Building Network Digital Twins Part II: Real-Time Adaptive PID for Enhanced State Synchronization

TL;DR

This paper tackles real-time state synchronization between the Physical Twin (PT) and Digital Twin (DT) in Network Digital Twins (NDTs) for dynamic mobile networks. It combines a CNN-based traffic predictor with a real-time adaptive PID control loop to minimize the synchronization error $E(t)$ between PT and DT by adjusting the control signal $u(t)=K_p e(t)+K_i \int e(t)\,dt + K_d \frac{d e(t)}{dt}$. Experiments in a ComNetsEmu-based emulation with video and iPerf traffic show that the adaptive PID reduces prediction error and improves traffic synchronization, supported by a real-time visualization UI for parameter tuning. The results advance practical NDT deployment for near real-time network management and testing, and set the stage for future Part III, which will explore DT-driven orchestration to control the Physical Twin in next-generation 6G networks.

Abstract

As we evolve towards more heterogeneous and cutting-edge mobile networks, Network Digital Twins (NDTs) are proving to be a promising paradigm in solving challenges faced by network operators, as they give a possibility of replicating the physical network operations and testing scenarios separately without interfering with the live network. However, with mobile networks becoming increasingly dynamic and heterogeneous due to massive device connectivity, replicating traffic and having NDTs synchronized in real-time with the physical network remains a challenge, thus necessitating the need to develop real-time adaptive mechanisms to bridge this gap. In this part II of our work, we implement a novel framework that integrates an adaptive Proportional-Integral-Derivative (PID) controller to dynamically improve synchronization. Additionally, through an interactive user interface, results of our enhanced approach demonstrate an improvement in real-time traffic synchronization.

Figures (7)

• Figure 1: Synchronization illustration of a Digital Twin with a Physical Twin using a real-tme adaptive PID
• Figure 2: Network topology
• Figure 3: Video streaming traffic
• Figure 4: iPerf traffic flow 1
• Figure 5: Test traffic visualization depicting improved performance with PID adjusted predicted traffic closely synchronizing with actual traffic