Abstract: Disclosed herein is a neural network based pre-decoder comprising a permutation embedding engine, a permutation classifier each comprising one or more trained neural networks and a selection unit. The permutation embedding engine is trained to compute a plurality of permutation embedding vectors each for a respective one of a plurality of permutations of a received codeword encoded using an error correction code and transmitted over a transmission channel subject to interference. The permutation classifier is trained to compute a decode score for each of the plurality of permutations expressing its probability to be successfully decoded based on classification of the plurality of permutation embedding vectors coupled with the plurality of permutations. The selection unit is configured to output one or more selected permutations having a highest decode score. One or more decoders may be then applied to recover the encoded codeword by decoding the one or more selected permutations.

Abstract: Provided herein are methods and systems for applying an ensemble comprising a plurality of neural network based decoders trained using actively selected training samples for decoding error correction encoded codewords which are also encoded for error detection before transmitted over transmission channels subject to interference. In particular, each of the neural network based decoders is associated with only a limited size region of the distribution space of the error correction code where the distribution space is partitioned based on error detection values computed for the encoded codewords. As such each of the decoders is specialized for decoding encoded codewords mapped to its limited size associated region. During run-time a received encoded codeword may be mapped to one of the regions and may be fed accordingly to one of the neural network based decoders of the ensemble which is associated with the mapped region.

Abstract: Provided herein are methods and systems for applying active learning to train neural network based decoders to decode error correction codes transmitted over transmission channels subject to interference. The decoder may be trained using training samples actively by mapping a distribution of a large pool of samples and selecting samples estimated to most contribute to the training, specifically to exclude high SNR samples expected to be correctly decoded and low SNR samples which are potentially un-decodable. Further presented are ensembles of neural network based decoders applied to decode error correction codes. Each of the decoders of the ensemble is actively learned and trained using samples mapped into a respective region of the training samples distribution and is therefore optimized for the respective region. In runtime, the received code may be directed to one or more of the ensemble's decoders according to the region into which the received code is mapped.