7 papers
Neural network approaches to point lattice decoding
Vincent Corlay, Joseph J. Boutros, Philippe Ciblat +1
We characterize the complexity of the lattice decoding problem from a neural network perspective. The notion of Voronoi-reduced basis is introduced to restrict the space of solutio…
On the decoding of lattices constructed via a single parity check
Vincent Corlay, Joseph J. Boutros, Philippe Ciblat +1
This paper investigates the decoding of a remarkable set of lattices: We treat in a unified framework the Leech lattice in dimension 24, the Nebe lattice in dimension 72, and the B…
On the decoding of Barnes-Wall lattices
Vincent Corlay, Joseph J. Boutros, Philippe Ciblat +1
We present new efficient recursive decoders for the Barnes-Wall lattices based on their squaring construction. The analysis of the new decoders reveals a quasi-quadratic complexity…
On the CVP for the root lattices via folding with deep ReLU neural networks
Vincent Corlay, Joseph J. Boutros, Philippe Ciblat +1
Point lattices and their decoding via neural networks are considered in this paper. Lattice decoding in Rn, known as the closest vector problem (CVP), becomes a classification prob…
A lattice-based approach to the expressivity of deep ReLU neural networks
Vincent Corlay, Joseph J. Boutros, Philippe Ciblat +1
We present new families of continuous piecewise linear (CPWL) functions in Rn having a number of affine pieces growing exponentially in . We show that these functions can be see…
Multilevel MIMO Detection with Deep Learning
Vincent Corlay, Joseph J. Boutros, Philippe Ciblat +1
A quasi-static flat multiple-antenna channel is considered. We show how real multilevel modulation symbols can be detected via deep neural networks. A multi-plateau sigmoid functio…