Towards World Model-Empowered Integrated Sensing, Communication, and Decision for Complex Unmanned Systems

2026-06-29

AI Summary

This work addresses the challenge of fragmented perception, communication, and decision-making in existing autonomous systems, which hinders the coordinated optimization of real-time task execution among large-scale heterogeneous agents. To overcome this limitation, the paper proposes a world-model-driven integrated closed-loop framework that jointly optimizes time-sensitive perception, wireless communication, and intelligent decision-making through a unified world model. Key innovations include an Age-of-Information (AoI)-driven timeliness-aware perception mechanism, a predictive world model operating in a hybrid deterministic-stochastic latent space, and a multi-granularity knowledge graph tailored for multi-agent collaboration. Experimental results demonstrate that the proposed framework significantly outperforms state-of-the-art methods in terms of perception freshness, communication efficiency, and decision-making performance.

Abstract

Complex unmanned systems comprising satellites, unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and quadruped robots are increasingly deployed to perform large-scale sensing and autonomous operations. We propose a world model-empowered sensing, communication, decision (SCD) integration framework for complex unmanned communication networks. The proposed architecture establishes a closed-loop system where a unified world model jointly optimizes time-sensitive sensing, wireless communication, and intelligent decision-making. To regulate sensing freshness and reduce redundant data generation, we propose a time-sensitive age of information (AoI)-driven sensing mechanism that dynamically schedules sensing updates based on task urgency and predictive uncertainty. Furthermore, a predictive world model is developed to jointly represent environmental dynamics, wireless channel evolution, and agent mobility within a hybrid deterministic-stochastic latent space. This enables proactive communication scheduling and decision evaluation via latent rollout. To support large-scale heterogeneous coordination, a multi-granularity knowledge graph is further designed to organize cross-population relationships among satellites, UAVs, UGVs, and ground agents. Numerical results demonstrate that the proposed SCD framework outperforms conventional systems, highlighting the significant potential of world models for supporting unmanned systems.

Problem

• integrated sensing, communication, and decision
• complex unmanned systems
• world model
• age of information
• heterogeneous coordination

Innovation

• world model
• integrated sensing communication and decision (SCD)
• age of information (AoI)
• latent space modeling
• multi-granularity knowledge graph