Lattice decoders constructed with neural networks are presented. Firstly, we show how the fundamental parallelotope is used as a compact set for the approximation by a neural lattice decoder. Secondly, we introduce the notion of Voronoi-reduced lattice basis. As a consequence, a first optimal neural lattice decoder is built from Boolean equations and the facets of the Voronoi cell. This decoder needs no learning. Finally, we present two neural decoders with learning. It is shown that L1 regularization and {\em a priori} information about the lattice structure lead to a simplification of the model.
Cite
@article{arxiv.1807.00592,
title = {Neural Lattice Decoders},
author = {Vincent Corlay and Joseph J. Boutros and Philippe Ciblat and Loic Brunel},
journal= {arXiv preprint arXiv:1807.00592},
year = {2019}
}
Comments
6 pages, 5 figures, 2nd version of the 5-page paper initially submitted to the 2018 Sixth IEEE Global Conference on Signal and Information Processing