Big dAta aNalYtics for radio Access Networks

Informazioni relative al progetto

BANYAN

ID dell’accordo di sovvenzione: 860239

DOI

10.3030/860239

Progetto chiuso

Data della firma CE 27 Novembre 2019

Data di avvio 1 Dicembre 2019

Data di completamento 30 Aprile 2024

Finanziato da

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Costo totale

€ 1 360 393,92

Contributo UE

€ 1 360 393,92

Coordinato da

RANPLAN WIRELESS NETWORK DESIGN LTD
United Kingdom

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

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.

Pubblicazioni

Conference proceedings (17)

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

Autori: Bütün, Beyza; Akem, Aristide Tanyi-Jong; Gucciardo, Michele; Fiore, Marco
Pubblicato in: Crossref, Numero 1, 2023
Editore: 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

Autori: Aristide Tanyi-Jong Akem, Guillaume Fraysse, Marco Fiore
Pubblicato in: NOMS 2024-2024 IEEE Network Operations and Management Symposium, Numero 8, 2024, Pagina/e 1-9
Editore: IEEE
DOI: 10.1109/noms59830.2024.10575394

Showcasing In-Switch Machine Learning Inference

Autori: Akem Aritside; Bütün, Beyza; Gucciardo, Michele; Fiore, Marco
Pubblicato in: Crossref, Numero 3, 2022
Editore: 1st International Workshop on Native Network Intelligence
DOI: 10.1109/netsoft57336.2023.10175464

Henna

Autori: Aristide Tanyi-Jong Akem, Beyza Bütün, Michele Gucciardo, Marco Fiore
Pubblicato in: Proceedings of the 1st International Workshop on Native Network Intelligence, 2023
Editore: ACM
DOI: 10.1145/3565009.3569520

Impact of Public Protests on Mobile Networks

Autori: André F. Zanella, Orlando E. Martínez-Durive, Sachit Mishra, Diego Madariaga, Marco Fiore
Pubblicato in: IEEE INFOCOM 2024 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2024, Pagina/e 1-2
Editore: IEEE
DOI: 10.1109/infocomwkshps61880.2024.10620747

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

Autori: Sachit Mishra, André F. Zanella, Orlando E. Martínez-Durive, Diego Madariaga, Cezary Ziemlicki, Marco Fiore
Pubblicato in: IEEE INFOCOM 2024 - IEEE Conference on Computer Communications, 2024, Pagina/e 1531-1540
Editore: IEEE
DOI: 10.1109/infocom52122.2024.10621344

Demonstrating Flow-Level In-Switch Inference

Autori: Gucciardo, Michele; Akem, Aristide Tanyi-Jong; Bütün, Beyza; Fiore, Marco
Pubblicato in: Crossref, Numero 2, 2023
Editore: INFOCOM 2023
DOI: 10.1109/infocomwkshps57453.2023.10225967

IRDM: A generative diffusion model for indoor radio map interpolation

Autori: Kehai Q, Stefanos B, lan W, Hui S, Kan L, Jie Z
Pubblicato in: 2023
Editore: IEEE Global Communications Conference 2023
DOI: 10.1109/globecom54140.2023.10436970

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

Autori: André Felipe Zanella; Antonio Bazco-Nogueras; Cezary Ziemlicki; Marco Fiore
Pubblicato in: Crossref, Numero 2, 2023
Editore: Internet Measurement Conference. 2023
DOI: 10.1145/3618257.3624825

Characterizing Mobile Service Demands at Indoor Cellular Networks

Autori: Stefanos B, Andre F.Z, Stefania R, Cezary Z, Zbigniew S, lan W, Jie Z, Marco F
Pubblicato in: 2023
Editore: IMC '23: ACM Internet Measurement Conference
DOI: 10.1145/3618257.3624807

