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TwiNet: Connecting Real World Networks to their Digital Twins Through a Live Bidirectional Link

Clifton Paul Robinson, Andrea Lacava, Pedram Johari, Francesca Cuomo, Tommaso Melodia

TL;DR

TwiNet enables swift deployment and adaptation of Digital Twin replicas, addressing crucial challenges in modern wireless communication systems, and can achieve data transfer times with an average latency of 14 ms, suitable for real-time communication.

Abstract

The wireless spectrum's increasing complexity poses challenges and opportunities, highlighting the necessity for real-time solutions and robust data processing capabilities. Digital Twin (DT), virtual replicas of physical systems, integrate real-time data to mirror their real-world counterparts, enabling precise monitoring and optimization. Incorporating DTs into wireless communication enhances predictive maintenance, resource allocation, and troubleshooting, thus bolstering network reliability. Our paper introduces TwiNet, enabling bidirectional, near-realtime links between real-world wireless spectrum scenarios and DT replicas. Utilizing the protocol, MQTT, we can achieve data transfer times with an average latency of 14 ms, suitable for real-time communication. This is confirmed by monitoring real-world traffic and mirroring it in real-time within the DT's wireless environment. We evaluate TwiNet's performance in two use cases: (i) assessing risky traffic configurations of UEs in a Safe Adaptive Data Rate (SADR) system, improving network performance by approximately 15% compared to original network selections; and (ii) deploying new CNNs in response to jammed pilots, achieving up to 97% accuracy training on artificial data and deploying a new model in as low as 2 minutes to counter persistent adversaries. TwiNet enables swift deployment and adaptation of DTs, addressing crucial challenges in modern wireless communication systems.

TwiNet: Connecting Real World Networks to their Digital Twins Through a Live Bidirectional Link

TL;DR

TwiNet enables swift deployment and adaptation of Digital Twin replicas, addressing crucial challenges in modern wireless communication systems, and can achieve data transfer times with an average latency of 14 ms, suitable for real-time communication.

Abstract

The wireless spectrum's increasing complexity poses challenges and opportunities, highlighting the necessity for real-time solutions and robust data processing capabilities. Digital Twin (DT), virtual replicas of physical systems, integrate real-time data to mirror their real-world counterparts, enabling precise monitoring and optimization. Incorporating DTs into wireless communication enhances predictive maintenance, resource allocation, and troubleshooting, thus bolstering network reliability. Our paper introduces TwiNet, enabling bidirectional, near-realtime links between real-world wireless spectrum scenarios and DT replicas. Utilizing the protocol, MQTT, we can achieve data transfer times with an average latency of 14 ms, suitable for real-time communication. This is confirmed by monitoring real-world traffic and mirroring it in real-time within the DT's wireless environment. We evaluate TwiNet's performance in two use cases: (i) assessing risky traffic configurations of UEs in a Safe Adaptive Data Rate (SADR) system, improving network performance by approximately 15% compared to original network selections; and (ii) deploying new CNNs in response to jammed pilots, achieving up to 97% accuracy training on artificial data and deploying a new model in as low as 2 minutes to counter persistent adversaries. TwiNet enables swift deployment and adaptation of DTs, addressing crucial challenges in modern wireless communication systems.

Paper Structure

This paper contains 10 sections, 1 equation, 7 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Our Testbed Setup
  4. Experimental Setups
  5. for Traffic Management
  6. Model Creation for Pilot Jamming
  7. Experimental Results
  8. for Traffic Management
  9. Model Creation for Pilot Jamming
  10. Conclusions

Figures (7)

  • Figure 1: High-level representation of a digital twin for a smart city.
  • Figure 2: High-level overview of TwiNet, showing the link between the real world and along with the spectrum and deep learning capabilities.
  • Figure 3: Results of the real-time traffic monitoring over a 60-second experiment (LEFT); Zoomed-in graphs to show the time delay on the (RIGHT)
  • Figure 4: The system's data exchange with the real world and the is visually represented via continuous data flow facilitated by TwiNet.
  • Figure 5: Overview of the pipeline where the base station requests a new jamming detection model based on the chosen pilot carriers.
  • ...and 2 more figures