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Integrating large artificial intelligence models into 6G mobile networks

Reference number
Coordinator Kungliga Tekniska Högskolan - Avdelningen för teknisk informationsvetenskap
Funding from Vinnova SEK 7 360 000
Project duration October 2024 - August 2027
Status Ongoing
Venture 6G - Research and innovation
Call 6G - National collaboration projects in research and innovation

Purpose and goal

In the project, we will collaborate to research and develop the technologies and related software to integrate large artificial intelligence models (LAIM, such as ChatGPT) into 6G networks. We will apply LAIM to optimize wireless networks to improve performance and resource efficiency in e.g. energy use. We will investigate reliable and trustworthy distributed AI in 6G. The results include AI algorithms, knowledge, related software and trained researchers.

Expected effects and result

The expected results of the project include: (1), The AI/communication algorithms for the targeted use cases. (2), Simulation software. The algorithms will be verified by simulations. The software also includes scripts for LAIM such as ChatGPT prompts. The software will be programmed by common tools such as Python, Matlab. (3), Scientific publications. Publications include top journal articles in IEEE and also proceedings in conferences. (4), Knowhow and patents. (5), Trained researchers.

Planned approach and implementation

To achieve the project objectives, we will divide the project into three WPs for implementation. (1), WP1, LAIM based network optimization. (2) WP2, Reliable and Trustworthy LAIM in 6G mobile networks. (3) WP3, Semantic and Task-orient communications based on LAIM in 6G mobile. For approach, first new AI algorithms will be developed or existed algorithms will be applied for identified use cases. The algorithms will be analyzed, and the extensive simulation software will be developed

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 18 October 2024

Reference number 2024-02435