Fast Prototyping of AI-Enhanced Solutions

Responsible: Luis Blanco

Team members: David López, Nikolaos Bartzoudis


Focused R&D area 1

  • Algorithmic design and experimental validation of AI-based optimization techniques for function placement and resource management in 5G and beyond wireless communications systems.
  • Design of new algorithms based on Machine Learning (ML) for the next generation of PHY and MAC layers, with special focus on link adaptation, interference management and radio resource allocation.
  • Research on AI-assisted sensor data analytics and predictive maintenance for IoT wireless systems.
    • Ongoing activities: ML for hardware Trojan detection in integrated circuits based on the near-field EM signature.


  • Algorithmic design, experimental validation and real-time implementation of novel ML techniques to solve complex multi-dimensional DP problems in multi-antenna multi-band systems (see Efficient Transmitter research topic for further information).


Focused R&D area 2

  • ML to solve complex multi-dimensional DPD problems, tune both PA auxiliary waveforms and analog parameters, and apply CFR, to meet the efficiency and linearity goals (new area, started 2019)



Related projects

  • National/regional: 5G-TRIDENT (on-going)
  • Industrial: DPD4CABLE (finished)

Developed technologies

  • Matlab-based experimental demonstrator for PA digital linearization (one-dimensional DPD and CFR) based on artificial neural networks.

Targeted R&D effort

  • Neural network based linearization of multi-antenna transmitters and multi-band systems with dynamic-supply and dynamic-load modulation PAs (multi-dimensional DPD).

Selected references