Search Results for author:
Found 13 papers, 1 papers with code
CSI Compression using Channel Charting
Reaping the benefits of multi-antenna communication systems in frequency division duplex (FDD) requires channel state information (CSI) reporting from mobile users to the base station (BS).
Differentiable High-Order Markov Models for Spectrum Prediction
The advent of deep learning and recurrent neural networks revolutionized the field of time-series processing.
Physically Parameterized Differentiable MUSIC for DoA Estimation with Uncalibrated Arrays
Additionally, the proposed method outperforms the classical MUSIC algorithm in the DoA estimation task.
Fusion of Time and Angle Measurements for Digital-Twin-Aided Probabilistic 3D Positioning
Previous studies explained how the 2D positioning problem in indoor non line-of-sight environments can be addressed using ray tracing with noisy angle of arrival (AoA) measurements.
Active learning for efficient data selection in radio-signal based positioning via deep learning
As a result, to reduce the required size of the dataset, it may be interesting to carefully choose the positions to be labelled and to be used in the training.
Model-based learning for multi-antenna multi-frequency location-to-channel mapping
This paper leverages the model-based machine learning paradigm to derive a problem-specific neural architecture from a propagation channel model.
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.
