Aug 2026· 2026 2nd International Conference on Electronic Information, Computer and Aerospace Remote Sensing (EICARS)· pp. 410-414· 0 citations· 17 references
Abstract
As a key enabling technology for 6G networks, Integrated sensing and communication (ISAC) is recognized as an AI-native core enabler for sixth-generation (6G) wireless networks. Conventional ISAC frameworks, however, rely on isolated channel modeling and static resource allocation. Moreover, they lack environment prediction and reasoning capabilities, severely limiting their effectiveness in complex time-varying wireless environments. World action models, characterized by multimodal unified representation, physical spatial-temporal forecasting, and general-purpose reasoning, present a new paradigm for intelligent ISAC systems. This paper provides a systematic overview of World action model-enabled ISAC. The survey first examines the fundamental limitations of traditional ISAC and the enabling value of World action models. A three-layer ISAC architecture, namely, comprising sensing input, model cognition, and collaborative execution is then proposed, along with two dominant implementation paradigms. Four core technical pillars are further identified, which consist of wireless World action model training, physical-layer joint optimization, network resource cooperative scheduling, and edge-lightweight inference. Six critical research challenges are identified: data scarcity, physical consistency, distributed synchronization, real-time inference, lack of rigorous theoretical performance bounds, and security/privacy concerns. Finally, future research directions are outlined from the perspectives of theory, model architecture, engineering deployment, and multi-technology convergence.
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A comprehensive overview of this emerging paradigm of proactive channel cognition and reconfiguration, wherein wireless channels are no longer regarded as uncontrollable propagation media but as network resources that can be learned, predicted, and actively reconfigured.
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This survey unifies the literature within a six-stage closed-loop framework comprising observation, contextualization, reasoning and prediction, planning and orchestration, execution and collaboration, and feedback and resilience and uses this framework to review advances in multimodal intelligence, large language mode...
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Sixth generation (6G) wireless networks aim to move beyond connected devices toward a “connected cognition” model in which the network understands intent, reasons about constraints, and executes actions autonomously. This article proposes an Agentic-Native 6G architecture embedding intelligence directly into network fu...
Experimental results show that EMWM outperforms conventional neural network and large language model baselines in both CSI prediction and environment reconstruction, achieving a squared generalized cosine similarity (SGCS) of 0.9699 for CSI prediction while demonstrating robustness across different signal-to-noise rati...
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