An efficient algorithm for finding dominant trapping sets of LDPC codes

This paper presents an efficient algorithm for finding the dominant trapping sets of a low-density parity-check (LDPC) code. This algorithm can be used to estimate the error floor of LDPC codes or to be part of the apparatus to design LDPC codes with low error floors. The algorithm is initiated with a set of short cycles as the input. The cycles are then expanded recursively to dominant trapping sets of increasing size. At the core of the algorithm lies the analysis of the graphical structure of dominant trapping sets and the relationship of such structures to short cycles. The algorithm is universal in the sense that it can be used for an arbitrary graph and that it can be tailored to find other graphical objects, such as absorbing sets and Zyablov-Pinsker (ZP) trapping sets, known to dominate the performance of LDPC codes in the error floor region over different channels and for different iterative decoding algorithms. Simulation results on several LDPC codes demonstrate the accuracy and efficiency of the proposed algorithm. In particular, the algorithm is significantly faster than the existing search algorithms for dominant trapping sets.

[1]  Stephen G. Wilson,et al.  A General Method for Finding Low Error Rates of LDPC Codes , 2006, ArXiv.

[2]  Richard D. Wesel,et al.  Selective avoidance of cycles in irregular LDPC code construction , 2004, IEEE Transactions on Communications.

[3]  P. Vontobel,et al.  Graph-Cover Decoding and Finite-Length Analysis of Message-Passing Iterative Decoding of LDPC Codes , 2005, ArXiv.

[4]  G. A. Margulis,et al.  Explicit constructions of graphs without short cycles and low density codes , 1982, Comb..

[5]  V. Anantharam,et al.  Evaluation of the Low Frame Error Rate Performance of LDPC Codes Using Importance Sampling , 2007, 2007 IEEE Information Theory Workshop.

[6]  Hua Xiao,et al.  Estimation of Bit and Frame Error Rates of Finite-Length Low-Density Parity-Check Codes on Binary Symmetric Channels , 2007, IEEE Transactions on Communications.

[7]  Donald B. Johnson,et al.  Finding All the Elementary Circuits of a Directed Graph , 1975, SIAM J. Comput..

[8]  O. Milenkovic,et al.  Algorithmic and combinatorial analysis of trapping sets in structured LDPC codes , 2005, 2005 International Conference on Wireless Networks, Communications and Mobile Computing.

[9]  Babak Daneshrad,et al.  A performance improvement and error floor avoidance technique for belief propagation decoding of LDPC codes , 2005, 2005 IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications.

[10]  L. Sunil Chandran,et al.  Hardness of Approximation Results for the Problem of Finding the Stopping Distance in Tanner Graphs , 2006, FSTTCS.

[11]  Gilles Zémor,et al.  On the minimum distance of structured LDPC codes with two variable nodes of degree 2 per parity-check equation , 2006, 2006 IEEE International Symposium on Information Theory.

[12]  RosnesEirik,et al.  An efficient algorithm to find all small-size stopping sets of low-density parity-check matrices , 2009 .

[13]  B. Vasic,et al.  Trapping set ontology , 2009, 2009 47th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[14]  Hua Xiao,et al.  Improved progressive-edge-growth (PEG) construction of irregular LDPC codes , 2004, IEEE Global Telecommunications Conference, 2004. GLOBECOM '04..

[15]  Andrew McGregor,et al.  On the Hardness of Approximating Stopping and Trapping Sets in LDPC Codes , 2007, 2007 IEEE Information Theory Workshop.

[16]  Johannes B. Huber,et al.  The Trapping Redundancy of Linear Block Codes , 2006, IEEE Transactions on Information Theory.

[17]  Eric T. Bax Algorithms to Count Paths and Cycles , 1994, Inf. Process. Lett..

[18]  H. Vincent Poor,et al.  Finding All Small Error-Prone Substructures in LDPC Codes , 2009, IEEE Transactions on Information Theory.

[19]  William E. Ryan,et al.  Design of efficiently encodable moderate-length high-rate irregular LDPC codes , 2004, IEEE Transactions on Communications.

[20]  Axthonv G. Oettinger,et al.  IEEE Transactions on Information Theory , 1998 .

[21]  Amir H. Banihashemi,et al.  Message-Passing Algorithms for Counting Short Cycles in a Graph , 2010, IEEE Transactions on Communications.

[22]  Shuai Zhang,et al.  On the dynamics of the error floor behavior in regular LDPC codes , 2009, 2009 IEEE Information Theory Workshop.

[23]  Ayoub Otmani,et al.  On the Minimum Distance of Generalized LDPC Codes , 2007, 2007 IEEE International Symposium on Information Theory.

[24]  Øyvind Ytrehus,et al.  An Efficient Algorithm to Find All Small-Size Stopping Sets of Low-Density Parity-Check Matrices , 2009, IEEE Transactions on Information Theory.

[25]  Yifei Zhang,et al.  Toward low LDPC-code floors: a case study , 2009, IEEE Transactions on Communications.

