{'fraction_non_alphanumeric': 0.026336173508907823, 'fraction_numerical': 0.001549186676994578, 'mean_word_length': 4.568965517241379, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Low-Latency Neural Decoders for Linear and Non-Linear Block Codes

We consider the design of efficient neural-network based algorithms, referred to as neural decoders, for decoding linear and non-linear block codes, such as Hamming and constant-weight codes, respectively. Our goal is to study the impact of the number of layers and the number of hidden nodes in each layer on the performance and generalization ability of neural decoders. Specifically, we want to find the minimum number of hidden layers and the minimum number of hidden nodes in each layer required to achieve or closely approach the performance of the optimal maximum-likelihood soft-decision (ML-SD) decoder for a given code. For the linear block codes studied, we find that when the block-length is n, a neural network with a single hidden layer with n hidden units is necessary and sufficient to achieve the performance of the ML-SD decoder. For the non-linear block codes studied, a single hidden layer with n nodes results in performance that closely approaches that of the ML-SD decoder. In both the cases, we find that training at a signal-to-noise ratio (SNR) of 0 dB gives fairly good generalization ability, meaning that the neural decoder trained at an SNR of 0 dB works well across a wide range of SNR values.