{"title":"基于参数模型的时变信道递归盲估计与均衡","authors":"Zheng Yuanjin","doi":"10.1109/ICCS.2002.1182443","DOIUrl":null,"url":null,"abstract":"This paper proposes a new technique for recursive blind equalization of a time-varying IIR communication channel to obtain simultaneous estimation of channel impulse response and input symbols. The received sequences are represented as the output of a noisy non-Gaussian time-varying parametric model. A pseudo maximum likelihood estimation algorithm is proposed for the identification of channel parameters. The blind equalization is implemented by three algorithms: the recursive channel estimation algorithm, the Gaussian-mixture parameter estimation algorithm and the standard Kalman filtering algorithm. The equalization results are good even on low SNR received sequence and fast fading channel.","PeriodicalId":401041,"journal":{"name":"The 8th International Conference on Communication Systems, 2002. ICCS 2002.","volume":"59 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Recursive blind estimation and equalization of time-varying channel based on parametric model\",\"authors\":\"Zheng Yuanjin\",\"doi\":\"10.1109/ICCS.2002.1182443\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a new technique for recursive blind equalization of a time-varying IIR communication channel to obtain simultaneous estimation of channel impulse response and input symbols. The received sequences are represented as the output of a noisy non-Gaussian time-varying parametric model. A pseudo maximum likelihood estimation algorithm is proposed for the identification of channel parameters. The blind equalization is implemented by three algorithms: the recursive channel estimation algorithm, the Gaussian-mixture parameter estimation algorithm and the standard Kalman filtering algorithm. The equalization results are good even on low SNR received sequence and fast fading channel.\",\"PeriodicalId\":401041,\"journal\":{\"name\":\"The 8th International Conference on Communication Systems, 2002. ICCS 2002.\",\"volume\":\"59 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-11-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The 8th International Conference on Communication Systems, 2002. ICCS 2002.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCS.2002.1182443\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 8th International Conference on Communication Systems, 2002. ICCS 2002.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCS.2002.1182443","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recursive blind estimation and equalization of time-varying channel based on parametric model
This paper proposes a new technique for recursive blind equalization of a time-varying IIR communication channel to obtain simultaneous estimation of channel impulse response and input symbols. The received sequences are represented as the output of a noisy non-Gaussian time-varying parametric model. A pseudo maximum likelihood estimation algorithm is proposed for the identification of channel parameters. The blind equalization is implemented by three algorithms: the recursive channel estimation algorithm, the Gaussian-mixture parameter estimation algorithm and the standard Kalman filtering algorithm. The equalization results are good even on low SNR received sequence and fast fading channel.