A Reduced Complexity LDPC Decoding Algorithm Using Dynamic Bit Node Selection

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 174)


A simple and effective computational complexity reducing method for iterative message passing decoding algorithm of Low-Density Parity-Check (LDPC) codes is described. In each iteration, the algorithm selects the fraction of bit nodes with least reliability. At both check node processors as well as bit node processors, the extrinsic messages only for selected nodes are updated while for others the previous values are retained. The algorithm is based on a dynamic selection of bit nodes for updating the messages for each iteration. The complexity analysis and the simulation results shows that the method achieves up to 90 % saving in computations for high rate codes of code rate 0.9 compared to the standard Belief-Propagation (BP) algorithm while maintaining the same bit error rate performance.


Code Rate LDPC Code Parity Check Matrix Check Node Tanner Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Gallager, R.G.: Low-Density Parity-Check Codes. MIT Press, Cambridge (1963)Google Scholar
  2. 2.
    MacKay, D.J.C., Neal, R.M.: Near Shannon limit performance of low density parity-check codes. Electron. Lett. 32(18), 1645–1646 (1996)CrossRefGoogle Scholar
  3. 3.
    MacKay, D.J.C.: Good error-correcting codes based on very sparse matrices. IEEE Trans. Inform. Theory 45, 399–431 (1999)MathSciNetMATHCrossRefGoogle Scholar
  4. 4.
    Chung, S.Y., Forney Jr., G.D., Richardson, T.J., Urbanke, R.: On the design of low density parity check codes within 0.0045 dB of the Shannon limit. IEEE Commun. Lett. 5, 58–60 (2001)CrossRefGoogle Scholar
  5. 5.
    Luby, M.G., Mitzenmacher, M., Shokrollahi, M.A., Spielman, D.A.: Improved low-density parity ch eck codes using irregular graph. IEEE Trans. Inform. Theory 47, 585–598 (2001)MathSciNetMATHCrossRefGoogle Scholar
  6. 6.
    Fossorier, M.P.C., Chen, J.: Near optimum universal belief propagation based decoding of low-density parity-check codes. IEEE Trans. Commun. 50(3), 406–414 (2002)CrossRefGoogle Scholar
  7. 7.
    Zhang, J., Fossorier, M., Gu, D., Zhang, J.: Two-dimensional correction for min-sum decoding of irregular LDPC codes. IEEE Commun. Lett. 10, 180–182 (2006)CrossRefGoogle Scholar
  8. 8.
    Zhang, J., Fossorier, M.: Shuffled belief propagation decoding. In: Proc. 36th Asilomar Conference on Signals, Systems and Computers 2002, vol. 1, pp. 8–15 (2002)Google Scholar
  9. 9.
    Sharon, E., Litsyn, S., Goldberger, J.: Efficient serial message-passing schedules for LDPC decoding. In: Proc. Turbo-Coding Conference 2006 (2006)Google Scholar
  10. 10.
    Wang, Y., Zhang, J., Fossorier, M., Yedidia, J.: Reduced latency iterative decoding of LDPC codes. In: Proc. 2005 IEEE Global Telecommunications Conference (2005)Google Scholar
  11. 11.
    Fetweis, G., Zimmermann, E., Rave, W.: Forced convergence decoding of LDPC codes: EXIT chart analysis and combination with node complexity reduction techniques. In: Proc. 11th European Wireless Conference (2005)Google Scholar
  12. 12.
    Bora, P.K., Zimmermann, E., Fettweis, G., Pattisapu, P.: Reduced complexity LDPC decoding using forced convergence. In: Proc. 7th International Symposium on Wireless Personal Multimedia Communications, WPMC 2004 (2004)Google Scholar
  13. 13.
    Chi, Z., Wang, Z., Zhang, X.: Reduced Complexity Column Layered Decoding and Implementation for LDPC codes. IET Communications 5, 2177–2186 (2011)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.GulbargaIndia

Personalised recommendations