no code implementations • 1 Jun 2022 • Eliya Nachmani, Yair Be'ery
The problem of maximum likelihood decoding with a neural decoder for error-correcting code is considered.
no code implementations • 1 Jan 2021 • Nir Raviv, Avi Caciularu, Tomer Raviv, Jacob Goldberger, Yair Be'ery
Error correction codes are an integral part of communication applications and boost the reliability of transmission.
no code implementations • 6 Feb 2020 • Nir Raviv, Avi Caciularu, Tomer Raviv, Jacob Goldberger, Yair Be'ery
Error correction codes are an integral part of communication applications, boosting the reliability of transmission.
no code implementations • 6 Jun 2019 • Ishay Be'ery, Nir Raviv, Tomer Raviv, Yair Be'ery
High quality data is essential in deep learning to train a robust model.
no code implementations • 24 Nov 2018 • Weihong Xu, Xiaohu You, Chuan Zhang, Yair Be'ery
In this paper, we present a sparse neural network decoder (SNND) of polar codes based on belief propagation (BP) and deep learning.
no code implementations • 2 Apr 2018 • Xiaosi Tan, Weihong Xu, Yair Be'ery, Zaichen Zhang, Xiaohu You, Chuan Zhang
Numerical results are presented to demonstrate the performance of the DNN detectors in comparison with various BP modifications.
no code implementations • 8 Jan 2018 • Eliya Nachmani, Yaron Bachar, Elad Marciano, David Burshtein, Yair Be'ery
The proposed decoder is based on the neural Belief Propagation algorithm and the Automorphism Group.
no code implementations • 24 Feb 2017 • Eliya Nachmani, Elad Marciano, David Burshtein, Yair Be'ery
We also demonstrate improved performance over belief propagation on sparser Tanner graph representations of the codes.