Digital Twins for Access Networks: History
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Contributor:
Duc-Thinh Ngo
,
Ons Aouedi
,
Kandaraj Piamrat
,
Thomas Hassan
,
Philippe Raipin-Parvédy

As the complexity and scale of modern networks continue to grow, the need for efficient, secure management, and optimization becomes increasingly vital. Digital twin (DT) technology has emerged as a promising approach to address these challenges by providing a virtual representation of the physical network, enabling analysis, diagnosis, emulation, and control. The emergence of Software-defined network (SDN) has facilitated a holistic view of the network topology, enabling the use of Graph neural network (GNN) as a data-driven technique to solve diverse problems in future networks.

  • communication network
  • digital twin
  • graph neural networks
  • deep learning

1. Introduction

Digital twins (DTs) emerged in the early 2000s when Michael Grieves introduced them during a course presentation on product lifecycle management. By 2011, implementing DTs was regarded as a challenging process that demanded advancements in various technologies. Although the term “digital twin” emerged in 2003, NASA provided the first detailed account of its application in Technology Roadmaps several years later. In this context, a twin was employed to replicate space conditions and conduct tests for flight preparation. Initially rooted in the aerospace sector, the adoption of DTs expanded to the manufacturing industry around 2012.
Other than the three main components, the digital (virtual part), the real physical product, and the connection between them, according to Tao et al. [1], a DT can be extended to five components by including data and service as a part of it. While sharing a similar purpose with simulations, a digital twin is expected to be more potent by adopting a data-driven approach to model physical objects. Furthermore, the bidirectional synchronization in the connection allows for the real-time updating of the digital replica in response to changes in the mirrored physical object and facilitates the monitoring of the associated product through a virtualization layer. Simulations, on the other hand, focus on modeling the physical entity in specific scenarios or time frames. At the simplest level, a DT is a digital counterpart of a single, atomic, physical thing/system. In a web-based context, at this stage, the DT is a mere proxy of an IoT device with eventually augmented capabilities. However, the digital twin concept can be applied to larger systems in a cascading manner [2], where it can be considered as Things in the Web Of Things vision: they are components of a larger graph of Things and can be composed in a bottom-up or top-down fashion to realize large-scale cyber-physical systems in different application domains, e.g., transportation networks [3][4][5], water distribution networks [6], smart manufacturing systems [7], etc. In this context, Graph neural networks (GNNs) can be a relevant tool that can help in building additional AI-based services on top of this graph of DTs.
The application of the digital twin paradigm to network management and operation fits into the continuation of global network digitization. It is seen as a high-value target by network operators, and thus there are parallel efforts to offer a standardized definition of what is a Network DT, i.e., DTN or NDT. One can be found in the International Telecommunication Union (ITU) recommendation document [8]: “A digital twin network (DTN) is a virtual representation of the physical network. DTN is useful for analyzing, diagnosing, emulating, and controlling the physical network based on data, model, and interface, to achieve a real-time interactive mapping between physical networks and virtual twin networks. According to the definition, DTN contains four key characteristics: data, mapping, model, and interface [...]”. ITU also proposes a reference architecture of a Network digital twin (NDT), including three layers, three domains, and a double closed-loop. The three layers consist of the physical network layer, the Network digital twin (NDT) layer, and the network application layer. Inside the NDT layer, three domains are defined corresponding to three key subsystems: unified data repository, unified data models, and DT entity management. While the unified data repository serves as a single source of data for this layer, and provides the capability to collect, store, serve, and manage data, the unified data model is the ability source of this layer, equipped with specific model instances for different network applications. Within the model domain, an inner closed-loop optimization and emulation is defined between two model types: (i) basic models, which help verify and emulate control changes and optimization solutions before the new configuration is sent to the physical network, and (ii) functional models, which are established for specific use cases and help optimize network configurations to gain better performance. Additionally, the DT entity management enforces the NDT layer with three key controllers: model management, security management, and topology management. Finally, the outer closed loop control feedback is defined based on the three-layer architecture.
Like in many other fields (e.g., IoT, Industry 4.0, and healthcare), the DT paradigm can be applied to a diverse set of use cases. 

2. Digital Twins for Access Networks

2.1. Radio Networks

In 6G THz networks, Line-Of-Sight signal establishment is required to enable high bandwidth and low latency; however, obstacles in the path may absorb THz signals, which makes Line-Of-Sight signal establishment impossible. Pengnoo et al. [9] tackle the problem of signal reflection to avoid obstacles for the signals between a Base station (BS) and a user in indoor amenities, using a digital twin to model and control the signal propagation. The DT uses a set of cameras to stream images of the indoor space. The described system includes modules to perform calculations, e.g., ray tracing, path loss prediction, reflector, and mobile endpoint alignment, allowing the DT estimation of the THz Potential Field (THzPF) and use of this estimation to redirect the signal in real time. The authors present simulation results to show the effectiveness of the system.
In a similar context for outdoor applications, Jiang and Alkhateeb [10] address the adjustment of narrow beams in large-scale MIMO systems. This traditionally requires a large amount of data acquisition/beam sweeping, which scales with the system size/number of antennas. The authors propose to (i) construct a 3D digital replica, i.e., a digital twin, of the real-world communication system, and (ii) use ray tracing algorithms to simulate the signal’s propagation. The authors demonstrate that the DT can be used to pre-train Machine learning (ML) models, thus reducing the data acquisition overhead as an important part of the data collection can be simulated. The expected mismatches between the real world and the DT “can be calibrated by a small amount of real-world data”.