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

Autori: Bakirtzis, Stefanos; Wassell, Ian; Fiore, Marco; Zhang, Jie
Pubblicato in: Crossref, Numero 5, 2022
Editore: 2022 Globecom
DOI: 10.1109/globecom48099.2022.10001717

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

Autori: Aristide Tanyi-Jong Akem, Marco Fiore
Pubblicato in: NOMS 2024-2024 IEEE Network Operations and Management Symposium, Numero 28, 2024, Pagina/e 1-4
Editore: IEEE
DOI: 10.1109/noms59830.2024.10575655

DeepRay: Deep Learning Meets Ray-Tracing

Autori: Bakirtzis, S; Qiu, K; Zhang, J; Wassell, I
Pubblicato in: Crossref, Numero 10, 2022
Editore: 16th European Conference on Antennas and Propagation
DOI: 10.23919/eucap53622.2022.9769203

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

Autori: Akem Aristide Tanyi-Jong; Michele Gucciardo; Marco Fiore
Pubblicato in: Crossref, Numero 6, 2023
Editore: INFOCOM 2023
DOI: 10.1109/infocom53939.2023.10229100

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

Autori: Kehai Q, Stefanos B, Hui S, lan W, Jie Z,
Pubblicato in: 2023
Editore: IEEE International Conference on Acoustics
DOI: 10.1109/icassp49357.2023.10095501

Ray-Tracing Meets Deep Learning

Autori: Stefanos Bakirtzis, Kehai Qiu, Jie Zhang, Ian Wassell
Pubblicato in: 2022
Editore: EUCAP

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

Autori: Andre Felipe Zanella, Orlando E. Martinez-Durive, Sachit Mishra, Zbigniew Smoreda, Marco Fiore
Pubblicato in: IEEE INFOCOM 2022 - IEEE Conference on Computer Communications, 2022, Pagina/e 970-979
Editore: IEEE
DOI: 10.1109/infocom48880.2022.9796888

Peer reviewed articles (5)

Spatial and Temporal Exploratory Factor Analysis of Urban Mobile Data Traffic

Autori: Angelo Furno; André Felipe Zanella; Razvan Stanica; Marco Fiore
Pubblicato in: Crossref, Numero 1, 2024, ISSN 2948-135X
Editore: Data Science for Transportation
DOI: 10.1007/s42421-024-00089-y

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

Autori: Bakirtzis, S; Qiu, K; Wassell, I; Fiore, M; Zhang, J
Pubblicato in: instname:, Numero 4, 2022, ISSN 2327-4662
Editore: 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

Autori: Ferreira; Gabriel O.; Ravazzi; Chiara; Dabbene; Fabrizio; Calafiore; Giuseppe C.; Fiore; Marco
Pubblicato in: 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, Numero 1, 2023, ISSN 2169-3536
Editore: 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

Autori: Gabriel O. Ferreira, André F. Zanella, Stefanos Bakirtzis, Chiara Ravazzi, Fabrizio Dabbene, Giuseppe C. Calafiore, Ian Wassell, Jie Zhang, Marco Fiore
Pubblicato in: IEEE Journal on Selected Areas in Communications, 2024, Pagina/e 1-1, ISSN 0733-8716
Editore: Institute of Electrical and Electronics Engineers
DOI: 10.1109/jsac.2024.3431524

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

Autori: Bakirtzis, S; Chen, J; Qiu, K; Zhang, J; Wassell, I
Pubblicato in: Crossref, Numero 4, 2022, ISSN 1558-2221
Editore: 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

Autori: Jie Zhang; Ian Wassell; Marco Fiore; Stefanos Bakirtzis
Pubblicato in: Crossref, Numero 3, 2024
Editore: IEEE Networks
DOI: 10.36227/techrxiv.22682650.v1

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

Autori: Qiu, K; Bakirtzis, S; Song, H; Zhang, J; Wassell, I
Pubblicato in: Crossref, Numero 8, 2022
Editore: IEEE Wireless Communications Letters
DOI: 10.1109/lwc.2022.3175091