42 citations · 43 across the 6 of their papers we have counts for
9 papers
Sim2Field: End-to-End Development of AI RANs for 6G
Russell Ford, Hao Chen, Pranav Madadi +17
Following state-of-the-art research results, which showed the potential for significant performance gains by applying AI/ML techniques in the cellular Radio Access Network (RAN), t…
Context-Conditioned Spatio-Temporal Predictive Learning for Reliable V2V Channel Prediction
Lei Chu, Daoud Burghal, Rui Wang +2
Achieving reliable multidimensional Vehicle-to-Vehicle (V2V) channel state information (CSI) prediction is both challenging and crucial for optimizing downstream tasks that depend…
Wireless Channel Aware Data Augmentation Methods for Deep Learning-Based Indoor Localization
Omer Gokalp Serbetci, Daoud Burghal, Andreas F. Molisch
Indoor localization is a challenging problem that - unlike outdoor localization - lacks a universal and robust solution. Machine Learning (ML), particularly Deep Learning (DL), met…
Simple and Effective Augmentation Methods for CSI Based Indoor Localization
Omer Gokalp Serbetci, Ju-Hyung Lee, Daoud Burghal +1
Indoor localization is a challenging task. Compared to outdoor environments where GPS is dominant, there is no robust and almost-universal approach. Recently, machine learning (ML)…
A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals
Daoud Burghal, Ashwin T. Ravi, Varun Rao +2
The last few decades have witnessed a growing interest in location-based services. Using localization systems based on Radio Frequency (RF) signals has proven its efficacy for both…
A Machine Learning Solution for Beam Tracking in mmWave Systems
Daoud Burghal, Naveed A. Abbasi, Andreas F. Molisch
Utilizing millimeter-wave (mmWave) frequencies for wireless communication in \emph{mobile} systems is challenging since it requires continuous tracking of the beam direction. Recen…