B5GEMINI: AI-Driven Network Digital Twin

Abstract

Network Digital Twin (NDT) is a new technology that builds on the concept of Digital Twins (DT) to create a virtual representation of the physical objects of a telecommunications network. NDT bridges physical and virtual spaces to enable coordination and synchronization of physical parts while eliminating the need to directly interact with them. There is broad consensus that Artificial Intelligence (AI) and Machine Learning (ML) are among the key enablers to this technology. In this work, we present B5GEMINI, which is an NDT for 5G and beyond networks that makes an extensive use of AI and ML. First, we present the infrastructural and architectural components that support B5GEMINI. Next, we explore four paradigmatic applications where AI/ML can leverage B5GEMINI for building new AI-powered applications. In addition, we identify the main components of the AI ecosystem of B5GEMINI, outlining emerging research trends and identifying the open challenges that must be solved along the way. Finally, we present two relevant use cases in the application of NDTs with an extensive use of ML. The first use case lays in the cybersecurity domain and proposes the use of B5GEMINI to facilitate the design of ML-based attack detectors and the second addresses the design of energy efficient ML components and introduces the modular development of NDTs adopting the Digital Map concept as a novelty.

Using Constrained-Disorder Principle-Based Systems to Improve the Performance of Digital Twins in Biological Systems

“…Considering uncertainties during data processing provides better verification and validation of the output and improves the system's reliability [38,41,85]. Several Bayesian inference methods are explored: i.…”

“…This enables the estimation of all uncertainties associated with the predicted output and yields a higher value in cases of insufficient data [68]. These methods are used in medical digital twins to improve their prediction capabilities, guide the timing of interventions, and enable early diagnosis [38,52,68,71,[88][89][90].…”

“…The virtual twin can adapt to changes in its physical counterpart, just as the physical object responds to interventions in the virtual twin [ 36 , 37 , 38 ]. Digital twins follow the coevolution of digital objects and physical entities by continuously collecting relevant data and improving themselves [ 31 , 39 ].…”

“…The use of digital twins in healthcare is enhanced by improved computer capacity and the development of wearable and smart devices, which provide abundant data that require correct interpretation [ 37 , 38 ]. Nevertheless, implementing digital twins in medicine presents challenges due to the complexity and variability of biological processes, which are translated into noisy dynamic data [ 50 ].…”

Transformers for Multi-Horizon Forecasting in an Industry 4.0 Use Case

“…The use case components (an AGV, the 5G network infrastructure with the MEC module, a PLC virtualized on the MEC infrastructure, and the component that generated the network disturbances) were deployed in 5TONIC, an open laboratory founded by Telefónica focusing in 5G technologies that provides realistic deployments but in a controlled scenario. 5TONIC forms part of a proto Network Digital Twin [ 23 ] build on top of the Telefonica Mouseworld Lab [ 24 ] and has permanent and temporal infrastructures to set up specific experiments, such as the one we consider in this work.…”