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

Aiming at the problem of poor confidence propagation decoding performance of polarization codes caused by atmospheric turbulence in wireless optical communication, a Deep Neural Networks-Normalized and Offset Min-Sum (DNN-NOMS) decoding method under wireless optical communication was proposed. First, the factor graph of the traditional belief propagation decoding algorithm for polarized codes had been transformed into Tanner graphs which similar to Low-density Parity Check (LDPC) codes. The Tanner graphs were expanded and transformed into Deep Neural Network (DNN) graphical representations. The Min-Sum (MS) decoding method added the normalization factor and the offset factor, at the same time to the edge weights of the Tanner graph were given, which simplified the calculation method of the log likelihood ratio of the polarization code. By limiting the number of training parameters, the factor parameters were selected under the condition of the minimum loss function, and trained to obtain the optimal normalization factor and offset factor of the decoding model . The simulation results show that under different atmospheric turbulence intensities, the decoding method can select better normalization factor and offset factor parameters under the premise of sacrificing smaller storage space, so as to obtain better error codes. The DNN-NOMS decoding method can produce a performance gain of 0.21-3.56 dB and reduce the number of iterations by 87.5% when the error rate is 10⁻⁴.