A local flat histogram method for evaluating the performance of LDPC codes

The performance of low-density parity-check (LDPC) codes can be evaluated by using the dual adaptive importance sampling (DAIS) method. To further accelerate the convergence speed of simulation, an improved method is proposed in which the biased distribution is multiplied with an auxiliary biased function. In this proposed method, samples are limited in the restricted region, where most of the events result in decoding failure, by multiplying the original biased distribution of DAIS method by a second bias. This scheme speeds up the convergence of simulation in the restricted region. Compared with the traditional DAIS method, simulation results for two specific LDPC codes verify that this proposed method achieves the significant simulation gain without sacrificing the accuracy.

[1]  Dong-Soo Har,et al.  Evaluation of the Low Error-Rate Performance of LDPC Codes over Rayleigh Fading Channels Using Importance Sampling , 2013, IEEE Transactions on Communications.

[2]  Yuan Liu,et al.  Improved dual adaptive importance sampling method for LDPC codes , 2012, 2012 18th Asia-Pacific Conference on Communications (APCC).

[3]  C. K. Michael Tse,et al.  Evaluation of the Extremely Low Block Error Rate of Irregular LDPC Codes , 2009, 2009 IEEE International Conference on Communications.

[4]  Babak Daneshrad,et al.  Low BER performance estimation of LDPC codes via application of importance sampling to trapping sets , 2009, IEEE Transactions on Communications.

[5]  Ranjith Liyanapathirana,et al.  Multicanonical Estimation of Outage Probabilities in MIMO Channels , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[6]  Qiuju Diao,et al.  Cyclic and Quasi-Cyclic LDPC Codes on Constrained Parity-Check Matrices and Their Trapping Sets , 2012, IEEE Transactions on Information Theory.

[7]  Leslie A. Rusch,et al.  A Fresh Look at Multicanonical Monte Carlo from a Telecom Perspective , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

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

[9]  Gianluigi Ferrari,et al.  Does the Performance of LDPC Codes Depend on the Channel? , 2006, IEEE Transactions on Communications.

[10]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[11]  Ronald Holzlöhner,et al.  Evaluation of the very low BER of FEC codes using dual adaptive importance sampling , 2005, IEEE Communications Letters.

[12]  Amir H. Banihashemi,et al.  An efficient algorithm for finding dominant trapping sets of LDPC codes , 2011, 2010 6th International Symposium on Turbo Codes & Iterative Information Processing.

[13]  Shu Lin,et al.  Channel Codes: Classical and Modern , 2009 .

[14]  Amir H. Banihashemi,et al.  An efficient algorithm for finding dominant trapping sets of irregular LDPC codes , 2011, 2011 IEEE International Symposium on Information Theory Proceedings.

[15]  Mansoor Shafi,et al.  Quick Simulation: A Review of Importance Sampling Techniques in Communications Systems , 1997, IEEE J. Sel. Areas Commun..

[16]  Shu Lin,et al.  Bit-Error Probability for Maximum-Likelihood Decoding of Linear Block Codes and Related Soft-Decision Decoding Methods , 1998, IEEE Trans. Inf. Theory.

[17]  Luca Martino,et al.  Fully adaptive Gaussian mixture Metropolis-Hastings algorithm , 2012, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[18]  David J. C. MacKay,et al.  Encyclopedia of Sparse Graph Codes , 1999 .

[19]  Koji Shibata,et al.  A study on quick simulation for estimation of low FER of LDPC codes , 2009, 2009 IEEE 9th Malaysia International Conference on Communications (MICC).

[20]  D. Declercq,et al.  Fast Simulation for the Performance Evaluation of LDPC Codes using Fast Flat Histogram Method , 2008, 2008 IEEE Sarnoff Symposium.

[21]  Marco Secondini,et al.  Performance Evaluation of WDM Systems Through Multicanonical Monte Carlo Simulations , 2011, Journal of Lightwave Technology.

[22]  Koji Shibata,et al.  Fast BER estimation of LDPC codes , 2010, 2010 International ITG Conference on Source and Channel Coding (SCC).

[23]  William E. Ryan,et al.  Quasi-cyclic generalized ldpc codes with low error floors , 2007, IEEE Transactions on Communications.

[24]  M. Reimer,et al.  Multicanonical Evaluation of the Tails of the Probability Density Function of Semiconductor Optical Amplifier Output Power Fluctuations , 2010, IEEE Journal of Quantum Electronics.

[25]  William E. Ryan,et al.  On importance sampling for linear block codes , 2003, IEEE International Conference on Communications, 2003. ICC '03..

[26]  Tao Lu,et al.  Biased multicanonical sampling , 2005, IEEE Photonics Technology Letters.

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

[28]  2015 IEEE Wireless Communications and Networking Conference Workshops, WCNC Workshops 2015, New Orleans, LA, USA, March 9-12, 2015 , 2015, WCNC Workshops.