Deep learning based enhancement of ordered statistics decoding of LDPC codes

Aiming at designing plausible decoders with channel information free, low complexity, high throughput, and approaching maximum likelihood performance, we put forward a streamlined architecture which concatenates sequentially three components. Specifically, to tackle the decoding failures of normalized min-sum, the whole decoding trajectory, not limited to the last iteration information conventionally, is fed into a trained convolutional neural network to yield new reliability metric for each sequence bit, termed decoding information aggregation. Then an adapted order statistics decoding, following the suggested decoding path, is adopted to process the sequence ordered with new metric more efficiently in that many invalid searches contained in conventional methods otherwise are evaded. The role of decoding information aggregation is elaborated via statistics data to reveal that it can arrange more error-prone bits into the fore part of most reliable basis of order statistics decoding, which is vital for the effective decoding enhancement. We argue the superposition of improved bitwise reliability of the most reliable basis and the imposed rigorous code structure by OSD enables the proposed architecture being a competitive rival of the state of the art decoders, which was verified in extensive simulation in terms of performance, complexity and latency for short and moderate LDPC codes.