A Unified Cloud-Edge-Terminal Framework for Multimodal Integrated Sensing and Communication
Yubo Peng, Luping Xiang, Kun Yang, Feibo Jiang, Kezhi Wang, and Christos Masouros

TL;DR
This paper proposes a comprehensive cloud-edge-terminal framework for multimodal sensing and communication in 6G, integrating heterogeneous sensors and AI technologies to enhance robustness, semantic awareness, and adaptive operation modes.
Contribution
It introduces a novel hierarchical CET framework with three adaptive modes, addressing challenges in multimodal ISAC and enabling scalable, task-oriented perception.
Findings
GFM achieves highest sensing accuracy
PIM minimizes communication latency
CRM balances performance and efficiency
Abstract
The transition to 6G calls for tightly integrated sensing and communication to support mission-critical services such as autonomous driving, embodied AI, and high-precision telemedicine. However, most existing ISAC designs rely on a single sensing modality (often RF), which limits environmental understanding and becomes a bottleneck in complex and dynamic scenes. This motivates a shift from single-modal to multimodal ISAC, where heterogeneous sensors (e.g., radar, LiDAR, and cameras) complement each other to improve robustness and semantic awareness. In this article, we first summarize key challenges for multimodal ISAC, including heterogeneous fusion, communication overhead, and scalable system design. We then highlight three enabling technologies: large AI models, semantic communications, and multi-agent systems, and discuss how their combination can enable task-oriented multimodal…
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Taxonomy
TopicsIndoor and Outdoor Localization Technologies · Distributed Sensor Networks and Detection Algorithms · Advanced Wireless Communication Technologies
