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Found 7 papers, 0 papers with code
Model-based Deep Learning for Beam Prediction based on a Channel Chart
In non-standalone or cell-free systems, chart locations computed at a given base station can be transmitted to several other base stations (possibly operating at different frequency bands) for them to predict which beams to use.
Model-based learning for location-to-channel mapping
In order to overcome this limitation, this paper presents a frugal, model-based network that separates the low frequency from the high frequency components of the target mapping function.
Minimizing the Outage Probability in a Markov Decision Process
Standard Markov decision process (MDP) and reinforcement learning algorithms optimize the policy with respect to the expected gain.
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.
