A Predictive and Preventive Digital Twin Framework for Indoor Wireless Networks

TL;DR

This paper introduces a Digital Twin framework for indoor Wi-Fi networks that predicts future network states and guides proactive management to prevent performance issues, reducing the need for extensive simulations.

Contribution

It presents a novel Digital Twin approach that models wireless channels and traffic, providing analytical performance bounds and a proactive optimization method for Wi-Fi network management.

Findings

Successfully predicts network congestion before it occurs

Effectively guides network configuration to prevent performance degradation

Reduces reliance on time-consuming network simulations

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.