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)
- National/regional: 5G-TRIDENT (on-going)
- Industrial: DPD4CABLE (finished)
- 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).
- Machine Learning for Digital-Front-End: a Comprehensive Overview (book chapter)
- Linealización digital de transmisores mediante redes neuronales no lineales (natl. conference article, 2019)