Big dAta aNalYtics for radio Access Networks

Informations projet

BANYAN

N° de convention de subvention: 860239

DOI

10.3030/860239

Projet clôturé

Date de signature de la CE 27 Novembre 2019

Date de début 1 Decembre 2019

Date de fin 30 Avril 2024

Financé au titre de

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Coût total

€ 1 360 393,92

Contribution de l’UE

€ 1 360 393,92

Coordonné par

RANPLAN WIRELESS NETWORK DESIGN LTD
United Kingdom

CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.

Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Livrables

Documents, reports (11)

Recommendation to 3GPP on how to rank buildings for phased in-building mobile network deployment

The report will describe the output of Task 2.4: Rank buildings for phased in-building mobile network deployment. Details of T2.4 can be found in WP2 description.

Orchestrators for network slice management at the mobile edge

The report will describe the output of Task 3.1: Anticipatory edge network slices orchestration. Details of T3.1 can be found in WP3 description.

Mobile traffic demand predictors

The report will describe the output of Task 1.3: Predictors of mobile service demands. Details of T1.3 can be found in WP1 description.

Report on proactive optimization of indoor networks (to 3GPP, NGMN, Small Cell Forum, etc.)

The report will describe the output of Task 3.3: Proactive optimization of indoor networks. Details of T3.3 can be found in WP3 description.

Report on algorithms to localise indoor UEs

The report will describe the output of Task 2.2: Localise in-building UEs in 3D. Details of T2.2 can be found in WP2 description.

Report on in-building mobile traffic characterization

The report will describe the output of Task 2.3: Characterise in-building mobile traffic. Details of T2.3 can be found in WP2 description.

Report on mobile traffic demand multi-scale analytics

The report will describe the output of Task 12 Multiscale analytics for mobile service demands Details of T12 can be found in WP1 description

Advertising vacancies

The ESR posts will have been advertised

Report on mobile traffic demand baseline analytics

The report will describe the output of Task 11 Basic analytics for regular and irregular structures in macroscopic mobile service demands Details of T11 can be found in WP1 description

Report on joint outdoor-indoor optimization based on BDA (to 3GPP, NGMN, Small Cell Forum, etc.)

The report will describe the output of Task 3.2: Optimize and coordinate outdoor and indoor mobile networks. Details of T3.2 can be found in WP3 description.

Report on algorithms to geo-localise traffic to buildings

The report will describe the output of Task 21 Develop algorithms to geolocate in which building a UE is Details of T21 can be found in WP2 description

Other (5)

Summer school: Data-driven 5G RANs

Delivery of Summer school: Data-driven 5G RANsThe specific summer school on data-driven 5G RAN will be organized by CNR. This 3-day training school will deal with the new models applied for the 5G characterization, including channel, mobility, radio resource management and network traffic modelling, as well as addressing the visualization of dynamic results. Experts in the field from partners in the network will present relevant research results. The summer school will also be an occasion for ESRs and other PhD candidates or researchers to present their on-going research activities and obtain feedback from senior attendees.

Scientific and technological workshop

Delivery of Scientific and technological workshop.A scientific and technological workshop organized by RPN and UCAM, and hosted by UCAM. The 2-day event will be open to the research community. Each ESR will write a scientific paper and present the current status of their work. Electronic copies of all papers and tutorial materials and video records of invited talks will be made available to the participants. ESRs will be directly involved in the organization of the event.

Complementary skills training workshops

Delivery of complementary skills training workshops.CC1. Intellectual propertyCC2. Publication strategiesCC3. Public engagement skillsCC4. Management skillsCC5. CommunicationCC6. Knowledge transfer and commercial exploitation of results CC7. Grant proposal writing and research policyCC8. Entrepreneurship

Summer school: Sci. and technological training

Delivery of Summer school - Sci. and technological trainingTC1. Mobile network data processing, modelling and analysisTC2. Machine LearningTC3. Selected Topics on Spatiotemporal System Analysis and Data Mining TC4. Indoor localisationTC5. Modelling & Optimization in Wireless NetworksTC6. Key Enabling Technologies for 5G

BANYAN training school

A BANYAN training school should have been organised by Month 35.

