Towards a Foundation Model for Communication Systems

• URL: http://arxiv.org/abs/2505.14603v1
• Date: Tue, 20 May 2025 16:52:11 GMT
• Title: Towards a Foundation Model for Communication Systems
• Authors: Davide Buffelli, Sowmen Das, Yu-Wei Lin, Sattar Vakili, Chien-Yi Wang, Masoud Attarifar, Pritthijit Nath, Da-shan Shiu,
• Abstract summary: In this work, we take a step toward a foundation model for communication data.<n>We propose methodologies to address key challenges, including tokenization, positional embedding, multimodality, variable feature sizes, and normalization.<n>We empirically demonstrate that such a model can successfully estimate multiple features, including transmission rank, selected precoder, Doppler spread, and delay profile.
• Score: 16.85529517183343
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial Intelligence (AI) has demonstrated unprecedented performance across various domains, and its application to communication systems is an active area of research. While current methods focus on task-specific solutions, the broader trend in AI is shifting toward large general models capable of supporting multiple applications. In this work, we take a step toward a foundation model for communication data--a transformer-based, multi-modal model designed to operate directly on communication data. We propose methodologies to address key challenges, including tokenization, positional embedding, multimodality, variable feature sizes, and normalization. Furthermore, we empirically demonstrate that such a model can successfully estimate multiple features, including transmission rank, selected precoder, Doppler spread, and delay profile.

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