{"title":"使用Levenshtein距离度量的删除信道双向Viterbi解码","authors":"Ling Cheng, H. C. Ferreira, T. Swart","doi":"10.1109/ITW2.2006.323798","DOIUrl":null,"url":null,"abstract":"In this paper, we present a bidirectional Viterbi decoding algorithm using the Levenshtein distance metric for a regular convolutional encoding system. For a deletion channel, this decoding algorithm can correct an average of 30 deletion errors within a 6000 bit frame, when using an r = 0.67 regular convolutional code; and it can correct an average of 80 deletion errors within a 4000 bit frame, when using an r = 0.25 regular convolutional code","PeriodicalId":299513,"journal":{"name":"2006 IEEE Information Theory Workshop - ITW '06 Chengdu","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Bidirectional Viterbi Decoding using the Levenshtein Distance Metric for Deletion Channels\",\"authors\":\"Ling Cheng, H. C. Ferreira, T. Swart\",\"doi\":\"10.1109/ITW2.2006.323798\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present a bidirectional Viterbi decoding algorithm using the Levenshtein distance metric for a regular convolutional encoding system. For a deletion channel, this decoding algorithm can correct an average of 30 deletion errors within a 6000 bit frame, when using an r = 0.67 regular convolutional code; and it can correct an average of 80 deletion errors within a 4000 bit frame, when using an r = 0.25 regular convolutional code\",\"PeriodicalId\":299513,\"journal\":{\"name\":\"2006 IEEE Information Theory Workshop - ITW '06 Chengdu\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 IEEE Information Theory Workshop - ITW '06 Chengdu\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITW2.2006.323798\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE Information Theory Workshop - ITW '06 Chengdu","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITW2.2006.323798","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Bidirectional Viterbi Decoding using the Levenshtein Distance Metric for Deletion Channels
In this paper, we present a bidirectional Viterbi decoding algorithm using the Levenshtein distance metric for a regular convolutional encoding system. For a deletion channel, this decoding algorithm can correct an average of 30 deletion errors within a 6000 bit frame, when using an r = 0.67 regular convolutional code; and it can correct an average of 80 deletion errors within a 4000 bit frame, when using an r = 0.25 regular convolutional code