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Integrating wireless communication engineering and machine learning

Risultati finali

Final report on prediction and anticipatory optimisation for wireless channels

Final report on prediction and anticipatory optimisation for wireless channels

Novel ML techniques for wireless network optimisation

Novel ML techniques for wireless network optimisation

Report on dissemination/outreach of the project and plan for final two years

Report on dissemination/outreach of the project and plan for final two years

Dissemination plan

Dissemination plan

Supervisory Board of the network

Supervisory Board of the network – due date month 2

Final report on training activities

Final report on training activities

Initial report on training activities

Initial report on training activities

Intermediate 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

Intermediate report on machine learning techniques for wireless channels

Identify challenges of conventional ML in wireless networks

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

Initial report on MLfor radio resource management and initial datasets

State of the art on context acquisition & anticipatory optimisation techniques for network optimisation

State of the art on context acquisition & anticipatory optimisation techniques for network optimisation

Final report on machine learning techniques for radio resource management

Final report on machine learning techniques for radio resource management

Performance assessment of the proposed ML techniques

Performance assessment of the proposed ML techniques

Massive-MIMO mmWave channel model and system simulator description

Massive-MIMO mmWave channel model and system simulator description

Intermediate report on machine learning techniques for radio resource management

Intermediate report on machine learning techniques for radio resource management

Pubblicazioni

Wireless Control of Autonomous Guided Vehicle Using Reinforcement Learning

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

Motion Pattern Recognition in 4D Point Clouds

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

Coordinated Uplink Precoding for Spatially Consistent mmWave Channel Covariance Measurements

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

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

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

Team Deep Mixture of Experts for Distributed Power Control

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

Extending the ns-3 QUIC Module

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

SMURF: Reliable Multipath Routing in Flying Ad-Hoc Networks

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

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

Autori: Vaca Rubio, Cristian Jesús; Espinosa, Pablo Ramirez; Williams, Robin Jess; Kansanen, Kimmo; Tan, Zheng-Hua; De Carvalho, Elisabeth; Popovski, Petar
Pubblicato in: 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
Editore: Springer

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