571 citations · 699 across the 14 of their papers we have counts for
14 papers · 1 filter
CRC-Aided Learned Ensembles of Belief-Propagation Polar Decoders
Tomer Raviv, Alon Goldman, Ofek Vayner +2
Polar codes have promising error-correction capabilities. Yet, decoding polar codes is often challenging, particularly with large blocks, with recently proposed decoders based on l…
Neural Decoding with Optimization of Node Activations
Eliya Nachmani, Yair Be'ery
The problem of maximum likelihood decoding with a neural decoder for error-correcting code is considered. It is shown that the neural decoder can be improved with two novel loss te…
Deep Ensemble of Weighted Viterbi Decoders for Tail-Biting Convolutional Codes
Tomer Raviv, Asaf Schwartz, Yair Be'ery
Tail-biting convolutional codes extend the classical zero-termination convolutional codes: Both encoding schemes force the equality of start and end states, but under the tail-biti…
perm2vec: Graph Permutation Selection for Decoding of Error Correction Codes using Self-Attention
Nir Raviv, Avi Caciularu, Tomer Raviv +2
Error correction codes are an integral part of communication applications, boosting the reliability of transmission. The optimal decoding of transmitted codewords is the maximum li…
Data-Driven Ensembles for Deep and Hard-Decision Hybrid Decoding
Tomer Raviv, Nir Raviv, Yair Be'ery
Ensemble models are widely used to solve complex tasks by their decomposition into multiple simpler tasks, each one solved locally by a single member of the ensemble. Decoding of e…
Active Deep Decoding of Linear Codes
Ishay Be'ery, Nir Raviv, Tomer Raviv +1
High quality data is essential in deep learning to train a robust model. While in other fields data is sparse and costly to collect, in error decoding it is free to query and label…
Near Maximum Likelihood Decoding with Deep Learning
Eliya Nachmani, Yaron Bachar, Elad Marciano +2
A novel and efficient neural decoder algorithm is proposed. The proposed decoder is based on the neural Belief Propagation algorithm and the Automorphism Group. By combining neural…
Deep Learning Methods for Improved Decoding of Linear Codes
Eliya Nachmani, Elad Marciano, Loren Lugosch +3
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 u…
RNN Decoding of Linear Block Codes
Eliya Nachmani, Elad Marciano, David Burshtein +1
Designing a practical, low complexity, close to optimal, channel decoder for powerful algebraic codes with short to moderate block length is an open research problem. Recently it h…
Learning to Decode Linear Codes Using Deep Learning
Eliya Nachmani, Yair Beery, David Burshtein
A novel deep learning method for improving the belief propagation algorithm is proposed. The method generalizes the standard belief propagation algorithm by assigning weights to th…
On Pseudocodewords and Improved Union Bound of Linear Programming Decoding of HDPC Codes
Ohad Gidon, Yair Be'ery
In this paper, we present an improved union bound on the Linear Programming (LP) decoding performance of the binary linear codes transmitted over an additive white Gaussian noise c…
On Pseudocodewords and Decision Regions of Linear Programming Decoding of HDPC Codes
Asi Lifshitz, Yair Be'ery
In this paper we explore the decision regions of Linear Programming (LP) decoding. We compare the decision regions of an LP decoder, a Belief Propagation (BP) decoder and the optim…
Efficient Linear Programming Decoding of HDPC Codes
Alex Yufit, Asi Lifshitz, Yair Be'ery
We propose several improvements for Linear Programming (LP) decoding algorithms for High Density Parity Check (HDPC) codes. First, we use the automorphism groups of a code to creat…
Moderate-Density Parity-Check Codes
Samuel Ouzan, Yair Be'ery
We propose a new type of short to moderate block-length, linear error-correcting codes, called moderate-density parity-check (MDPC) codes. The number of ones of the parity-check ma…