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.