A Predictive and Preventive Digital Twin Framework for Indoor Wireless Networks

TL;DR

This work addresses the challenge of performance degradation in dense indoor Wi-Fi networks operated under CSMA/CA. It introduces a predictive and preventive Digital Twin framework that uses spatio-temporal DT state representations, generates plausible future scenarios via importance sampling, and evaluates two analytical bounds—Shannon capacity and CSMA/CA latency—to screen risk and guide proactive reconfiguration. A gradient-based risk mitigator then seeks feasible improvements (e.g., AP associations, channel/backhaul choices, and QoS settings) to maintain service margins rather than pursuing globally optimal but fragile solutions. Simulations in realistic household mesh scenarios demonstrate the DT’s ability to foresee time-dependent congestion and mitigate it before degradation, highlighting practical potential for scalable, model-driven Wi-Fi management and suggesting applicability to other distributed wireless systems. The framework combines principled feasibility reasoning with forward-looking scenario generation to enable proactive, robust, and explainable network control in dynamic environments.

Abstract

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.

Figures (21)

• Figure 1: Block diagram of the proposed network optimization system
• Figure 2: An example scenario to test spatial structure. (a)House layout, and (b)AP locations and its final spatial clustering.
• Figure 3: Heat maps of 4 APs at 2.4G frequency band.
• Figure 4: (a) and (b) are respectively DB index and cluster volume ratio of the example scenario with uniform user distribution, (c) and (d) are respectively DB index and cluster volume ratio with non-uniform user distribution,
• Figure 5: The probability of turning active for 3 roles (left column) and their respective long-term daily average (right column).