Infrared and Laser Engineering, Volume. 51, Issue 5, 20210420(2022)

Research on DNN-NOMS decoding method of polarization code in wireless optical communication

Hao Wen, Yang Cao, and Yuchao Dang
Author Affiliations
  • School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China
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    Figures & Tables(15)
    DNN neural network model assisted system model for polarization code decoding
    (8,4) factor diagram of polarization code BP decoding method
    (8,4) PE information transmission process of the processing unit of the polarization code
    (8,4) Dense Tanner graph (a) and sparse Tanner graph (b) of polarization codes
    (8,4) sparse neural network decoding structure of polarization codes
    Structure diagram of DNN-NOMS neural network decoder
    Comparison of the decoding performance of the neural network model under different network layers
    Evolution of loss function and training parameters
    Performance comparison of different BP decoding methods
    Performance comparison of decoding methods under different code rates
    Performance comparison of decoding methods under different turbulence intensities
    • Table 1. Symbol meaning of NOMS decoding method

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      Table 1. Symbol meaning of NOMS decoding method

      SymbolMeaning
      $ {r_{ji}} $Check the information passed from nodejto variable node i
      $ {q_{ij}} $Information passed from variable node i to verification node j
      $C{{(i)} }$Variable node iis the collection of adjacent verification nodes
      $V{{(j)/i} }$The combination of variable nodes adjacent to the check matrixj, in which the variable node i is removed
      $V{{(i)} }$Set of check nodes adjacent to variable node i
      ${i'}$${j'}$Variable node ${i'}$and check node ${j'}$ represent the next value transformed after iteration
      $ {u_i}(0) $The reception is a posteriori probability of Yicorresponding to codeword bit xi=0
      $ {u_i}(1) $The reception is a posteriori probability of Yi corresponding to codeword bit xi=1
      $ L(x) $Log likelihood ratio refers to the logarithm of the ratio of the probability of judging that the node is 0 to the probability of judging that the node is 1
      $ l $Represents the L-th hidden layer
    • Table 2. Simulation parameters

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      Table 2. Simulation parameters

      ParameterValue
      Length of polar code1024/4096
      Code rate0.25/0.5/0.75
      Turbulence intensity0.09/1.193/49.725
      ModulationPPM
    • Table 3. Calculation of optimal factor parameters under different turbulence intensity conditions

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      Table 3. Calculation of optimal factor parameters under different turbulence intensity conditions

      Turbulence intensityNormalization factorOffset factorLoss1Loss2
      0.090.440.890.41110.2624
      1.1930.390.850.47230.2839
      49.72150.480.920.54260.3108
    • Table 4. Comparison of decoding complexity of different BP modified decoding methods

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      Table 4. Comparison of decoding complexity of different BP modified decoding methods

      Decoding methods operation type MS (40) OMS (40) DNN-NOMS (5)
      Addition/Subtraction $ {T_{{\text{MS}}}}\left( {2 N{{\log }_2}N} \right) $819200 $ {T_{{\text{OMS}}}}\left( {2 N{{\log }_2}N{\text{ + }}1} \right) $819240 $ TN\left( {2 N{{\log }_2}N{\text{ + }}1} \right) $102405
      Multiplication/Division $ {T_{{\text{MS}}}}\left( {2 N{{\log }_2}N} \right) $819200 $ {T_{{\text{OMS}}}}\left( {2 N{{\log }_2}N} \right) $819200 $ TN\left( {2 N{{\log }_2}N{\text{ + }}1} \right) $102405
      Compare $ {T_{{\text{MS}}}}\left( {2 N{{\log }_2}N} \right) $819200 $ {T_{{\text{OMS}}}}\left( {2 N{{\log }_2}N} \right) $819200 $ TN\left( {2 N{{\log }_2}N} \right) $102400
      Storage space--$ 4 N{\log _2}N $
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    Hao Wen, Yang Cao, Yuchao Dang. Research on DNN-NOMS decoding method of polarization code in wireless optical communication[J]. Infrared and Laser Engineering, 2022, 51(5): 20210420

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    Paper Information

    Category: Optical communication and sensing

    Received: Jun. 22, 2021

    Accepted: --

    Published Online: Jun. 14, 2022

    The Author Email:

    DOI:10.3788/IRLA20210420

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