dYnAmic resource scHeduling in Massive MIMO based NOMA with RF imperfections and EnergY hArvesting for 6G networks

Project Information

YAHYA-6G

Grant agreement ID: 101109435

DOI 10.3030/101109435

EC signature date 12 December 2023

Start date 1 January 2024

End date 31 December 2025

Funded under

• Marie Skłodowska-Curie Actions (MSCA)

Total cost No data

EU contribution € 172 618,56

Coordinated by

INSTITUTO DE TELECOMUNICACOES
Portugal

Project description

6G IoT: managing radio frequency imperfections, enhancing energy efficiency

Future 6G wireless networks will orchestrate a tremendous symphony of interconnected, edge-connected devices (IoT), many of which will require rapid, high-integrity signal transmission for safety-critical applications such as self-driving cars and remote surgery. The functioning of these IoT networks, consisting of massive multiple input multiple output-based non-orthogonal multiple access pairs, is threatened by radio frequency imperfections. With the support of the Marie Skłodowska-Curie Actions programme, the YAHYA-6G project aims to improve the integrity and functioning of these 6G networks, while optimising their energy efficiency. This will be accomplished through a combination of stochastic optimisation, deep learning, and new algorithms – to be demonstrated in a software defined radio application.

Objective

YAHYA-6G aims to propose new signal processing solutions doped with machine-learning. We will focus on the detection
and compensation of RF imperfections in mMIMO (massive Multiple input Multiple output) based NOMA (Non-orthogonal multiple
access) pair . In other hand, YAHYA-6G target is to minimize the long-term power consumption based on the stochastic optimization theory for mMIMO-NOMA IoT networks with EH (Energy Harvesting) in presence of RF imperfections. Thus the objectives of the YAHYA-6G project are:
1- Identify major RF imperfections that may occur in a multi-access / multi-antenna broadband system.
2- Propose new solutions to optimize the energy efficiency at the RF transmitters. This solution will focus on the power amplifier that represents 60 at 70% of the energy consumed in an RF transmitter.
3- Analyze the impact of these RF imperfections on mobile radio systems exploiting NOMA technologies.
4- Propose a Deep Learning online learning process to detect the NOMA channel characteristics and compensate the effect of HPA nonlinearity. A joint detection of the NOMA interference and HPA (High Power Amplifier) nonlinearity will be studied
in mMIMO-NOMA system.
5- Resolve a non convex based problem coping with the expected 6G requirements, with a particular focus on optimal resource scheduling and computation capacity allocation and reducing energy consumption of wireless devices, through a set of new algorithms .
6- Realize a demonstrator based on the SDR (Software Defined Radio) USRP cards on which some algorithms developed in
the project will be implemented.

Fields of science (EuroSciVoc)

CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.

• natural sciences › computer and information sciences › internet › internet of things
• natural sciences › computer and information sciences › software

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Keywords

• Massive MIMO
• EH
• NOMA
• RF imperfections

Programme(s)

• HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme

Topic(s)

• HORIZON-MSCA-2022-PF-01-01 - MSCA Postdoctoral Fellowships 2022

Call for proposal

HORIZON-MSCA-2022-PF-01

Funding Scheme

HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

Coordinator

INSTITUTO DE TELECOMUNICACOES

• Net EU contribution: € 172 618,56
• Address: CAMPUS UNIVERSITARIO DE SANTIAGO UNIVERSIDADE DE AVEIRO ; 3810 193 Gloria E Vera Cruz ; Portugal
• Activity type: Research Organisations
• Total cost: No data