Trapping set ontology

Authors:
Bane Vasić;Shashi Kiran Chilappagari;Dung Viet Nguyen;Shiva Kumar Planjery
Affiliations:
Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ;Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ;Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ;Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ
Venue:
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Year:
2009

Citing 8
Cited 0

Graphs, Codes and Designs

Graphs, Codes and Designs
Instanton-based techniques for analysis and reduction of error floors of LDPC codes

IEEE Journal on Selected Areas in Communications - Special issue on capaciyy approaching codes
Error-correction capability of column-weight-three LDPC codes

IEEE Transactions on Information Theory
Analysis of error floors of LDPC codes under LP decoding over the BSC

ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
The capacity of low-density parity-check codes under message-passing decoding

IEEE Transactions on Information Theory
Finite-length analysis of low-density parity-check codes on the binary erasure channel

IEEE Transactions on Information Theory
Regular and irregular progressive edge-growth tanner graphs

IEEE Transactions on Information Theory
Eliminating Trapping Sets in Low-Density Parity-Check Codes by Using Tanner Graph Covers

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

The failures of iterative decoders for low-density parity-check (LDPC) codes on the additive white Gaussian noise channel (AWGNC) and the binary symmetric channel (BSC) can be understood in terms of combinatorial objects known as trapping sets. In this paper, we derive a systematic method to identify the most relevant trapping sets for decoding over the BSC in the error floor region. We elaborate on the notion of the critical number of a trapping set and derive a classification of trapping sets. We then develop the trapping set ontology, a database of trapping sets that summarizes the topological relations among trapping sets. We elucidate the usefulness of the trapping set ontology in predicting the error floor as well as in designing better codes.