Integrating wireless communication engineering and machine learning

Informations projet

WINDMILL

N° de convention de subvention: 813999

DOI

10.3030/813999

Projet clôturé

Date de signature de la CE 13 Septembre 2018

Date de début 1 Janvier 2019

Date de fin 30 Juin 2023

Financé au titre de

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Coût total

€ 4 072 823,64

Contribution de l’UE

€ 4 072 823,64

Coordonné par

AALBORG UNIVERSITET
Denmark

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 (19)

Final report on prediction and anticipatory optimisation for wireless channels
Novel ML techniques for wireless network optimisation
Report on dissemination/outreach of the project and plan for final two years
Dissemination plan
Supervisory Board of the network

Supervisory Board of the network – due date month 2

Final report on training activities
Initial report on training activities
Intermediate report on training activities
Final report on dissemination/outreach of the project

Final report on disseminationoutreach of the project

Intermediate report on machine learning techniques for wireless channels
Identify challenges of conventional ML in wireless networks
System-wide cognitive optimisation schemes. Final results: cognitive network slicing and related security aspects

Systemwide cognitive optimisation schemes Final results cognitive network slicing and related security aspects

Hierarchical and distributed learning architecture & multi-objective optimisation strategies

Hierarchical and distributed learning architecture multiobjective optimisation strategies

Initial report on MLfor radio resource management and initial datasets
State of the art on context acquisition & anticipatory optimisation techniques for network optimisation
Final report on machine learning techniques for radio resource management
Performance assessment of the proposed ML techniques
Massive-MIMO mmWave channel model and system simulator description
Intermediate report on machine learning techniques for radio resource management

Publications

Conference proceedings (7)

Wireless Control of Autonomous Guided Vehicle Using Reinforcement Learning

Auteurs: Pedro M. de Sant Ana, Nikolaj Marchenko, Petar Popovski, Beatriz Soret
Publié dans: GLOBECOM 2020 - 2020 IEEE Global Communications Conference, 2020, Page(s) 1-7, ISBN 978-1-7281-8298-8
Éditeur: IEEE
DOI: 10.1109/globecom42002.2020.9322156

Motion Pattern Recognition in 4D Point Clouds

Auteurs: Dariush Salami, Sameera Palipana, Manila Kodali, Stephan Sigg
Publié dans: 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP), 2020, Page(s) 1-6, ISBN 978-1-7281-6662-9
Éditeur: IEEE
DOI: 10.1109/mlsp49062.2020.9231569

Coordinated Uplink Precoding for Spatially Consistent mmWave Channel Covariance Measurements

Auteurs: Hanan Al-Tous, Parham Kazemi, Olav Tirkkonen
Publié dans: 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2020, Page(s) 1-5, ISBN 978-1-7281-5478-7
Éditeur: IEEE
DOI: 10.1109/spawc48557.2020.9154333

The challenges of Scheduling and Resource Allocation in IEEE 802.11ad/ay

Auteurs: Salman Mohebi, Mattia Lecci, Andrea Zanella, Michele Zorzi
Publié dans: 2020 Mediterranean Communication and Computer Networking Conference (MedComNet), 2020, Page(s) 1-4, ISBN 978-1-7281-6248-5
Éditeur: IEEE
DOI: 10.1109/medcomnet49392.2020.9191491

Team Deep Mixture of Experts for Distributed Power Control

Auteurs: Matteo Zecchin, David Gesbert, Marios Kountouris
Publié dans: 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2020, Page(s) 1-5, ISBN 978-1-7281-5478-7
Éditeur: IEEE
DOI: 10.1109/spawc48557.2020.9154235

Extending the ns-3 QUIC Module

Auteurs: Umberto Paro, Federico Chiariotti, Anay Ajit Deshpande, Michele Polese, Andrea Zanella, Michele Zorzi
Publié dans: Proceedings of the 23rd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, 2020, Page(s) 19-26, ISBN 9781450381178
Éditeur: ACM
DOI: 10.1145/3416010.3423224

SMURF: Reliable Multipath Routing in Flying Ad-Hoc Networks

Auteurs: Anay Ajit Deshpande, Federico Chiariotti, Andrea Zanella
Publié dans: 2020 Mediterranean Communication and Computer Networking Conference (MedComNet), 2020, Page(s) 1-8, ISBN 978-1-7281-6248-5
Éditeur: IEEE
DOI: 10.1109/medcomnet49392.2020.9191526

Other (1)

A Primer on Large Intelligent Surface (LIS) for Wireless Sensing in an Industrial Setting

Auteurs: Vaca Rubio, Cristian Jesús; Espinosa, Pablo Ramirez; Williams, Robin Jess; Kansanen, Kimmo; Tan, Zheng-Hua; De Carvalho, Elisabeth; Popovski, Petar
Publié dans: Vaca Rubio , C J , Espinosa , P R , Williams , R J , Kansanen , K , Tan , Z-H , De Carvalho , E & Popovski , P 2021 , A Primer on Large Intelligent Surface (LIS) for Wireless Sensing in an Industrial Setting . in EAI CROWNCOM 2020 - 15th EAI International Conference on Cognitive Radio Oriented Wireless Networks ., 2020
Éditeur: Springer