Low density parity check codes based on sparse matrices with no small cycles

  • J. Bond
  • S. Hui
  • H. Schmidt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1355)


In this paper we give a systematic construction of matrices with constant row weights and column weights and arbitrarily large girths. This resolves a problem raised by D. MacKay. The matrices are used in the generator matrices of linear codes. We give the experiment performance results for codes whose associated matrices have girth 8. We also give a randomized construction of matrices with constant row sums and column sums and few 4-cycles. The codes generated using the matrices are used to encode bit streams for a Gaussian channel and decoded using a decoding algorithm that combines features of the algorithms given by MacKay and Cheng and McEliece. The experimental performance results for codes generated using the random matrices are compared to those of the systematically constructed codes. The results show that the codes generated using the random codes with smaller block sizes perform as well as the systematic codes with bigger block sizes. The performance of the systematic codes, for specified weights, can be used to tailor the random codes. MATLAB routines for the construction for the girth 8 case and a special girth 4 case are included.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    J. F. Cheng and R. J. McEliece, Some high rate near capacity codes for the Gaussian channel, Preprint.Google Scholar
  2. [2]
    R. G. Gallager, Low density parity check codes, IRE Transactions on Information Theory, Jan. 1962.Google Scholar
  3. [3]
    R. G. Gallager, Low Density Parity Check Codes, MIT Press, 1963.Google Scholar
  4. [4]
    D. J. C. MacKay, Good error-correcting codes based on very sparse matrices, Preprint.Google Scholar
  5. [5]
    D. J. C. MacKay and C. P. Hesketh, Sensitivity of low density parity check codes to decoding assumptions, Preprint.Google Scholar
  6. [6]
    J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann Publishers, 1988.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • J. Bond
    • 1
  • S. Hui
    • 2
  • H. Schmidt
    • 3
  1. 1.Science Applications International CorpSan Diego
  2. 2.Department of Mathematical SciencesSan Diego State UniversitySan Diego
  3. 3.Technology Service CorporationSilver Spring

Personalised recommendations