构建通信数据的通用大模型，直接处理原始信号。
Towards a Foundation Model for Communication Systems
- 基于Transformer设计多模态通信数据模型，支持原始信号输入。
- 可同时估计传输秩、预编码器、多普勒扩展和时延分布等4个关键参数。
- 适合通信系统设计与智能信号处理研究者参考。
人工智能在多个领域展现出前所未有的性能，其在通信系统中的应用是当前研究热点。尽管现有方法聚焦于特定任务，但人工智能整体趋势正向能支持多种应用的大规模通用模型演进。本文迈向通信数据的通用模型：一种基于Transformer的多模态模型，可直接处理通信数据。我们提出方法以应对关键挑战，包括分词、位置嵌入、多模态融合、可变特征尺寸及归一化。实验证明，该模型能有效估计多个通信特征，包括传输秩、所选预编码器、多普勒扩展和时延分布。
原文摘要 · Abstract (English)
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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