mlsanityData to 14 Oct 2026

Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey

arXiv 2601.03181 ↗First posted 6 Jan 2026cs.NI cs.AI cs.CLcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 215 of its 307 references that are in the map. First posted on arXiv 6 Jan 2026.

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215 of 307 references.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026MultiPathFormer: Towards a Foundation Model for Multipath Wireless Propagationcited by 0
  2. 2026A Comprehensive Survey of Wireless Foundation Models for AI-Native 6G Networkscited by 0
  3. 2026Wireless Foundation Models: State-of-the-Art and Open Challengescited by 0