164 citations · 1.2k across the 72 of their papers we have counts for
6 papers · 2 filters
Understanding Phase Transitions via Mutual Information and MMSE
Galen Reeves, Henry Pfister
The ability to understand and solve high-dimensional inference problems is essential for modern data science. This article examines high-dimensional inference problems through the…
Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding
Fabrizio Carpi, Christian Häger, Marco Martalò +2
In this paper, we use reinforcement learning to find effective decoding strategies for binary linear codes. We start by reviewing several iterative decoding algorithms that involve…
Polar Codes for the Deletion Channel: Weak and Strong Polarization
Ido Tal, Henry D. Pfister, Arman Fazeli +1
This paper presents the first proof of polarization for the deletion channel with a constant deletion rate and a regular hidden-Markov input distribution. A key part of this work i…
Kerdock Codes Determine Unitary 2-Designs
Trung Can, Narayanan Rengaswamy, Robert Calderbank +1
The non-linear binary Kerdock codes are known to be Gray images of certain extended cyclic codes of length over . We show that exponentiating these $\mathbb…
Learned Belief-Propagation Decoding with Simple Scaling and SNR Adaptation
Mengke Lian, Fabrizio Carpi, Christian Häger +1
We consider the weighted belief-propagation (WBP) decoder recently proposed by Nachmani et al. where different weights are introduced for each Tanner graph edge and optimized using…
What Can Machine Learning Teach Us about Communications?
Mengke Lian, Christian Häger, Henry D. Pfister
Rapid improvements in machine learning over the past decade are beginning to have far-reaching effects. For communications, engineers with limited domain expertise can now use off-…