Publications

Conference proceedings (17)

Fast Detection of Cyberattacks on the Metaverse through User-plane Inference

Auteurs: Bütün, Beyza; Akem, Aristide Tanyi-Jong; Gucciardo, Michele; Fiore, Marco
Publié dans: Crossref, Numéro 1, 2023
Éditeur: 2023 IEEE International Conference on Metaverse Computing, Networking and Applications
DOI: 10.1109/metacom57706.2023.00067

Encrypted Traffic Classification at Line Rate in Programmable Switches with Machine Learning

Auteurs: Aristide Tanyi-Jong Akem, Guillaume Fraysse, Marco Fiore
Publié dans: NOMS 2024-2024 IEEE Network Operations and Management Symposium, Numéro 8, 2024, Page(s) 1-9
Éditeur: IEEE
DOI: 10.1109/noms59830.2024.10575394

Showcasing In-Switch Machine Learning Inference

Auteurs: Akem Aritside; Bütün, Beyza; Gucciardo, Michele; Fiore, Marco
Publié dans: Crossref, Numéro 3, 2022
Éditeur: 1st International Workshop on Native Network Intelligence
DOI: 10.1109/netsoft57336.2023.10175464

Henna

Auteurs: Aristide Tanyi-Jong Akem, Beyza Bütün, Michele Gucciardo, Marco Fiore
Publié dans: Proceedings of the 1st International Workshop on Native Network Intelligence, 2023
Éditeur: ACM
DOI: 10.1145/3565009.3569520

Impact of Public Protests on Mobile Networks

Auteurs: André F. Zanella, Orlando E. Martínez-Durive, Sachit Mishra, Diego Madariaga, Marco Fiore
Publié dans: IEEE INFOCOM 2024 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2024, Page(s) 1-2
Éditeur: IEEE
DOI: 10.1109/infocomwkshps61880.2024.10620747

Characterizing 5G Adoption and its Impact on Network Traffic and Mobile Service Consumption

Auteurs: Sachit Mishra, André F. Zanella, Orlando E. Martínez-Durive, Diego Madariaga, Cezary Ziemlicki, Marco Fiore
Publié dans: IEEE INFOCOM 2024 - IEEE Conference on Computer Communications, 2024, Page(s) 1531-1540
Éditeur: IEEE
DOI: 10.1109/infocom52122.2024.10621344

Demonstrating Flow-Level In-Switch Inference

Auteurs: Gucciardo, Michele; Akem, Aristide Tanyi-Jong; Bütün, Beyza; Fiore, Marco
Publié dans: Crossref, Numéro 2, 2023
Éditeur: INFOCOM 2023
DOI: 10.1109/infocomwkshps57453.2023.10225967

IRDM: A generative diffusion model for indoor radio map interpolation

Auteurs: Kehai Q, Stefanos B, lan W, Hui S, Kan L, Jie Z
Publié dans: 2023
Éditeur: IEEE Global Communications Conference 2023
DOI: 10.1109/globecom54140.2023.10436970

Characterizing and Modeling Session-Level Mobile Traffic Demands from Large-Scale Measurements

Auteurs: André Felipe Zanella; Antonio Bazco-Nogueras; Cezary Ziemlicki; Marco Fiore
Publié dans: Crossref, Numéro 2, 2023
Éditeur: Internet Measurement Conference. 2023
DOI: 10.1145/3618257.3624825

Characterizing Mobile Service Demands at Indoor Cellular Networks

Auteurs: Stefanos B, Andre F.Z, Stefania R, Cezary Z, Zbigniew S, lan W, Jie Z, Marco F
Publié dans: 2023
Éditeur: IMC '23: ACM Internet Measurement Conference
DOI: 10.1145/3618257.3624807

Stochastic Evaluation of Indoor Wireless Network Performance with Data-Driven Propagation Models

Auteurs: Bakirtzis, Stefanos; Wassell, Ian; Fiore, Marco; Zhang, Jie
Publié dans: Crossref, Numéro 5, 2022
Éditeur: 2022 Globecom
DOI: 10.1109/globecom48099.2022.10001717

Towards Data-Driven Management of Mobile Networks through User Plane Inference

Auteurs: Aristide Tanyi-Jong Akem, Marco Fiore
Publié dans: NOMS 2024-2024 IEEE Network Operations and Management Symposium, Numéro 28, 2024, Page(s) 1-4
Éditeur: IEEE
DOI: 10.1109/noms59830.2024.10575655

DeepRay: Deep Learning Meets Ray-Tracing

Auteurs: Bakirtzis, S; Qiu, K; Zhang, J; Wassell, I
Publié dans: Crossref, Numéro 10, 2022
Éditeur: 16th European Conference on Antennas and Propagation
DOI: 10.23919/eucap53622.2022.9769203

Flowrest: Practical Flow-Level Inference in Programmable Switches with Random Forests

Auteurs: Akem Aristide Tanyi-Jong; Michele Gucciardo; Marco Fiore
Publié dans: Crossref, Numéro 6, 2023
Éditeur: INFOCOM 2023
DOI: 10.1109/infocom53939.2023.10229100

