{"title":"Capacity and capacity-achieving input distribution of the energy detector","authors":"E. Leitinger, B. Geiger, K. Witrisal","doi":"10.1109/ICUWB.2012.6340409","DOIUrl":null,"url":null,"abstract":"This paper presents the channel capacity and capacity-achieving input distribution of an energy detection receiver structure. A proper statistical model is introduced which makes it possible to treat the energy detector as a constrained continuous communication channel. To solve this non-linear optimization we used the Blahut-Arimoto algorithm extended with a particle method, so that also continuous channels can be handled. To get a better convergence behavior of the algorithm, we also implement two new methods, which are called “fuse particles” and “kick particles” [1]. The results we present show that the capacity of the energy detector decreases with increasing integration time and decreasing peak-to-average power ratio. It is shown that the capacity-achieving input distribution is discrete with a finite number of mass points.","PeriodicalId":260071,"journal":{"name":"2012 IEEE International Conference on Ultra-Wideband","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Conference on Ultra-Wideband","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICUWB.2012.6340409","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
This paper presents the channel capacity and capacity-achieving input distribution of an energy detection receiver structure. A proper statistical model is introduced which makes it possible to treat the energy detector as a constrained continuous communication channel. To solve this non-linear optimization we used the Blahut-Arimoto algorithm extended with a particle method, so that also continuous channels can be handled. To get a better convergence behavior of the algorithm, we also implement two new methods, which are called “fuse particles” and “kick particles” [1]. The results we present show that the capacity of the energy detector decreases with increasing integration time and decreasing peak-to-average power ratio. It is shown that the capacity-achieving input distribution is discrete with a finite number of mass points.