571 citations · 922 across the 17 of their papers we have counts for
20 papers · 1 filter
On the error correction of iterative bounded distance decoding of generalized LDPC codes
David Burshtein
Consider an ensemble of regular generalized LDPC (GLDPC) codes and assume that the same component code is associated with each parity check node. To decode a GLDPC code from the en…
Efficient Belief Propagation List Ordered Statistics Decoding of Polar Codes
Yonatan Urman, Guy Mogilevsky, David Burshtein
New algorithms for efficient decoding of polar codes (which may be CRC-augmented), transmitted over either a binary erasure channel (BEC) or an additive white Gaussian noise channe…
Efficient Maximum Likelihood Decoding of Polar Codes Over the Binary Erasure Channel
Yonatan Urman, David Burshtein
A new algorithm for efficient exact maximum likelihood decoding of polar codes (which may be CRC augmented), transmitted over the binary erasure channel, is presented. The algorith…
Belief Propagation List Ordered Statistics Decoding of Polar Codes
Guy Mogilevsky, David Burshtein
It is shown how to combine ordered statistics decoding (OSD) with CRC-aided belief propagation list (CBPL) decoding of polar codes. Even when the reprocessing order of the OSD is a…
Feedback Channel Communication with Low Precision Arithmetic
Yonatan Urman, David Burshtein
The problem of communicating over an additive white Gaussian noise channel with feedback, using low precision arithmetic, is considered. The Schalkwijk-Kailath (SK) scheme is known…
On Polar Coding for Binary Dirty Paper
Barak Beilin, David Burshtein
The problem of communication over binary dirty paper (DP) using nested polar codes is considered. An improved scheme, focusing on low delay, short to moderate blocklength communica…
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…
Performance Bounds of Concatenated Polar Coding Schemes
Dina Goldin, David Burshtein
A concatenated coding scheme over binary memoryless symmetric (BMS) channels using a polarization transformation followed by outer sub-codes is analyzed. Achievable error exponents…
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 the Finite Length Scaling of Ternary Polar Codes
Dina Goldin, David Burshtein
The polarization process of polar codes over a ternary alphabet is studied. Recently it has been shown that the scaling of the blocklength of polar codes with prime alphabet size s…
Improved Bounds on the Finite Length Scaling of Polar Codes
Dina Goldin, David Burshtein
Improved bounds on the blocklength required to communicate over binary-input channels using polar codes, below some given error probability, are derived. For that purpose, an impro…
Polar write once memory codes
David Burshtein, Alona Strugatski
A coding scheme for write once memory (WOM) using polar codes is presented. It is shown that the scheme achieves the capacity region of noiseless WOMs when an arbitrary number of m…
The approximate maximum-likelihood certificate
Idan Goldenberg, David Burshtein
A new property which relies on the linear programming (LP) decoder, the approximate maximum-likelihood certificate (AMLC), is introduced. When using the belief propagation decoder,…
Improved linear programming decoding of LDPC codes and bounds on the minimum and fractional distance
David Burshtein, Idan Goldenberg
We examine LDPC codes decoded using linear programming (LP). Four contributions to the LP framework are presented. First, a new method of tightening the LP relaxation, and thus imp…
Error Bounds for Repeat-Accumulate Codes Decoded via Linear Programming
Idan Goldenberg, David Burshtein
We examine regular and irregular repeat-accumulate (RA) codes with repetition degrees which are all even. For these codes and with a particular choice of an interleaver, we give an…
Upper Bound on Error Exponent of Regular LDPC Codes Transmitted over the BEC
Idan Goldenberg, David Burshtein
The error performance of the ensemble of typical LDPC codes transmitted over the binary erasure channel (BEC) is analyzed. In the past, lower bounds on the error exponents were der…
On the Fading Paper Achievable Region of the Fading MIMO Broadcast Channel
Amir Bennatan, David Burshtein
We consider transmission over the ergodic fading multi-antenna broadcast (MIMO-BC) channel with partial channel state information at the transmitter and full information at the rec…
Design and Analysis of Nonbinary LDPC Codes for Arbitrary Discrete-Memoryless Channels
Amir Bennatan, David Burshtein
We present an analysis, under iterative decoding, of coset LDPC codes over GF(q), designed for use over arbitrary discrete-memoryless channels (particularly nonbinary and asymmetri…