CHIST-ERA Call 2022 - 13ème Appel à Projets de l'ERA-NET CHIST-ERA (Call 2022)

MLDR: A Machine Learning-Driven Radio Interface – MLDR

Submission summary

1.Project summary
In the following years, we will see the advent of many new applications and use-cases such as the metaverse, the adoption of XR/VR, holographic telepresence, the Internet of the Senses, the consolidation of the Internet of Things, with autonomous robots, fully automated industries and manufacturing plants, as well as smart infrastructures and environments, to mention just a few. To satisfy their strict and high requirements —in terms of throughput, latency, reliability, connectivity, and power consumption— wireless networks —and their radio interface in particular— are becoming exceedingly complex, with a plethora of advanced communication features, protocols and parameters, usually involving nonlinear dependencies between them. To deal with such complexity, the use of Artificial Intelligence and Machine Learning (AI/ML) techniques—and their ability to deal with complexity in general—is the necessary performance enabler for next-generation wireless networks.

In this project, we aim to build a new, clean-slate AI/ML-Driven Radio (MLDR) interface. This new MLDR interface will learn to communicate by selecting and configuring the set of communication protocols and functionalities that better suit every particular use-case and scenario, thus satisfying the aforementioned hard performance requirements and efficiently using the available spectrum resources. While the project proposal is groundbreaking in terms of focus and goals, we will follow a standard research approach to reach the stated objectives, i.e., we will move from use-cases, concepts/specifications and design, to implementation, evaluation and analysis. The consortium includes four partners, all working at the intersection of wireless networks and AI/ML areas, with complementary expertise. During the MLDR design and evaluation process, we will generate new knowledge in the form of new ideas, theories, practical solutions, ML algorithms, and disruptive communication functions. We expect the results from this project will guide the design of future AI/ML-driven wireless communications and networks, becoming a reference to follow and compare with.


2.Relevance to the call
The proposal meets the topic addressed in the call and is fully in-line with the following five target outcomes: (i) design of AI-enhanced techniques for resource optimisation in RANs (MLDR will perform decisions directly related to resource optimisation), (ii) implementation of ML in physical layer signal processing (a key component of MLDR), (iii) development of AI-enhanced techniques for MIMO processing & beamforming (MLDR will embrace any available hardware functionality such as multiple antennas to provide performance gains), (iv) design of improved Cognitive Radio Networks (MLDR is itself a self-learning, cognitive interface), (v) design of use-cases to take advantage of these technologies (the definition of appropriate use-cases is crucial for the justification of deploying MLDR). Additionally, we are partially in agreement with the following two outcomes: (i) development of hardware and or/software techniques to improve energy efficiency in wireless networks (energy efficiency is not a direct goal to address in this project, but resource optimisation will lead to energy efficiency), and (ii) generation and assurance of reliable training data for ML (we will openly provide our training data, but its generation is not our direct goal)

Project coordination

richard Combes (Laboratoire des Signaux et Systèmes)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.

Partner

L2S Laboratoire des Signaux et Systèmes
UOULU University of Oulu
UPF Universitat Pompeu Fabra
AGH University AGH University of Science and Technology

Help of the ANR 890,035 euros
Beginning and duration of the scientific project: January 2024 - 36 Months

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