Iteration-Aware Dynamic Quantization Neural Belief Propagation Decoder

For some linear codes, neural networks have been found to be a way to enhance traditional belief propagation (BP) decoder. When deploying such methods in practice, quantization issues also need to be considered. Considering that the messages of variable/check nodes are variable during the BP decoding process, this paper proposes a dynamic quantization method to predict the optimal bit-width for the node's message quantization in different decoding iterations. We build a shallow neural network as a bit-width predictor and train it jointly with a neural belief propagation decoding network. During actual decoding, the bit prediction network will generate appropriate bit-width for quantizing the messages of each layer of nodes. Finally, simulation experiments validate the effectiveness of the proposed quantization method.