Adaptive Learned Message Passing Algorithms for Decoding Error Correcting Codes

Inhalt:
The weighted belief propagation (WBP) for the decoding of the linear block codes is considered. In WBP, the Tanner graph of the code is unrolled with respect to the iterations of the belief propagation decoder. Then, weights are assigned to the edges of the resulting recurrent network, and optimized offline using a training data set. In this paper, an adaptive neural decoder is proposed, where the weights of the decoder are determined for each received word. Two variants of this decoder are investigated. In the parallel weighted min-sum (WMS) decoder, the weights take values in a discrete set. A number of WMS decoders are run in parallel to search for the best sequence of weights in realtime. In the two-stage decoder, a small neural network is used to determine the weights of the WMS decoder for each received word. The findings show that the adaptive neural decoders offer substantial improvements in the bit error rate compared to their static counterparts for several codes, at about the same computational complexity.