Jiunn-Tsair Chen | Searcharxiv

arXiv2026

AutoLab: An Internet-Accessible Experimental Platform for Operational World Models in Wireless Networks

Operational World Models (OWMs) require structured interaction with the physical world: they must observe operational state, impose controlled actions, measure consequences, preserve experience, and use that experience to support prediction and preventive decision making. This paper presents Autolab, an Internet-accessible experimental platform that provides these physical grounding functions for wireless-network OWMs. A remote researcher can inspect a live test site, reconstruct recent state history, author a time-ordered experiment containing robot, access-point, wireless-scan, and traffic operations, validate it locally, and submit the same explicit contract for registration. At the physical site, a centralized network management system (NMS) revalidates and schedules the experiment, coordinates distributed device agents, estimates robot location from raw camera observations, supervises physical execution, and records measurements and diagnostic state. The same Python validation rules are used at the authoring and registration boundaries, while destination-level commands isolate Internet users from low-level device control. An end-to-end mobility trace demonstrates how the NMS detects a large physical movement error, issues corrective motion, and restores the intended experiment trajectory without exposing that error to the script writer or robot agent. Autolab therefore contributes a reproducible Internet-to-physical experimentation and feedback infrastructure, rather than claiming a completed autonomous OWM. Its structured spatial--temporal records provide the experience needed for continuing work on generative scenarios, predictive risk evaluation, and preventive wireless-network management.

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arXiv2026

A Predictive and Preventive Digital Twin Framework for Indoor Wireless Networks

Wi-Fi networks increasingly suffer from performance degradation caused by contention-based channel access, dense deployments, and largely self-managed operation among mutually interfering access points (APs). In this paper, we propose a Digital Twin (DT) framework that captures the essential spatial and temporal characteristics of wireless channels and traffic patterns, enabling the prediction of likely future network scenarios while respecting physical constraints. Leveraging this predictive capability, we introduce two analytically derived performance upper bounds-one based on Shannon capacity and the other on latency behavior under CSMA-CA (Carrier Sense Multiple Access with Collision Avoidance)-that can be evaluated efficiently without time-consuming network simulations. By applying importance sampling to DT-generated scenarios, potentially risky network conditions can be identified within large stochastic scenario spaces. These same performance bounds are then used to proactively guide a gradient-based search for improved network configurations, with the objective of avoiding imminent performance degradation rather than pursuing globally optimal but fragile solutions. Simulation results demonstrate that the proposed approach can successfully predict time-dependent network congestion and mitigate it in advance, highlighting its potential for predictive and preventive Wi-Fi network management.

cs.NI↗