The Design and Implementation of General Symmetrical Self-Organizing Map Decoder

Traditional error correcting decoder is designed according to specific algorithm, which is a reverse course of encoding. In this paper, a novel general neural network decoder is proposed in the form of symmetrical self-organizing map (SSOM), which can achieve decoding function to any error correcting codes. The SSOM decoder is tested by decoding low-density parity-check (LDPC) code. The performance comparison of SSOM decoder and traditional decoder is accomplished by simulation. Actual results show that the SSOM decoder can implement both learning and decoding in the same time regardless of any encoding rules. And higher possibility of the codeword emergence means greater probability of correct error correction. Compare to traditional error correcting decoder, it is easy to construct, more intelligent to different codeword sets, which has certain prospect in future communication channel coding field.