Origami

L. Schiavo, G. Garcia-Aviles, A. Saavedra, M. Gramaglia, M. Fiore, A. Banchs, X. Costa-Perez. ACM MobiCom, 2024.

CloudRIC is a system that meets specific reliability targets in 5G FEC processing while sharing pools of heterogeneous processors among DUs, which leads to more cost- and energy-efficient vRANs. The details of the solution are presented in CloudRIC: Open Radio Access Network (O-RAN) Virtualization with Shared Heterogeneous Computing.

S. Raffeck, S. Grøsvik, S. Lange, T. Hoßfeld, T. Zinner, S. Geißler. AAAI Conference on Artificial Intelligence, 2024.

This 5G campus network dataset contains 6 distinct CSV files. Three for each of the testbeds located at either the Norwegian University of Science and Technology(NTNU) or the University of Wuerzburg(WUE).

V. Vomhoff, M. Sichermann, S. Geißler, A. Lutu, M. Giess, T. Hoßfeld. TMA Conference, 2024.

This repository contains a description and sample data for the Paper A Shortcut through the IPX: Measuring Latencies in Global Mobile Roaming with Regional Breakouts published at the Network Traffic Measurement and Analysis (TMA) Conference 2024.

Milani, Mattia. IEEE, 2024.

This dataset contains all the raw (and processed) information for thousands of experiments that transfer a video on-demand from a core network to a UE emulated through SRSRan inside docker containers. The dataset contains both the network statistics but also the evaluation of the QoE through VMAF for each experiment. In total, 3 different videos has been used with different characteristics and different network impairments has been applied.

Milani, Mattia. IEEE, 2024.

This dataset contains all the raw (and processed) information for thousands of experiments that transfer a video on-demand from a core network to a UE emulated through SRSRan inside docker containers. The dataset contains both the network statistics but also the evaluation of the QoE through VMAF for each experiment. In total, 3 different videos has been used with different characteristics and different network impairments has been applied.

Milani, Mattia. IEEE, 2024.

This dataset contains all the statistics collected during the training of multiple Neural Networks which uses the following architecture technologies: Siamese Neural Networks, Dynamic loss function modified during the training through Reinforcement learning and a 'Curriculum-Learning' cycle in between training cycles. The goal of the Models is to correctly identify anomalies in the QoE of real-time videos trough the network KPIs.