SOURCE-LINKED INTELLIGENCE

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

CORDIS · observation · Publication date unknown

er 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. Massive MIMO, EH, NOMA, RF imperfections

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recordType

award

status

SIGNED

region

EU

value

172618.56

unit

EUR