8 citations · 11 across the 16 of their papers we have counts for
5 papers · 1 filter
EMForecaster: A Deep Learning Framework for Time Series Forecasting in Wireless Networks with Distribution-Free Uncertainty Quantification
Xavier Mootoo, Hina Tabassum, Luca Chiaraviglio
With the recent advancements in wireless technologies, forecasting electromagnetic field (EMF) exposure has become critical to enable proactive network spectrum and power allocatio…
Stochastic Sparse Sampling: A Framework for Variable-Length Medical Time Series Classification
Xavier Mootoo, Alan A. Díaz-Montiel, Milad Lankarany +1
While the majority of time series classification research has focused on modeling fixed-length sequences, variable-length time series classification (VTSC) remains critical in heal…
Optimizing Vehicular Networks with Variational Quantum Circuits-based Reinforcement Learning
Zijiang Yan, Ramsundar Tanikella, Hina Tabassum
In vehicular networks (VNets), ensuring both road safety and dependable network connectivity is of utmost importance. Achieving this necessitates the creation of resilient and effi…
Generalized Multi-Objective Reinforcement Learning with Envelope Updates in URLLC-enabled Vehicular Networks
Zijiang Yan, Hina Tabassum
We develop a novel multi-objective reinforcement learning (MORL) framework to jointly optimize wireless network selection and autonomous driving policies in a multi-band vehicular…
Non-orthogonal Age-Optimal Information Dissemination in Vehicular Networks: A Meta Multi-Objective Reinforcement Learning Approach
A. A. Habob, H. Tabassum, O. Waqar
This paper considers minimizing the age-of-information (AoI) and transmit power consumption in a vehicular network, where a roadside unit (RSU) provides timely updates about a set…