[26]  Hua Xiao,et al.  Error rate estimation of low-density parity-check codes on binary symmetric channels using cycle enumeration , 2009, IEEE Transactions on Communications.

[27]  Babak Daneshrad,et al.  An IS Simulation Technique for Very Low BER Performance Evaluation of LDPC Codes , 2006, 2006 IEEE International Conference on Communications.

[28]  Sae-Young Chung,et al.  On the construction of some capacity-approaching coding schemes , 2000 .

[29]  R. M. Tanner,et al.  A Class of Group-Structured LDPC Codes , 2001 .

[30]  P. Vontobel,et al.  Graph-covers and iterative decoding of nite length codes , 2003 .

[31]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[32]  Thomas J. Richardson,et al.  Error Floors of LDPC Codes , 2003 .

[33]  Amir H. Banihashemi,et al.  Lowering the error floor of LDPC codes using cyclic liftings , 2010, 2010 IEEE International Symposium on Information Theory.

[34]  Emina Soljanin,et al.  Asymptotic Spectra of Trapping Sets in Regular and Irregular LDPC Code Ensembles , 2007, IEEE Transactions on Information Theory.

[35]  강태훈,et al.  IEEE 802.11n 시스템에서 다중 변조 및 부호화율, 다중 안테나 전송 모드의 적응적 선택 방안 , 2008 .

[36]  Lara Dolecek,et al.  Quantization Effects in Low-Density Parity-Check Decoders , 2007, 2007 IEEE International Conference on Communications.

[37]  Hua Xiao,et al.  Estimation of Bit and Frame Error Rates of Low-Density Parity-Check Codes on Binary Symmetric Channels , 2006, 2007 10th Canadian Workshop on Information Theory (CWIT).

[38]  Shashi Kiran Chilappagari,et al.  On the Construction of Structured LDPC Codes Free of Small Trapping Sets , 2012, IEEE Transactions on Information Theory.

[39]  Øyvind Ytrehus,et al.  An Algorithm to Find All Small-Size Stopping Sets of Low-Density Parity-Check Matrices , 2007, 2007 IEEE International Symposium on Information Theory.

[40]  David Declercq,et al.  Trapping set enumerators for specific LDPC codes , 2010, 2010 Information Theory and Applications Workshop (ITA).

[41]  Dejan Vukobratovic,et al.  Generalized ACE Constrained Progressive Edge-Growth LDPC Code Design , 2008, IEEE Communications Letters.

[42]  Lara Dolecek,et al.  Predicting error floors of structured LDPC codes: deterministic bounds and estimates , 2009, IEEE Journal on Selected Areas in Communications.

[43]  Hua Xiao,et al.  Error rate estimation of finite-length low-density parity-check codes decoded by soft-decision iterative algorithms , 2008, 2008 IEEE International Symposium on Information Theory.

[44]  Emre Telatar,et al.  Finite-length analysis of low-density parity-check codes on the binary erasure channel , 2002, IEEE Trans. Inf. Theory.

[45]  Hua Xiao,et al.  Improved progressive-edge-growth (PEG) construction of irregular LDPC codes , 2004, IEEE Communications Letters.

[46]  Priti Shankar,et al.  Computing the Stopping Distance of a Tanner Graph Is NP-Hard , 2007, IEEE Transactions on Information Theory.

[47]  W. Ryan,et al.  LDPC decoder strategies for achieving low error floors , 2008, 2008 Information Theory and Applications Workshop.

[48]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[49]  Amir H. Banihashemi,et al.  A Message-Passing Algorithm for Counting Short Cycles in a Graph , 2010, ArXiv.

[50]  Shashi Kiran Chilappagari,et al.  Eliminating trapping sets in low-density parity-check codes by using Tanner graph covers , 2008, IEEE Transactions on Information Theory.

[51]  Chih-Chun Wang,et al.  Exhaustive search for small fully absorbing sets and the corresponding low error-floor decoder , 2010, 2010 IEEE International Symposium on Information Theory.

[52]  Brendan D. McKay,et al.  Short Cycles in Random Regular Graphs , 2004, Electron. J. Comb..

[53]  David J. C. MacKay,et al.  Weaknesses of Margulis and Ramanujan-Margulis low-density parity-check cCodes , 2003, MFCSIT.

[54]  採編典藏組 Society for Industrial and Applied Mathematics(SIAM) , 2008 .

[55]  N. S. Barnett,et al.  Private communication , 1969 .

[56]  Chih-Chun Wang,et al.  On the Exhaustion and Elimination of Trapping Sets: Algorithms & The Suppressing Effect , 2007, 2007 IEEE International Symposium on Information Theory.

[57]  Evangelos Eleftheriou,et al.  Regular and irregular progressive edge-growth tanner graphs , 2005, IEEE Transactions on Information Theory.

[58]  Rüdiger L. Urbanke,et al.  Design of capacity-approaching irregular low-density parity-check codes , 2001, IEEE Trans. Inf. Theory.