Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014, Article ID 606913, 10 pages
http://dx.doi.org/10.1155/2014/606913
Research Article

A Generalization Belief Propagation Decoding Algorithm for Polar Codes Based on Particle Swarm Optimization

1Key Laboratory of Military Satellite Communications, College of Communications Engineering, PLA University of Science and Technology, Nanjing 210007, China
2Unit 75706, PLA, Guangzhou 510000, China

Received 31 December 2013; Accepted 21 April 2014; Published 13 May 2014

Academic Editor: Balaji Raghavan

Copyright © 2014 Yingxian Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. E. Arıkan, “Channel polarization: a method for constructing capacity-achieving codes for symmetric binary-input memoryless channels,” Institute of Electrical and Electronics Engineers. Transactions on Information Theory, vol. 55, no. 7, pp. 3051–3073, 2009. View at Publisher · View at Google Scholar · View at MathSciNet
  2. E. Arikan and E. Telatar, “On the rate of channel polarization,” in Proceedings of the IEEE International Symposium on Information Theory (ISIT '09), pp. 1493–1495, Seoul, South Korea, July 2009. View at Publisher · View at Google Scholar · View at Scopus
  3. S. B. Korada, E. Şaşoğlu, and R. Urbanke, “Polar codes: characterization of exponent, bounds, and constructions,” Institute of Electrical and Electronics Engineers. Transactions on Information Theory, vol. 56, no. 12, pp. 6253–6264, 2010. View at Publisher · View at Google Scholar · View at MathSciNet
  4. I. Tal and A. Vardy, “List decoding of polar codes,” in Proceedings of the IEEE International Symposium on Information Theory (ISIT '11), pp. 1–5, St Petersburg, Russia, August 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. I. Tal and A. Vardy, “List decoding of polar codes,” http://arxiv.org/abs/1206.0050.
  6. K. Chen, K. Niu, and J. R. Lin, “Improved successive cancellation decoding of polar codes,” IEEE Transactions on Communications, vol. 61, no. 8, pp. 3100–3107, 2013. View at Google Scholar
  7. K. Niu and K. Chen, “CRC-aided decoding of polar codes,” IEEE Communications Letters, vol. 16, no. 10, pp. 1668–1671, 2012. View at Google Scholar
  8. A. Alamdar-Yazdi and F. R. Kschischang, “A simplified successive-cancellation decoder for polar codes,” IEEE Communications Letters, vol. 15, no. 12, pp. 1378–1380, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. G. Sarkis and W. J. Gross, “Increasing the throughput of polar decoders,” IEEE Communications Letters, vol. 17, no. 4, pp. 725–728, 2013. View at Google Scholar
  10. G. Sarkis, P. Giard, A. Vardy, C. Thibeault, and W. J. Gross, “Fast polar decoders: algorithm and implementation,” http://arxiv.org/abs/1306.6311.
  11. P. Giard, G. Sarkis, C. Thibeault, and W. J. Gross, “A fast software polar decoder,” http://arxiv.org/abs/1306.6311.
  12. R. G. Gallager, “Low-density parity-check codes,” IRE Transactions on Information Theory, vol. IT-8, pp. 21–28, 1962. View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  13. E. Arikan, “A performance comparison of polar codes and reed-muller codes,” IEEE Communications Letters, vol. 12, no. 6, pp. 447–449, 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. N. Hussami, S. B. Korada, and R. Urbanke, “Performance of polar codes for channel and source coding,” in Proceedings of the IEEE International Symposium on Information Theory (ISIT '09), pp. 1488–1492, Seoul, South Korea, July 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. E. Arikan, “Polar codes: a pipelined implementation,” in Proceedings of the 4th International Symposium on Broadband Communication (ISBC '10), pp. 11–14, July 2010.
  16. A. Eslami and H. Pishro-Nik, “On bit error rate performance of polar codes in finite regime,” in Proceedings of the 48th Annual Allerton Conference on Communication, Control, and Computing, Allerton (AACCCC '10), pp. 188–194, Allerton, Ill, USA, October 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. A. Eslami and H. Pishro-Nik, “On finite-length performance of polar codes: stopping sets, error floor, and concatenated design,” http://arxiv.org/abs/1211.2187.
  18. D. Divsalar and C. Jones, “Protograph LDPC codes with node degrees at least 3,” in Proceedings of the IEEE Global Telecommunications Conference (GLOBECOM '06), pp. 1–5, San Francisco, Calif, USA, November 2006. View at Publisher · View at Google Scholar · View at Scopus
  19. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948, December 1995. View at Scopus
  20. C.-L. Sun, J.-C. Zeng, and J.-S. Pan, “An improved vector particle swarm optimization for constrained optimization problems,” Information Sciences, vol. 181, no. 6, pp. 1153–1163, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. S. He, E. Prempain, and Q. H. Wu, “An improved particle swarm optimizer for mechanical design optimization problems,” Engineering Optimization, vol. 36, no. 5, pp. 585–605, 2004. View at Publisher · View at Google Scholar · View at MathSciNet
  22. X. Hu and R. C. Eberhart, “Solving constrained nonlinear optimization problems with particle swarm optimization,” in Proceedings of the 6th World Multiconference on Systemics, Cybernetics and Informatics, 2002.
  23. F. Y. H. Ahmed, S. M. Shamsuddin, and S. Z. M. Hashim, “Improved SpikeProp for using particle swarm optimization,” Mathematical Problems in Engineering, vol. 2013, Article ID 257085, 13 pages, 2013. View at Publisher · View at Google Scholar
  24. A. Y. Alanis, E. Rangel, J. Rivera, N. Arana-Daniel, and C. Lopez-Franco, “Particle swarm based approach of a real-time discrete neural identifier for linear induction motors,” Mathematical Problems in Engineering, vol. 2013, Article ID 715094, 9 pages, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  25. A. M. Arasomwan and A. O. Adewumi, “An investigation into the performance of particle swarm optimization with various chaotic maps,” Mathematical Problems in Engineering, vol. 2014, Article ID 178959, 17 pages, 2014. View at Publisher · View at Google Scholar · View at MathSciNet
  26. N. G. Hedeshi and M. S. Abadeh, “Coronary artery disease detection using a fuzzy-boosting PSO approach,” Mathematical Problems in Engineering, vol. 2014, Article ID 783734, 12 pages, 2014. View at Publisher · View at Google Scholar