the demand of current and future wireless communication. As the next-generation communication network, cognitive radio network also needs to meet the requirements of green communication. Therefore, improving energy efficiency is an inevitable requirement for the development of cognitive radio networks. However, there is a need to compromise sensing performance while improving energy efficiency. To take into account the two important indicators of sensing performance and energy efficiency, a grouping algorithm is proposed in this article, which can effectively improve the energy efficiency while improving the spectrum sensing performance. The algorithm obtains the initial value of the reliability of the nodes through training, and sorts them according to the highest reliability value, then selects an even number of nodes with the highest reliability value, and divides the selected nodes into two groups, and the two groups of nodes take turns in Alternate work. At this time, other nodes not participating in cooperative spectrum sensing are in a silent state, waiting for the instruction of the fusion center. The experimental results show that compared with the traditional algorithm, the proposed algorithm has a great improvement in the two indicators of sensing performance and energy efficiency.","PeriodicalId":50327,"journal":{"name":"International Journal of Distributed Sensor Networks","volume":" ","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Energy-efficient and intelligent cooperative spectrum sensing algorithm in cognitive radio networks\",\"authors\":\"Tangsen Huang, Xiangdong Yin, Xiaowu Li\",\"doi\":\"10.1177/15501329221125119\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Green communication is the demand of current and future wireless communication. As the next-generation communication network, cognitive radio network also needs to meet the requirements of green communication. Therefore, improving energy efficiency is an inevitable requirement for the development of cognitive radio networks. However, there is a need to compromise sensing performance while improving energy efficiency. To take into account the two important indicators of sensing performance and energy efficiency, a grouping algorithm is proposed in this article, which can effectively improve the energy efficiency while improving the spectrum sensing performance. The algorithm obtains the initial value of the reliability of the nodes through training, and sorts them according to the highest reliability value, then selects an even number of nodes with the highest reliability value, and divides the selected nodes into two groups, and the two groups of nodes take turns in Alternate work. At this time, other nodes not participating in cooperative spectrum sensing are in a silent state, waiting for the instruction of the fusion center. The experimental results show that compared with the traditional algorithm, the proposed algorithm has a great improvement in the two indicators of sensing performance and energy efficiency.\",\"PeriodicalId\":50327,\"journal\":{\"name\":\"International Journal of Distributed Sensor Networks\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2022-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Distributed Sensor Networks\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1177/15501329221125119\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Distributed Sensor Networks","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1177/15501329221125119","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Energy-efficient and intelligent cooperative spectrum sensing algorithm in cognitive radio networks
Green communication is the demand of current and future wireless communication. As the next-generation communication network, cognitive radio network also needs to meet the requirements of green communication. Therefore, improving energy efficiency is an inevitable requirement for the development of cognitive radio networks. However, there is a need to compromise sensing performance while improving energy efficiency. To take into account the two important indicators of sensing performance and energy efficiency, a grouping algorithm is proposed in this article, which can effectively improve the energy efficiency while improving the spectrum sensing performance. The algorithm obtains the initial value of the reliability of the nodes through training, and sorts them according to the highest reliability value, then selects an even number of nodes with the highest reliability value, and divides the selected nodes into two groups, and the two groups of nodes take turns in Alternate work. At this time, other nodes not participating in cooperative spectrum sensing are in a silent state, waiting for the instruction of the fusion center. The experimental results show that compared with the traditional algorithm, the proposed algorithm has a great improvement in the two indicators of sensing performance and energy efficiency.
期刊介绍:
International Journal of Distributed Sensor Networks (IJDSN) is a JCR ranked, peer-reviewed, open access journal that focuses on applied research and applications of sensor networks. The goal of this journal is to provide a forum for the publication of important research contributions in developing high performance computing solutions to problems arising from the complexities of these sensor network systems. Articles highlight advances in uses of sensor network systems for solving computational tasks in manufacturing, engineering and environmental systems.