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

2026-01-06

IEEE Communications Surveys and Tutorials

AI Summary

This study addresses the complex and dynamic management demands of wireless networks by systematically exploring the application of multimodal foundation models in prediction and control tasks. It proposes a unified framework for deploying such models in wireless environments, integrating multimodal contextual understanding, context-aware modeling, transfer learning, and domain adaptation. The work establishes a systematic taxonomy and technical roadmap, introduces a dedicated dataset, and articulates key challenges and future directions for developing wireless-specific foundation models. Collectively, these contributions provide both theoretical grounding and practical guidance toward realizing intelligent, general-purpose network management.

Technology Category

• Machine Learning: Multimodal Learning
• Multiagent Systems: Other Foundations of Multi Agent Systems
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Models for Web evolution

Abstract

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.

Problem

• Foundation Models
• Multi-Modal Data
• Wireless Networks
• Prediction Tasks
• Control Tasks

Innovation

• Foundation Models
• Multi-Modal Data
• Wireless Networks
• Prediction and Control
• AI Agents