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

AI-enabled MUltimodal SEmantic COMmunications and COMputing – MUSE-COM^2

Submission summary

MUSE-COM^2 aims to develop and validate a novel system for AI-empowered multimodal communications considering semantics of individual modalities jointly optimized with the processing of the modalities in multi-access/mobile edge computing (MEC) servers. Unlike conventional semantic and goal-oriented communications, the inclusion of information processing in MEC imposes new challenges related to the impact of information carried in individual modalities on the MEC processing outcome. The goal is to obtain a coherent framework jointly reducing the amount of information carried over the wireless links and subsequently processed in MEC; thus, saving not only radio and computing resources, but also energy while leading to the same outcome of the MEC processing.
Since various modalities carry information about the same object, a redundancy in transmitted data for individual modalities will be identified and analyzed using AI. The AI training itself is typically a very complex and time/energy consuming process. Hence, we will also optimize the AI training to improve its efficiency and to reduce its energy consumption. Furthermore, we will design AI-based solutions managing jointly communication and computing considering the semantics of individual modalities to improve the overall system performance. The project targets a multi-disciplinary approach that combines elements of AI and communication systems. Consequently, our research approach will involve a combination of computational modeling, experimental studies, optimization, and data analysis.
The developed system will be widely tested and validated in the lab and, then, the system will be demonstrated in the real-world environment in the facility of the industrial partner of the project. We target a practical industrial use-case focused on a production line monitoring and an evaluation of the products’ quality. The efficiency of the developed system will be assessed in terms of the targeted KPIs, especially energy savings for communications, computing, and AI training.

MUSE-COM^2 will address challenges for traditional networks with respect to their design, deployment, operation and optimization to reduce power consumption related to communication and computing in line with the call. This will be facilitated via joint multimodal semantic communication and computing exploiting artificial intelligence and machine learning techniques implemented in wireless communication systems to improve network management and resource allocation.
The following topics expected in the project call are targeted in MUSE-COM^2:
- Design of AI-enhanced techniques for resource optimization in Radio Access Networks
(addressed in Tasks T3.2, T3.3, and T4.2);
- Implementation of ML in physical layer signal processing (T3.2, T3.3, and T4.2);
- Development of software techniques to improve energy efficiency in wireless networks (T4.3);
- Design of protocols for reliable AI-based Edge processing (T3.3 and T4.2);
- Generation and assurance of reliable training data for ML (T3.1 and T4.1);
- Development of open-access testbeds (T4.3 and T5.1);
- Design of use-cases to take advantage of these technologies (T2.1).
In line with the call, MUSE-COM2 will make all the data, protocol description, and software needed to reproduce experiments publically available.

Project coordination

Maurizio Filippone (EURECOM)

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

EURECOM EURECOM
BOSCH Robert Bosch, spol. s r. o.
CTU Czech Technical University in Prague
OULU University of Oulu

Help of the ANR 994,499 euros
Beginning and duration of the scientific project: December 2023 - 36 Months

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