Deep Learning Methods for Improved Decoding of Linear Codes

• Published in: IEEE journal of selected topics in signal processing 2018-02, Vol.12 (1), p.119-131
• Main Authors: Nachmani, Eliya, Marciano, Elad, Lugosch, Loren, Gross, Warren J., Burshtein, David, Be'ery, Yair
• Format: Article
• Language: English
• Subjects: BCH codes; Belief propagation; Complexity; Decoders; Decoding; Deep learning; error correcting codes; Graphical representations; Linear codes; Machine learning; min-sum decoding; Neural networks; Parity check codes; Performance enhancement; Propagation; Recurrent neural networks; Signal processing algorithms; Teaching methods
• ISSN: 1932-4553; 1941-0484
• DOI: 10.1109/JSTSP.2017.2788405

The problem of low complexity, close to optimal, channel decoding of linear codes with short to moderate block length is considered. It is shown that deep learning methods can be used to improve a standard belief propagation decoder, despite the large example space. Similar improvements are obtained for the min-sum algorithm. It is also shown that tying the parameters of the decoders across iterations, so as to form a recurrent neural network architecture, can be implemented with comparable results. The advantage is that significantly less parameters are required. We also introduce a recurrent neural decoder architecture based on the method of successive relaxation. Improvements over standard belief propagation are also observed on sparser Tanner graph representations of the codes. Furthermore, we demonstrate that the neural belief propagation decoder can be used to improve the performance, or alternatively reduce the computational complexity, of a close to optimal decoder of short BCH codes.