Neural Successive Cancellation Decoding of Polar Codes

Neural network (NN) based decoders have appeared as potential candidates to replace successive cancellation (SC) based and belief propagation (BP) decoders for polar codes, due to their one-shot-decoding property. Partitioned NN (PNN) decoder has provided a solution to make use of multiple NN decoders which are connected with BP decoding, with the presence of insufficient training data for practical-length polar codes. However, PNN decoder requires BP iterations that detrimentally affect the decoding latency as compared to non-iterative approaches. In this paper, we propose a neural SC (NSC) decoder to overcome the issue associated with PNN. Unlike PNN, the NSC decoder is constructed by multiple NN decoders connected with SC decoding. Compared to a PNN decoder for a polar code of length 128 and rate 0.5, the proposed NSC decoder achieves the same decoding performance, while reducing the decoding latency by 42.5%.