Towards World Model-Empowered Integrated Sensing, Communication, and Decision for Complex Unmanned Systems
2026-06-29 • Information Theory
Information Theory
AI summaryⓘ
The authors introduce a new system architecture that helps different unmanned devices like satellites, drones, and robots work together more smartly by sharing information and making decisions efficiently. Their approach uses a unified "world model" to keep data fresh and reduce unnecessary sensing by predicting what is important based on task urgency. This model also helps plan communications and evaluate decisions ahead of time, improving coordination among many types of devices. Their tests show this method works better than traditional systems for managing complex unmanned networks.
unmanned aerial vehicles (UAVs)unmanned ground vehicles (UGVs)age of information (AoI)world modelwireless communicationpredictive modelingknowledge graphlatent spaceautonomous systemsmulti-agent coordination
Authors
Xue Han, Yongpeng Wu, Meng Shen, Wenjun Xu, Biqian Feng, Zijin Wang, Xiaohu You, Shengli Sun, Wenjun Zhang
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.