{"title":"Analysis and elimination of short cycles in LDPC convolutional codes","authors":"Ziqin Su, Qiaoyong Qiu, Hua Zhou","doi":"10.1109/COMPCOMM.2016.7924880","DOIUrl":null,"url":null,"abstract":"Time-invariant low-density parity-check convolutional codes (TI LDPC-CCs) can be represented by a polynomial-domain parity-check matrix derived from the corresponding quasi-cyclic (QC) LDPC block codes (LDPC-BCs), while time-varying (TV) LDPC-CCs can be obtained by unwrapping the parity-check matrices of LDPC-BCs. The cycle enumerators for TI and TV LDPC-CCs are compared. Based on the analysis of the graphical structures of short cycles in HT(D), we introduce a method of designing the polynomial syndrome former matrix HCRT(D) for LDPC-CCs. It eliminates short cycles and shows improved decoding performance on an additive white Gaussian noise (AWGN) channel with lower bit error ratio (BER) curves.","PeriodicalId":210833,"journal":{"name":"2016 2nd IEEE International Conference on Computer and Communications (ICCC)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 2nd IEEE International Conference on Computer and Communications (ICCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMPCOMM.2016.7924880","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Time-invariant low-density parity-check convolutional codes (TI LDPC-CCs) can be represented by a polynomial-domain parity-check matrix derived from the corresponding quasi-cyclic (QC) LDPC block codes (LDPC-BCs), while time-varying (TV) LDPC-CCs can be obtained by unwrapping the parity-check matrices of LDPC-BCs. The cycle enumerators for TI and TV LDPC-CCs are compared. Based on the analysis of the graphical structures of short cycles in HT(D), we introduce a method of designing the polynomial syndrome former matrix HCRT(D) for LDPC-CCs. It eliminates short cycles and shows improved decoding performance on an additive white Gaussian noise (AWGN) channel with lower bit error ratio (BER) curves.