The CENTRIC European project aims to leverage Artificial Intelligence techniques through a top-down, modular approach to wireless connectivity that puts the users’ communication needs and environmental constraints at the center of requirements for the wireless network communications protocol stack design.
The CENTRIC project is designing a user-centric AI Air Interface (AI-AI) applying AI techniques to create and customize tailor-made waveforms, dynamically configure transceivers, and enable appropriate signaling and medium access protocols to support these requirements. The results of CENTRIC will be validated and demonstrated in laboratory prototypes and its breakthroughs will enable future 6G use cases, such as self-driving vehicles, the internet of nano bio-things, or multi-sensory holographic communications.
PROJECT VISION
CENTRIC positions the AI-AI as the essential fabric of future wireless connectivity systems, for the benefit of mobile network operators and private networks markets, which will profit from highly customizable systems wherein different customers have entirely distinct personalized needs. For instance, a university campus is unlikely to have the same connectivity requirements as an indoor smart factory, as well as new and radically different communication needs, will emerge in the near future. Addressing them with a traditional multi-purpose wireless network would significantly limit the scope of future applications, and potentially prevent many cutting-edge inventions from fulfilling their potential.
The CENTRIC aims at being “future-proof,” endowing the networks of tomorrow with AI tools to support a range of new applications.
CENTRIC will explore and deliver AI-based breakthroughs in some of the most pressing 6G Radio Resource Management (RRM) areas, such as sustainable data management at the wireless edge, Electro-Magnetic Field (EMF) exposure control in novel network architectures and energy savings, seamless interoperability among services and applications, etc.
PROJECT OBJECTIVES
Objective 1 – To develop AI methods for the discovery of novel and efficient waveforms
- Machine Learning (ML) techniques that enable the discovery and optimization of tailor-made waveforms targeting the user communication needs while accounting the radio-front and computing hardware limitations as well as the propagation environment
Objective 2 – To develop AI methods for the discovery of novel and efficient transceivers
- Efficient AI-based transceivers for two particularly challenging use-cases: large-scale extreme massive multiple-input multiple-output (MIMO) deployments beam-based communications in millimeter-wave (mmWave) spectrum
Objective 3 – To develop AI methods for the discovery of customized lightweight communication protocols
- Learning-to-communicate (L2C) techniques based on multi-agent reinforcement learning (MARL), semantic communications, and edge control/caching.
Objective 4 – To introduce novel end-to-end hardware co-design solutions for energy-efficient AI-native transceivers
- CENTRIC will explore, design and validate AI accelerators that are based on both standard digital technology and emerging mixed-analog digital platforms based on non-volatile memory elements.
Objective 5 – To develop training and monitoring environments as enablers for AI-AI deployments
- tools for network designers and operators to deploy, train, and monitor AI models general CENTRIC framework based on digital twins (DT), which encompasses cloud-based agents running networked AI models of physical agent counterparts
Objective 6 – To validate user-centric AI-AI solutions in a lab setting
- CENTRIC will define novel testing and benchmarking frameworks for user-centric AI-based communications.
Objective 7 – To demonstrate and disseminate AI-AI concepts
- Laboratory proof-of-concept (PoC) implementations to validate the expected gains and shed light on the feasibility and cost of the AI-AI implementation.
