76 citations · 170 across the 12 of their papers we have counts for
12 papers
Leveraging Multi-Step Traffic Forecasts for Multi-Period Planning Optical Networks
Giannis Savva, Hafsa Maryam, Venkatesh Chebolu +2
In this work, multi-step traffic predictions are leveraged to enable multi-period planning in reconfigurable optical networks. The proposed framework aims to achieve spectrum savin…
A Fair Federated Learning Framework for Collaborative Network Traffic Prediction and Resource Allocation
Saroj Kumar Panda, Tania Panayiotou, Georgios Ellinas +1
In the beyond 5G era, AI/ML empowered realworld digital twins (DTs) will enable diverse network operators to collaboratively optimize their networks, ultimately improving end-user…
Adaptive Autopilot: Constrained DRL for Diverse Driving Behaviors
Dinesh Cyril Selvaraj, Christian Vitale, Tania Panayiotou +3
In pursuit of autonomous vehicles, achieving human-like driving behavior is vital. This study introduces adaptive autopilot (AA), a unique framework utilizing constrained-deep rein…
Edge-Assisted ML-Aided Uncertainty-Aware Vehicle Collision Avoidance at Urban Intersections
Dinesh Cyril Selvaraj, Christian Vitale, Tania Panayiotou +3
Intersection crossing represents one of the most dangerous sections of the road infrastructure and Connected Vehicles (CVs) can serve as a revolutionary solution to the problem. In…
Multi-Step Traffic Prediction for Multi-Period Planning in Optical Networks
Hafsa Maryam, Tania Panayiotou, Georgios Ellinas
A multi-period planning framework is proposed that exploits multi-step ahead traffic predictions to address service overprovisioning and improve adaptability to traffic changes, wh…
A Multi-task Learning Framework for Drone State Identification and Trajectory Prediction
Antreas Palamas, Nicolas Souli, Tania Panayiotou +2
The rise of unmanned aerial vehicle (UAV) operations, as well as the vulnerability of the UAVs' sensors, has led to the need for proper monitoring systems for detecting any abnorma…
Machine Learning for Real-Time Anomaly Detection in Optical Networks
Sadananda Behera, Tania Panayiotou, Georgios Ellinas
This work proposes a real-time anomaly detection scheme that leverages the multi-step ahead prediction capabilities of encoder-decoder (ED) deep learning models with recurrent unit…
Survey on Machine Learning for Traffic-Driven Service Provisioning in Optical Networks
Tania Panayiotou, Maria Michalopoulou, Georgios Ellinas
The unprecedented growth of the global Internet traffic, coupled with the large spatio-temporal fluctuations that create, to some extent, predictable tidal traffic conditions, are…
Modeling Soft-Failure Evolution for Triggering Timely Repair with Low QoT Margins
Sadananda Behera, Tania Panayiotou, Georgios Ellinas
In this work, the capabilities of an encoder-decoder learning framework are leveraged to predict soft-failure evolution over a long future horizon. This enables the triggering of t…
Fair Resource Allocation in Optical Networks under Tidal Traffic
Tania Panayiotou, Georgios Ellinas
We propose an alpha-fair routing and spectrum allocation (RSA) framework for reconfigurable elastic optical networks under modeled tidal traffic, that is based on the maximization…
Centralized and Distributed Machine Learning-Based QoT Estimation for Sliceable Optical Networks
Tania Panayiotou, Giannis Savva, Ioannis Tomkos +1
Dynamic network slicing has emerged as a promising and fundamental framework for meeting 5G's diverse use cases. As machine learning (ML) is expected to play a pivotal role in the…
Machine Learning for QoT Estimation of Unseen Optical Network States
Tania Panayiotou, Giannis Savva, Behnam Shariati +2
We apply deep graph convolutional neural networks for Quality-of-Transmission estimation of unseen network states capturing, apart from other important impairments, the inter-core…