{"title":"基于衰落抑制技术的通信卫星信道状态估计信噪比估计算法研究","authors":"A. Aroumont, L. Castanet, M. Bousquet","doi":"10.3233/SC-2009-0352","DOIUrl":null,"url":null,"abstract":"Channel state estimation is one of the key processes carried out at the physical layer of Satellite Systems employing Fade Mitigation Techniques such as Adaptive Coding and Modulation ACM. An accurate and reliable channel estimate is needed to fully realize the capacity gains accrued by using ACM. In this paper the SNR estimation algorithms, which perform the role of channel state estimators, are analysed from a system point of view to get a quantitative idea on the number of received symbols needed to get a reliable estimate, and the impact of interference noise on it. A DVB type satellite system has been investigated for the study. An improvement over the Maximum Likelihood ML estimator using Bayesian principles is suggested and illustrated.","PeriodicalId":51158,"journal":{"name":"Space Communications","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"A study on SNR estimation algorithms for channel state estimation in Communication Satellite Systems employing Fade Mitigation Techniques\",\"authors\":\"A. Aroumont, L. Castanet, M. Bousquet\",\"doi\":\"10.3233/SC-2009-0352\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Channel state estimation is one of the key processes carried out at the physical layer of Satellite Systems employing Fade Mitigation Techniques such as Adaptive Coding and Modulation ACM. An accurate and reliable channel estimate is needed to fully realize the capacity gains accrued by using ACM. In this paper the SNR estimation algorithms, which perform the role of channel state estimators, are analysed from a system point of view to get a quantitative idea on the number of received symbols needed to get a reliable estimate, and the impact of interference noise on it. A DVB type satellite system has been investigated for the study. An improvement over the Maximum Likelihood ML estimator using Bayesian principles is suggested and illustrated.\",\"PeriodicalId\":51158,\"journal\":{\"name\":\"Space Communications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Space Communications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3233/SC-2009-0352\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Space Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/SC-2009-0352","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A study on SNR estimation algorithms for channel state estimation in Communication Satellite Systems employing Fade Mitigation Techniques
Channel state estimation is one of the key processes carried out at the physical layer of Satellite Systems employing Fade Mitigation Techniques such as Adaptive Coding and Modulation ACM. An accurate and reliable channel estimate is needed to fully realize the capacity gains accrued by using ACM. In this paper the SNR estimation algorithms, which perform the role of channel state estimators, are analysed from a system point of view to get a quantitative idea on the number of received symbols needed to get a reliable estimate, and the impact of interference noise on it. A DVB type satellite system has been investigated for the study. An improvement over the Maximum Likelihood ML estimator using Bayesian principles is suggested and illustrated.