Foundation models (FMs) are recognized as a transformative breakthrough that has started to reshape the future of artificial intelligence (AI) across both academia and industry. The integration of FMs into wireless networks is expected to enable the development of general-purpose AI agents capable of handling diverse network management requests and highly complex wireless-related tasks involving multi-modal data. Inspired by these ideas, this work discusses the utilization of FMs, especially multi-modal FMs in wireless networks. We focus on two important types of tasks in wireless network management: prediction tasks and control tasks. In particular, we first discuss FMs-enabled multi-modal contextual information understanding in wireless networks. Then, we explain how FMs can be applied to prediction and control tasks, respectively. Following this, we introduce the development of wireless-specific FMs from two perspectives: available datasets for development and the methodologies used. Finally, we conclude with a discussion of the challenges and future directions for FM-enhanced wireless networks.

翻译：基础模型（FMs）被公认为一项变革性突破，已开始重塑学术界与工业界人工智能（AI）的未来。将基础模型融入无线网络，有望推动通用人工智能代理的发展，使其能够处理多样化的网络管理请求以及涉及多模态数据的高度复杂无线相关任务。受这些理念启发，本文探讨了基础模型——特别是多模态基础模型——在无线网络中的应用。我们聚焦于无线网络管理中两类重要任务：预测任务与控制任务。具体而言，我们首先讨论了基础模型赋能的无线网络多模态上下文信息理解。接着，我们分别阐述了基础模型如何应用于预测任务与控制任务。随后，我们从两个视角介绍了无线专用基础模型的发展：可用于开发的数据集及所采用的方法论。最后，我们总结了基础模型增强无线网络所面临的挑战与未来研究方向。