14 citations · 14 across the 6 of their papers we have counts for
6 papers
Accident-Driven Congestion Prediction and Simulation: An Explainable Framework Using Advanced Clustering and Bayesian Networks
Kranthi Kumar Talluri, Galia Weidl, Vaishnavi Kasuluru
Traffic congestion due to uncertainties, such as accidents, is a significant issue in urban areas, as the ripple effect of accidents causes longer delays, increased emissions, and…
Probabilistic Forecasting for Network Resource Analysis in Integrated Terrestrial and Non-Terrestrial Networks
Cristian J. Vaca-Rubio, Vaishnavi Kasuluru, Engin Zeydan +4
Efficient resource management is critical for Non-Terrestrial Networks (NTNs) to provide consistent, high-quality service in remote and under-served regions. While traditional sing…
Minimizing Power Consumption under SINR Constraints for Cell-Free Massive MIMO in O-RAN
Vaishnavi Kasuluru, Luis Blanco, Miguel Angel Vazquez +2
This paper deals with the problem of energy consumption minimization in Open RAN cell-free (CF) massive Multiple-Input Multiple-Output (mMIMO) systems under minimum per-user signal…
On the Impact of PRB Load Uncertainty Forecasting for Sustainable Open RAN
Vaishnavi Kasuluru, Luis Blanco, Cristian J. Vaca-Rubio +1
The transition to sustainable Open Radio Access Network (O-RAN) architectures brings new challenges for resource management, especially in predicting the utilization of Physical Re…
Enhancing Cloud-Native Resource Allocation with Probabilistic Forecasting Techniques in O-RAN
Vaishnavi Kasuluru, Luis Blanco, Engin Zeydan +2
The need for intelligent and efficient resource provisioning for the productive management of resources in real-world scenarios is growing with the evolution of telecommunications…
On the use of Probabilistic Forecasting for Network Analysis in Open RAN
Vaishnavi Kasuluru, Luis Blanco, Engin Zeydan
Unlike other single-point Artificial Intelligence (AI)-based prediction techniques, such as Long-Short Term Memory (LSTM), probabilistic forecasting techniques (e.g., DeepAR and Tr…