Name
Abstract | ||
|---|---|---|
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 a priori information about the lattice structure lead to a simplification of the model. |
Year | Venue | Keywords |
|---|---|---|
2018 | 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) | Closest Vector Problem,Neural Network,Machine Learning,Lattice Reduction. |
DocType | Volume | ISSN |
Conference | abs/1807.00592 | 2018 6th IEEE Global Conference on Signal and Information
Processing |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vincent Corlay | 1 | 1 | 0.70 |
| Joseph J. Boutros | 2 | 183 | 17.05 |
| Philippe Ciblat | 3 | 516 | 56.63 |
| Loïc Brunel | 4 | 147 | 14.09 |