Telecom Foundation Models: Applications, Challenges, and Future Trends
Tahar Zanouda, Meysam Masoudi, Fitsum Gaim Gebre, Mischa Dohler

TL;DR
This paper explores the potential of Foundation Models in telecom networks, highlighting their advantages over traditional AI, and discusses development, applications, challenges, and future trends for Telecom Foundation Models in the context of 6G.
Contribution
It introduces the concept of Telecom Foundation Models, outlining their development process, potential applications, and the challenges faced in deploying them in telecom networks.
Findings
FMs can generalize across diverse telecom tasks.
Telecom FMs require multi-modal data for training.
Challenges include data privacy, model complexity, and deployment issues.
Abstract
Telecom networks are becoming increasingly complex, with diversified deployment scenarios, multi-standards, and multi-vendor support. The intricate nature of the telecom network ecosystem presents challenges to effectively manage, operate, and optimize networks. To address these hurdles, Artificial Intelligence (AI) has been widely adopted to solve different tasks in telecom networks. However, these conventional AI models are often designed for specific tasks, rely on extensive and costly-to-collect labeled data that require specialized telecom expertise for development and maintenance. The AI models usually fail to generalize and support diverse deployment scenarios and applications. In contrast, Foundation Models (FMs) show effective generalization capabilities in various domains in language, vision, and decision-making tasks. FMs can be trained on multiple data modalities generated…
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Taxonomy
TopicsWireless Networks and Protocols · Satellite Communication Systems · Advanced MIMO Systems Optimization
