Generalization Bounds for Neural Belief Propagation Decoders

arXiv:2305.10540v2 Announce Type: replace-cross
Abstract: Machine learning based approaches are being increasingly used for designing decoders for next generation communication systems. One widely used framework is neural belief propagation (NBP), which unfolds the belief propagation (BP) iterations into a deep neural network and the parameters are trained in a data-driven manner. NBP decoders have been shown to improve upon classical decoding algorithms. In this paper, we investigate the generalization capabilities of NBP decoders. Specifically, the generalization gap of a decoder …

abstract arxiv belief communication cs.it cs.lg data data-driven deep neural network designing framework machine machine learning math.it network neural network next parameters propagation systems type