Deep Learning-Based Path Loss Prediction For Outdoor Wireless Communication Systems

Auteurs: Kehai Q, Stefanos B, Hui S, lan W, Jie Z,
Publié dans: 2023
Éditeur: IEEE International Conference on Acoustics
DOI: 10.1109/icassp49357.2023.10095501

Ray-Tracing Meets Deep Learning

Auteurs: Stefanos Bakirtzis, Kehai Qiu, Jie Zhang, Ian Wassell
Publié dans: 2022
Éditeur: EUCAP

Impact of Later-Stages COVID-19 Response Measures on Spatiotemporal Mobile Service Usage

Auteurs: Andre Felipe Zanella, Orlando E. Martinez-Durive, Sachit Mishra, Zbigniew Smoreda, Marco Fiore
Publié dans: IEEE INFOCOM 2022 - IEEE Conference on Computer Communications, 2022, Page(s) 970-979
Éditeur: IEEE
DOI: 10.1109/infocom48880.2022.9796888

Peer reviewed articles (5)

Spatial and Temporal Exploratory Factor Analysis of Urban Mobile Data Traffic

Auteurs: Angelo Furno; André Felipe Zanella; Razvan Stanica; Marco Fiore
Publié dans: Crossref, Numéro 1, 2024, ISSN 2948-135X
Éditeur: Data Science for Transportation
DOI: 10.1007/s42421-024-00089-y

Deep-Learning-Based Multivariate Time-Series Classification for Indoor/Outdoor Detection

Auteurs: Bakirtzis, S; Qiu, K; Wassell, I; Fiore, M; Zhang, J
Publié dans: instname:, Numéro 4, 2022, ISSN 2327-4662
Éditeur: Institute of Electrical and Electronics Engineers Inc.
DOI: 10.1109/jiot.2022.3190555

Forecasting Network Traffic: A Survey and Tutorial With Open-Source Comparative Evaluation

Auteurs: Ferreira; Gabriel O.; Ravazzi; Chiara; Dabbene; Fabrizio; Calafiore; Giuseppe C.; Fiore; Marco
Publié dans: info:cnr-pdr/source/autori:Ferreira, Gabriel O. and Ravazzi, Chiara and Dabbene, Fabrizio and Calafiore, Giuseppe C. and Fiore, Marco/titolo:Forecasting Network Traffic: A Survey and Tutorial With Open-Source Comparative Evaluation/doi:10.1109%2FACCESS.2023.3236261/rivista:IEEE access/anno:2023/pagina_da:6018/pagina_a:6044/intervallo_pagine:6018–6044/volume:11, Numéro 1, 2023, ISSN 2169-3536
Éditeur: Institute of Electrical and Electronics Engineers Inc.
DOI: 10.1109/access.2023.3236261

A Joint Optimization Approach for Power-Efficient Heterogeneous OFDMA Radio Access Networks

Auteurs: Gabriel O. Ferreira, André F. Zanella, Stefanos Bakirtzis, Chiara Ravazzi, Fabrizio Dabbene, Giuseppe C. Calafiore, Ian Wassell, Jie Zhang, Marco Fiore
Publié dans: IEEE Journal on Selected Areas in Communications, 2024, Page(s) 1-1, ISSN 0733-8716
Éditeur: Institute of Electrical and Electronics Engineers
DOI: 10.1109/jsac.2024.3431524

EM DeepRay: An Expedient, Generalizable, and Realistic Data-Driven Indoor Propagation Model

Auteurs: Bakirtzis, S; Chen, J; Qiu, K; Zhang, J; Wassell, I
Publié dans: Crossref, Numéro 4, 2022, ISSN 1558-2221
Éditeur: IEEE Transactions on Antennas and Propagation
DOI: 10.1109/tap.2022.3172221

Other (2)

Expedient AI-assisted Indoor Wireless Network Planning with Data-Driven Propagation Models

Auteurs: Jie Zhang; Ian Wassell; Marco Fiore; Stefanos Bakirtzis
Publié dans: Crossref, Numéro 3, 2024
Éditeur: IEEE Networks
DOI: 10.36227/techrxiv.22682650.v1

Pseudo Ray-Tracing: Deep Leaning Assisted Outdoor mm-Wave Path Loss Prediction

Auteurs: Qiu, K; Bakirtzis, S; Song, H; Zhang, J; Wassell, I
Publié dans: Crossref, Numéro 8, 2022
Éditeur: IEEE Wireless Communications Letters
DOI: 10.1109/lwc.2022.3175091