Two Novel Semi-/Auto-Adaptive SNR Algorithms to Efficiently Train Deep Neural SPA Decoders

In the past few years, deep learning has been widely used in various fields due to its outstanding progress. One of the latest applications of deep learning is to use a neural network (NN) with trainable multiplicative weights to design decoders for error-correcting codes. High quality data are essential for deep learning to train robust NN models. In this study, two novel semi-/auto-adaptive SNR algorithms are proposed to efficiently train the neural decoders based on the Sum-Product Algorithm (SPA). For illustration, several neural SPA decoders for the Bose-Chaudhuri-Hocquenghem (BCH) code and low-density parity-check (LDPC) code have been constructed as examples. Simulation results show that, compared with the original neural decoders, the performance of these neural decoders trained by the proposed algorithms can be improved in the range of 0.2 to 0.6 dB. Moreover, the training time required for these decoders to achieve convergence can be reduced by up to 28.8% for the BCH code, and up to 35.6% for the LDPC code, without increasing decoding complexity.