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Found 7 papers, 0 papers with code
Context-Conditioned Spatio-Temporal Predictive Learning for Reliable V2V Channel Prediction
Achieving reliable multidimensional Vehicle-to-Vehicle (V2V) channel state information (CSI) prediction is both challenging and crucial for optimizing downstream tasks that depend on instantaneous CSI.
Wireless Channel Aware Data Augmentation Methods for Deep Learning-Based Indoor Localization
The data collection is usually a laborious and time-consuming task, but Data Augmentation (DA) can be used to alleviate this issue.
Simple and Effective Augmentation Methods for CSI Based Indoor Localization
We also showed that if we further augment the dataset with the proposed techniques, test accuracy is improved more than three-fold.
A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals
In this paper, we provide a comprehensive survey of ML-based localization solutions that use RF signals.
A Machine Learning Solution for Beam Tracking in mmWave Systems
Utilizing millimeter-wave (mmWave) frequencies for wireless communication in \emph{mobile} systems is challenging since it requires continuous tracking of the beam direction.
Supervised ML Solution for Band Assignment in Dual-Band Systems with Omnidirectional and Directional Antennas
This paper considers the band assignment (BA) problem in dual-band systems, where the basestation (BS) chooses one of the two available frequency bands (centimeter-wave and millimeter-wave bands) to communicate with the user equipment (UE).
Band Assignment in Dual Band Systems: A Learning-based Approach
In this work, we use a machine learning framework to provide an efficient and practical solution to the band assignment problem.
