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Neural network approaches to point lattice decoding
It is exponential in the space dimension $n$, which induces shallow neural networks of exponential size.
A lattice-based approach to the expressivity of deep ReLU neural networks
We prove that they can be computed by ReLU networks with quadratic depth and linear width in the space dimension.
On the CVP for the root lattices via folding with deep ReLU neural networks
Lattice decoding in Rn, known as the closest vector problem (CVP), becomes a classification problem in the fundamental parallelotope with a piecewise linear function defining the boundary.
Multilevel MIMO Detection with Deep Learning
A quasi-static flat multiple-antenna channel is considered.
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