Volume 31 Issue 09
Sep.  2005
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Dong Zhenghong, Zhou Hui, Zhu Renfenget al. Using neural network inverse control system to improve the performance of turbo decoding[J]. Journal of Beijing University of Aeronautics and Astronautics, 2005, 31(09): 989-993. (in Chinese)
Citation: Dong Zhenghong, Zhou Hui, Zhu Renfenget al. Using neural network inverse control system to improve the performance of turbo decoding[J]. Journal of Beijing University of Aeronautics and Astronautics, 2005, 31(09): 989-993. (in Chinese)

Using neural network inverse control system to improve the performance of turbo decoding

  • Received Date: 14 May 2004
  • Publish Date: 30 Sep 2005
  • To solve inverse decoding model problem of turbo codes, an adaptive inverse turbo decoding model was proposed, based on neural network nonlinear filter. Desired coefficients attenuation of cross-entropy (CE) was gotten by using optimal constant scaling factor with statistical principle. The auto-regressive exogenous input (NARX) neural network was trained to build the mapping model between the input and output of turbo inverse decoding all round by the desired coefficients attenuation. Compared with the nonlinear inverse control system nonlinear structure based,the proposed adaptive inverse model with linear structure has simpler structure, less computation. Simulations show that when the signal to noise ratio (SNR) is greater than 0 dB,the inverse model algorithm converges rapidly and stably with computational error being within a minimal bound. The proposed adaptive inverse model for turbo decoding has agreement with turbo decoding mechanism and provides a new way to improve the performance of turbo decoding.

     

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