Nina Slamnik-Kriještorac, Girma Mamuye Yilma, Faqir Zarrar Yousaf, Marco Liebsch, Johann M. Marquez-Barja: "Multi-domain MEC orchestration platform for enhanced Back Situation Awareness", IEEE Conference on Computer Communications (INFOCOM) 2021
Network Function Virtualization (NFV) and Multi-Access Edge Computing (MEC) are among the key technology pillars of 5G systems & beyond for fostering and enhancing the performance of new and existing use cases. In the context of public safety, 5G offers great opportunities towards enhancing mission-critical services, by running network functions at the network edge to provide reliable & low-latency services. This demo introduces an on-demand Back Situation Awareness (BSA) application service, in a multi-domain scenario, enabling early notification for vehicles of an approaching Emergency Vehicle (EmV), indicating its Estimated Time of Arrival (ETA). The application provides the drivers ample time to create a safety corridor for the EmV to pass through unhindered in a safe manner thereby increasing the mission success. For this demo, we have developed an orchestrated MEC platform on which we have implemented the BSA service following modern cloud-native principles, based on Docker and Kubernetes.
Index Terms—MEC, 5G, multi-domain back situation awareness, NFV orchestration, vehicular communications
Paper available at: https://ieeexplore.ieee.org/abstract/document/9484632
Jose A. Ayala-Romero, Andres Garcia-Saavedra, Xavier Costa-Perez, George Iosifidis: "Demonstrating a Bayesian Online Learning for Energy-Aware Resource Orchestration in vRANs", IEEE Conference on Computer Communications (INFOCOM), 2021
Radio Access Network Virtualization (vRAN) will spearhead the quest towards supple radio stacks that adapt to heterogeneous infrastructure: from energy-constrained platforms deploying cells-on-wheels (e.g., drones) or battery-powered cells to green edge clouds. We demonstrate a novel machine learning approach to solve resource orchestration problems in energy-constrained vRANs. Specifically, we demonstrate two algorithms: (i) BP-vRAN, which uses Bayesian online learning to balance performance and energy consumption, and (ii) SBP-vRAN, which augments our Bayesian optimization approach with safe controls that maximize performance while respecting hard power constraints. We show that our approaches are data-efficient—converge an order of magnitude faster than other machine learning methods—and have provably performance, which is paramount for carrier-grade vRANs. We demonstrate the advantages of our approach in a testbed comprised of fully-fledged LTE stacks and a power meter, and implementing our approaching into O-RAN’s non-real-time RAN Intelligent Controller (RIC).
F.W.Murti, J.A.Romero, A.Garcia-Saavedra, X.C.Pérez, G.Iosifidis: "Optimal Deployment Framework for Multi-Cloud Virtualized Radio Access Networks", IEEE Trans. Wireless Communications, 2021.
Virtualized radio access networks (vRAN) are emerging as a key component of wireless cellular networks, and it is therefore imperative to optimize their architecture. vRANs are decentralized systems where the Base Station (BS) functions can be split between the edge Distributed Units (DUs) and Cloud computing Units (CUs); hence they have many degrees of design freedom. We propose a framework for optimizing the number and location of CUs, the function split for each BS, and the association and routing for each DU-CU pair. We combine a linearization technique with a cutting-planes method to expedite the exact problem solution. The goal is to minimize the network costs and balance them with the criterion of centralization, i.e., the number of functions placed at CUs. Using data-driven simulations we find that multi-CU vRANs achieve cost savings up to 28% and improve centralization by 77%, compared to single-CU vRANs. Interestingly, we see non-trivial trade-offs among centralization and cost, which can be aligned or conflicting based on the traffic and network parameters. Our work sheds light on the vRAN design problem from a new angle, highlights the importance of deploying multiple CUs, and offers a rigorous optimization tool for balancing costs and performance.
