… 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 …

… Data is a vital part of deep learning methods, yet we see that it is not fully comprehended.
Many researchers focus on preliminary choice of training data, followed by passive generation …

… Essentially, we provide a deep learning-based method to obtain a good set of parameters
and penalty functions in the ADMMpenalized decoders. Simulation results demonstrate that …

… 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 …

… linear code incorporating the noisy output feedback can perform well [8]. This is especially
troubling since all practical codes are linear and linear codes … padding (ZP) technique; we pad …

… 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 …