Towards a Foundation Model for Communication Systems
This work addresses the need for more versatile AI solutions in communication systems, moving beyond task-specific approaches, though it appears incremental as it builds on existing transformer and foundation model trends.
The paper tackles the problem of developing a general AI model for communication systems by proposing a transformer-based, multi-modal foundation model that operates directly on communication data, and it demonstrates the model's ability to estimate multiple features such as transmission rank and Doppler spread.
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.