Summary
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.
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.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101109435 |
| Start date: | 01-01-2024 |
| End date: | 31-12-2025 |
| Total budget - Public funding: | - 172 618,00 Euro |
Cordis data
Original description
YAHYA-6G aims to propose new signal processing solutions doped with machine-learning. We will focus on the detectionand 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.
Status
SIGNEDCall topic
HORIZON-MSCA-2022-PF-01-01Update Date
12-03-2024
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