Digital Twin of the Radio Environment: A Novel Approach for Anomaly Detection in Wireless Networks

TL;DR

This paper introduces a novel digital twin-based method for anomaly detection in wireless networks, leveraging 6G technologies like joint communications and sensing to compare expected and actual signal strengths for identifying issues such as jammers.

Contribution

It proposes a new digital twin framework that integrates contextual network information for improved anomaly detection in wireless environments.

Findings

Feasibility demonstrated through simulations.

Effective comparison of algorithms for jammer detection.

Supports comprehensive wireless network monitoring.

Abstract

The increasing relevance of resilience in wireless connectivity for Industry 4.0 stems from the growing complexity and interconnectivity of industrial systems, where a single point of failure can disrupt the entire network, leading to significant downtime and productivity losses. It is thus essential to constantly monitor the network and identify any anomaly such as a jammer. Hereby, technologies envisioned to be integrated in 6G, in particular joint communications and sensing (JCAS) and accurate indoor positioning of transmitters, open up the possibility to build a digital twin (DT) of the radio environment. This paper proposes a new approach for anomaly detection in wireless networks enabled by such a DT which allows to integrate contextual information on the network in the anomaly detection procedure. The basic approach is thereby to compare expected received signal strengths (RSSs) from the DT with measurements done by distributed sensing units (SUs). Employing simulations, different algorithms are compared regarding their ability to infer from the comparison on the presence or absence of an anomaly, particular a jammer. Overall, the feasibility of anomaly detection using the proposed approach is demonstrated which integrates in the ongoing research on employing DTs for comprehensive monitoring of wireless networks.