… We apply our method to different linear codes, BCH(63, 45), BCH(63, 36), BCH(127, 64)
and BCH(127, 99). In all experiments the results on the training, validation and test sets were …

… In this work we applied deep learning techniques to improve the performance of the BP
algorithm. We showed that a “soft” Tanner graph can produce improvements when used in the …

… deep neural networks (DNN) to soft decoding of linear codes at … in methods whose primary
components are neural networks. … application of deep learning to error correction coding. An …

… We propose training methods that discover novel non-linear codes when trained … -linear
codes improves significantly upon the baseline code it was built on (both RM and Polar codes) …

… codes are linear and linear codes are known to achieve capacity (without feedback) [10],
whereas [9] proposes an asymptotically optimal and nonlinear coding … or learning methodology …

… In the last few years, in the encoding method of linear codes based on the syndrome equation,
methods based on deep learning are used to solve the equation of the symbol and also to …

… framework based on a complex valued linear combination. Finally, we introduce a phase-aware,
parametric normalization method for complex spectrograms. We provide details of our …

… to maximum likelihood decoding (MLD) of linear block error-correcting codes (ECCs) [6]: an
… One can see that the length N is not crucial to learn a code by deep learning techniques. …

… deep learning-based ECC decoding. In [33], the authors proposed … neural network (RNN)-based
decoding for linear codes, … ’ery, “Deep learning methods for improved decoding of linear …

… Deep Learning has been proved a powerful method of solving … Lately, Deep Learning has
been adopted by an increasing … , "Learning to decode linear codes using deep learning," …