logo_eWINE

website: https://ewine-project.eu/ 

eWINE stands for Elastic WIreless Networking Experimentation. It is EU H2020 project with a consortium consisting of the following partners: iMinds VZW, Trinity College Dublin, Technische Universität Berlin, Technische Universität Dresden, Institut “Jožef Stefan”, Thales Communications & Security S.A.S., Martel GmbH, SigFox Wireless SA, IS-Wireless, Spacetime Networks Oy.

The main goal of eWINE is to realize elastic networks that can scale to a high number of users in a short timespan through the use of an agile infrastructure (intelligent software and flexible hardware), enabling:

  • Dynamic on-demand end-to-end wireless connectivity service provisioning
  • Elastic resource sharing in dense heterogeneous and small cell networks (HetSNets)
  • Intelligent and informed configuration of the physical layer

eWINE will develop and validate algorithms for advanced Cognitive Networking (context determination & sensing, optimization & negotiation techniques, and online learning algorithms) through experimentally-driven research on top of existing FIRE/FIRE+ facilities (CREW, WiSHFUL, FLEX).

Within eWINE IS-Wireless will contribute to benchmarking and coexistence verification between LTE and proposed 5G waveforms.

Project Deliverables

  • D1.1 Y1 periodic project report
  • D1.2 IPR management report – Download
  • D1.3 Final project report
  • D1.4 Data Management Plan – Download
  • D2.1 Specification of showcases – Download
  • D2.2a Development and integration of showcases
  • D2.2b Demonstration and integration of showcases
  • D2.3 End-to-end demonstration and evaluation of showcases
  • D3.1 Context provisioning and sensing algorithms design and implementation – Download
  • D3.2 Context provisioning and modules – Download
  • D3.3 Demonstration and evaluation of the context provisioning and sensing algorithms
  • D3.4 Context provisioning and sensing demonstrator
  • D4.1 Optimization and negotiation algorithms design and implementation – Download
  • D4.2 Optimization and negotiation modules
  • D4.3 Demonstration and evaluation of the optimization and negotiation algorithms
  • D4.4 Optimization and negotiation algorithm demonstrator
  • D5.1 Machine learning algorithms development and implementation – Download
  • D5.2 Machine learning modules – Download
  • D5.3 Demonstration and evaluation of the machine learning algorithms
  • D5.4 Learning to adapt demonstrator
  • D6.1 Project web site – Download
  • D6.2 Dissemination and communication strategy plan – Download
  • D6.4.1 Report on dissemination and communication activities – Download
  • D6.4.2 Report on dissemination and communication activities
  • D6.5 Report on the eWINE Grand Challenge
  • D6.6 Final report on exploitation strategy and plans

Tutorials / Panels / Presentations

  • Jemielity, M. “Custom eNB scheduler and lessons learnt from OAI development” , OAI Eurecom, May 2016 – View materials
  • Pietrzyk, S., Kwiatkowski, Ł., “5G versus 4G waveforms benchmarking based on link-level modeling tools and SDR hardware applied in education and research”, IEEE Globecom, December 2016 – View materials
  • Pietrzyk, S.  „5G Small Cells as the enabling ComTech solution”, IEEE ICC, May 2017 – View materials
  • Ul, M., Kwiatkowski, Ł., “5G Toolset – The Easiest Way to Explain Signal Processing in Communication”, Signal Processing Symposium, September 2017