Finite-Dimensional Bounds on Zm and Binary LDPC Codes with Belief Propagation Decoders

dc.creatorWang, Chih-Chun
dc.creatorKulkarni, Sanjeev R.
dc.creatorPoor, H. Vincent
dc.date2006-10-10
dc.date.accessioned2026-07-07T08:16:45Z
dc.date.available2026-07-07T08:16:45Z
dc.descriptionThis paper focuses on finite-dimensional upper and lower bounds on decodable thresholds of Zm and binary low-density parity-check (LDPC) codes, assuming belief propagation decoding on memoryless channels. A concrete framework is presented, admitting systematic searches for new bounds. Two noise measures are considered: the Bhattacharyya noise parameter and the soft bit value for a maximum a posteriori probability (MAP) decoder on the uncoded channel. For Zm LDPC codes, an iterative m-dimensional bound is derived for m-ary-input/symmetric-output channels, which gives a sufficient stability condition for Zm LDPC codes and is complemented by a matched necessary stability condition introduced herein. Applications to coded modulation and to codes with non-equiprobable distributed codewords are also discussed. For binary codes, two new lower bounds are provided for symmetric channels, including a two-dimensional iterative bound and a one-dimensional non-iterative bound, the latter of which is the best known bound that is tight for binary symmetric channels (BSCs), and is a strict improvement over the bound derived by the channel degradation argument. By adopting the reverse channel perspective, upper and lower bounds on the decodable Bhattacharyya noise parameter are derived for non-symmetric channels, which coincides with the existing bound for symmetric channels.
dc.descriptionto appear in IEEE Transactions on Information Theory
dc.identifierhttps://arxiv.org/abs/cs/0610052
dc.identifierhttp://arxiv.org/abs/cs/0610052
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/133887
dc.subjectInformation Theory
dc.titleFinite-Dimensional Bounds on Zm and Binary LDPC Codes with Belief Propagation Decoders
dc.typetext

Files

Collections