{"title":"Convergence based design of turbo codes","authors":"H. El Gamal, A. Hammons","doi":"10.1109/ISIT.2001.935962","DOIUrl":null,"url":null,"abstract":"In earlier work (see IEEE Trans. Inform. Theory, vol.47, no.2, p.671-86, Feb. 2001), we introduced a simple technique for analyzing the iterative decoder that is broadly applicable to different classes of codes defined over graphs in certain fading as well as AWGN channels. This technique is based on the observation that the extrinsic information from constituent MAP decoders is well approximated by Gaussian random variables when the inputs to the decoders are Gaussian. The independent Gaussian model implies the existence of an iterative decoder threshold that statistically characterizes the convergence of the iterative decoder. In this paper, we present an interesting convergence-based construction for asymmetric parallel concatenated convolutional codes.","PeriodicalId":433761,"journal":{"name":"Proceedings. 2001 IEEE International Symposium on Information Theory (IEEE Cat. No.01CH37252)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. 2001 IEEE International Symposium on Information Theory (IEEE Cat. No.01CH37252)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISIT.2001.935962","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In earlier work (see IEEE Trans. Inform. Theory, vol.47, no.2, p.671-86, Feb. 2001), we introduced a simple technique for analyzing the iterative decoder that is broadly applicable to different classes of codes defined over graphs in certain fading as well as AWGN channels. This technique is based on the observation that the extrinsic information from constituent MAP decoders is well approximated by Gaussian random variables when the inputs to the decoders are Gaussian. The independent Gaussian model implies the existence of an iterative decoder threshold that statistically characterizes the convergence of the iterative decoder. In this paper, we present an interesting convergence-based construction for asymmetric parallel concatenated convolutional codes.