16 citations · 26 across the 5 of their papers we have counts for
5 papers
DISTINQT: A Distributed Privacy Aware Learning Framework for QoS Prediction for Future Mobile and Wireless Networks
Nikolaos Koursioumpas, Lina Magoula, Ioannis Stavrakakis +3
Beyond 5G and 6G networks are expected to support new and challenging use cases and applications that depend on a certain level of Quality of Service (QoS) to operate smoothly. Pre…
A Safe Deep Reinforcement Learning Approach for Energy Efficient Federated Learning in Wireless Communication Networks
Nikolaos Koursioumpas, Lina Magoula, Nikolaos Petropouleas +5
Progressing towards a new era of Artificial Intelligence (AI) - enabled wireless networks, concerns regarding the environmental impact of AI have been raised both in industry and a…
A Safe Genetic Algorithm Approach for Energy Efficient Federated Learning in Wireless Communication Networks
Lina Magoula, Nikolaos Koursioumpas, Alexandros-Ioannis Thanopoulos +4
Federated Learning (FL) has emerged as a decentralized technique, where contrary to traditional centralized approaches, devices perform a model training in a collaborative manner,…
Learning to Communicate with Intent: An Introduction
Miguel Angel Gutierrez-Estevez, Yiqun Wu, Chan Zhou
We propose a novel framework to learn how to communicate with intent, i.e., to transmit messages over a wireless communication channel based on the end-goal of the communication. T…
Hybrid Model and Data Driven Algorithm for Online Learning of Any-to-Any Path Loss Maps
M. A. Gutierrez-Estevez, Martin Kasparick, Renato L. G. Cavalvante +1
Learning any-to-any (A2A) path loss maps, where the objective is the reconstruction of path loss between any two given points in a map, might be a key enabler for many applications…