Published in:IEEE Transactions on Wireless Communications
Full paper download: Optimal_Deployment_Framework_for_Multi-Cloud_vRANs_pre-print_2021.pdf
Antonio Albanese, Vincenzo Sciancalepore, Xavier Costa-Pérez: “SARDO: An Automated Search-and-Rescue "Drone-based Solution for Victims Localization", accepted to IEEE Transactions on Mobile Computing
Natural disasters affect millions of people every year. Finding missing persons in the shortest possible time is of crucial importance to reduce the death toll. This task is especially challenging when victims are sparsely distributed in large and/or difficult-to-reach areas and cellular networks are down. In this paper, we present SARDO, a drone-based search and rescue solution that leverages the high penetration rate of mobile phones in the society to localize missing people. SARDO is an autonomous, all-in-one drone-based mobile network solution that does not require infrastructure support or mobile phones modifications. It builds on novel concepts such as pseudo-trilateration combined with machine-learning techniques to efficiently locate mobile phones in a given area. Our results, with a prototype implementation in a field-trial, show that SARDO rapidly determines the location of mobile phones (~3 min/UE) in a given area with an accuracy of few tens of meters and at a low battery consumption cost (~5%). State-of-the-art localization solutions for disaster scenarios rely either on mobile infrastructure support or exploit onboard cameras for human/computer vision, IR, thermal-based localization. To the best of our knowledge, SARDO is the first drone-based cellular search-and-rescue solution able to accurately localize missing victims through mobile phones.
Accepted to: IEEE Transactions on Mobile Computing
To be published: 2021
U. Fattore, M. Liebsch, C. Bernardos, “UPFlight – An enabler for Avionic MEC in a Drone-extended 5G Mobile Network”, VTC 2020, May 2020
D. Bega, M. Gramaglia, M. Fiore, A. Banchs, X. Costa, “AZTEC: Anticipatory Capacity Allocation for Zero-Touch Network Slicing”, INFOCOM 2020, November 2019
F. Devoti, V. Sciancalepore, I. Filippini, X. Costa Pérez: "PASID: Exploiting Indoor mmWave Deployments for Passive Intrusion Detection", INFOCOM 2020, November 2019
JA. Ayala-Romero, A. Garcia-Saavedra, M.Gramaglia, X. Costa-Perez, A. Banchs, J.J. Alcaraz: “vrAIn: A Deep Learning Approach Tailoring Computing and Radio Resources in Virtualized RANs”, ACM MOBICOM 2019. July 2019
C. Marquez, M. Gramaglia, M. Fiore, A. Banchs, X. Costa-Perez, “Resource Sharing Efficiency in Network Slicing”, IEEE Transactions on Network and Service Management. June 2019
L. Zanzi, F. Cirillo, S. Mangiante, V. Sciancalepore, F. Giust, X CostaPerez and G. Klas, “Evolving Multi-Access Edge Computing to support enhanced IoT deployments”, IEEE Communications Standards Magazine. May 2019
D.Bega, M.Gramaglia, A.Banchs, V.Sciancalepore, X.Costa-Perez, “A Machine Learning approach to 5G Infrastructure Market optimization” IEEE Transactions Journal on Mobile Computing (TMC). Accepted Date March 2019
T. Taleb, A. Ibrahim, S. Konstantinos, F. Zarrar Yousaf, “On Multi-domain Network Slicing Orchestration Architecture & Federated Resource Control”, IEEE Network Magazine, Feb 2019
B. Han, V. Sciancalepore, D. Feng, X. Costa-Perez, H. D. Schotten, "A Utility-driven MultiQueue Admission Control Solution for Network Slicing", IEEE INFOCOM 2019 Accepted Date December 2018
D. Bega, M. Gramaglia, M. Fiore, A. Banchs, X. Costa-Perez, "DeepCog: Cognitive Network Management in Sliced 5G Networks with Deep Learning", IEEE INFOCOM 2019 Accepted Date December 2018