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Volume 28 Issue 10
Sep.  2010
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Zheng He, HuHan-ying, Zhou Hua-ying. New Approach to Optimal Approximation of Tanh Rule for LDPC Codes under the Gaussian Approximation[J]. Journal of Electronics & Information Technology, 2006, 28(10): 1837-1841.
Citation: Zheng He, HuHan-ying, Zhou Hua-ying. New Approach to Optimal Approximation of Tanh Rule for LDPC Codes under the Gaussian Approximation[J]. Journal of Electronics & Information Technology, 2006, 28(10): 1837-1841.

New Approach to Optimal Approximation of Tanh Rule for LDPC Codes under the Gaussian Approximation

  • Received Date: 2005-01-17
  • Rev Recd Date: 2006-01-27
  • Publish Date: 2006-10-19
  • In this paper, a new approach is presented for optimizing the approximation of tanh rule based on Minimum Mean Square Error (MMSE) criterion under the Gaussian approximation. New concepts of anti-symmetric distribution and isomorphic generalized symmetric distribution are introduced. Under the isomorphic generalized symmetric distribution, several useful conclusions are drawn, by which a practical method for computing the optimal approximation is also presented. In comparison with the conventional approximation presented by Hagenauer, simulation results for several (3,6) regular Low-Density Parity-Check (LDPC) codes on the Additive White Gaussian Noise (AWGN) channel show that the approach can improve the decoding performance with a little increase in decoding complexity.
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  • Callager R C. Low-Density Parity-Check Codes. Cambridge,MA: MIT Press, 1963, Chapter 1.[2]Tanner R M. A recursive approach to low complexity codes.IEEE Trcms. on I匆orm. Theory, 1981, IT-27(5): 533-547.[3]Richardson T J, Shokrollahi M A, Urbanke R L. Design ofcapacity-approaching irregular low-density parity-check codes[J].IEEETrans. onlnform. Theory.2001, 47(2):619-637[4]Luby M C, Mitzenmacher M, Shokrollahi M A, Spielman D A.Efficient erasure correcting codes[J].IEEE Trans. on Inform. Theory.2001, 47(2):569-584[5]Hagenauer J, Offer E,block and convolutionalPapkecodes.L. Iterative decoding of binaryZF.F.F.7YCL12,Son InformTheory,1996, 42(2): 429-445.[6]Ha J, Kim J, McLaughlin S W Rate-compatible puncturing of low-density parity-check codes. IEEE Trans[J].on Inform. Theory.2004, 50(11):2824-2836[7]Chung S Y, Richardson T J, Urbanke R L. Analysis of sum-product decoding of low-density parity-check codes using a Caussian approximation[J].IEEE Trans. on Iorm. Theory.2001, 47(2):657-670[8]Hagenauer J. Source-controlled channel decoding[J].IEEE Trans. on6}mmun.1995, 43(9):2449-2457[9]MacKay D J C. Good error-correcting codes based on very sparse matrices[J].IEEE Trans. on I匆orm. Theory.1999, 45(2):399-431[10]Chen J, Fossorier M. Near optimum universal belief propagation based decoding of low-density parity check codes[J].IEEE Trans. on6}mmun.2002, 50(3):406-414[11]Chen J, Fossorier M. Density evolution for two improved BP-based decoding algorithms of LDPC codes[J].IEEE Commun. Levers.2002, 6(5):208-210[12]Wei L. Several properties of short LDPC codes[J].IEEE Trans. on Commun.2004, 52(5):721-727
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