no code implementations • 14 Apr 2024 • Merim Dzaferagic, Bruno Missi Xavier, Diarmuid Collins, Vince D'Onofrio, Magnos Martinello, Marco Ruffini
We propose a Long-Short Term Memory Machine Learning approach that takes standard Radio Access Network measurements to predict handover events.
no code implementations • 4 Aug 2023 • Agastya Raj, Zehao Wang, Frank Slyne, Tingjun Chen, Dan Kilper, Marco Ruffini
We present a novel ML framework for modeling the wavelength-dependent gain of multiple EDFAs, based on semi-supervised, self-normalizing neural networks, enabling one-shot transfer learning.
no code implementations • 13 Jan 2023 • Merim Dzaferagic, Jose A. Ayala-Romero, Marco Ruffini
The flexibility introduced with the Open Radio Access Network (O-RAN) architecture allows us to think beyond static configurations in all parts of the network.
no code implementations • 2 Jan 2023 • Sourav Mondal, Marco Ruffini
The open-radio access network (O-RAN) embraces cloudification and network function virtualization for base-band function processing by dis-aggregated radio units (RUs), distributed units (DUs), and centralized units (CUs).
no code implementations • 21 Mar 2018 • Francesco Musumeci, Cristina Rottondi, Avishek Nag, Irene Macaluso, Darko Zibar, Marco Ruffini, Massimo Tornatore
Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data.