2.2. Internet of Things Networks

Akbarian et al. [11] develop a DT for industrial control systems with the support of an intrusion detection algorithm allowing the detection of attacks and the diagnosis of the types of attack by classification. There exists literature regarding the implementation of DTs for intrusion detection systems but with limitations: one [12] considers only the rule-based detection algorithm, and one [13] shows limitations in the data synchronization between the physical system and its DT. Accordingly, in this work, the authors emphasize the novelty of the detection algorithm and the synchronization ability of the DT enabling continuous synchronization without a need for a specification of the system’s correct behavior.
In another study, Benedictis et al. [14] introduce a self-adaptive architecture for DTs aimed at Industrial internet of things (IIoT) anomaly detection. This architecture incorporates ML algorithms and draws inspiration from the MAPE-K [15] (Monitor-Analyze-Plan-Execute over a shared Knowledge base) feedback loop. The authors also demonstrate a Proof Of Concept (PoC) by developing a digital twin for a real-world IIoT system, specifically the European Railway Traffic Management System. The PoC showcases the reference architecture of the DT and includes quantitative evaluation to assess its performance.

2.3. Vehicular Networks

In [16], Hui et al. propose an architecture to solve (generic) Federated learning (FL) tasks in heterogeneous vehicular networks (HetVNets). In their simulation scenario, roadside units (RSUs) (i.e., cellular BSs or aerial vehicles) act as FL endpoints with FL capabilities/training resources, while vehicles hold data that can be used in training processes. In their approach, DTs of vehicles and RSUs are deployed, and the FL multi-tasks are considered as a matching game between learning task requests and available data within the RSUs range, which must be optimized in terms of training cost and model accuracy.
Similarly, Zhao et al. [17] deal with optimizing software-defined vehicular networks where the DT acts as a centralized controller of the network, enabling more computation resources than what is available at the edge. In this centralized configuration, the DT controller provides optimal per-flow routing computation adapted to the demands of vehicles. The authors implement a simulation where the vehicular network state is considered as a temporal graph, constructed as a Hidden Markov Model, and the routing scheme optimization is a temporal graph routing task. The centralized DT can run different routing scheme simulations by prioritizing different parameters, e.g., historical routing or vehicle density, and then apply the best routing scheme found to improve routing efficiency, maintenance, traffic flow, and security in the physical network.

2.4. Edge Networks

To achieve 6G ambitions such as ubiquitous connectivity, extremely low latency, and enhanced edge intelligence, Multiple-access edge computing (MEC) plays a crucial role. Lu et al. [18] propose a wireless digital twin edge network model that aims to provide hyper-connected user experience and low-latency edge computing. They tackle the DT to edge association problem concerning the dynamic network states and varying network topology, where DTs can be either twins of user devices or twins of services that users are using. The authors propose to use Deep reinforcement learning (DRL) for digital twin placement and transfer learning for digital twin migration to follow dynamically placed users, thus trying to minimize the latency and energy costs of users in the network.
In a different context, Van Huynh et al. [19] focus on the integration of MEC offloading with Ultra-reliable low-latency communication (URLLC) and short packet transmission, specifically in the IIoT context. The authors aim to minimize the latency of task offloading by optimizing the user association, transmission power, and processing rate of User equipments (UEs) and edge servers (ESs). To achieve this, a DT of the edge network architecture is constructed, while the wireless communications between UEs and ESs are established via URLLC links, realizing a DT-empowered URLLC edge network. In this work, the DT replicates the physical system (hardware information, operating applications, real-time states), optimizes resources, and makes decisions to control the whole system in real time. To achieve this, the DT optimizes a variety of variables, e.g., user association, offloading policies, transmission power, processing rates, energy consumption budget, and computation resources budget of UEs and ESs. Similarly, Do-Duy et al. [20] introduce the use of DTs for intelligently offloading the computing tasks of UEs onto MEC servers. They formulate the problem as an optimization problem with the main objective of minimizing the total digital twin latency by choosing the optimal user association, transmit power, offloading policies, and processing rate of UE and MEC servers.
Furthermore, Duong et al. [21] presented a solution to address the challenge of minimizing latency in the context of intelligent offloading of IoT devices, assisted by digital twins, onto edge networks deployed with unmanned aerial vehicles (UAVs). The solution takes into consideration the constraints imposed by URLLC links. The problem is formulated as an optimization task that involves jointly optimizing the transmit power, offloading policies, and processing rate of IoT devices and ESs.

3. Digital Twins for Core Networks

As for the core networks, DT also plays an important role in their evolution to the Next-generation network. As an example, Wang et al. [22] propose a DT framework to enhance optical communication networks. The authors describe a generic framework composed of the physical layer, the data layer, the model layer, and the application layer. The approach includes the propositions of three separate Deep learning (DL) models to achieve different objectives. First, a fault management model that uses bidirectional Gated recurrent unit (GRU) [23] to predict faulty equipment and XGBoost [24] to make a fault diagnosis. Second, a flexible hardware configuration model to dynamically configure a programmable optical transceiver (POT) using DRL. Finally, a dynamic transmission simulation system based on bidirectional Long short-term memory (LSTM) [25] that could replace a traditional block-based optical transmission simulation system.
Another approach proposed by Seilov et al. [26] involves building networks of digital twins to tackle problems in complex telecommunication networks with two specific examples in feedback loops and traffic monitoring. In the former case, the network of digital twins allows the developer the tracking of all changes made and intervention in the development of the telecommunication system. In the latter case, the network of digital twins enables the network operators to solve urgent problems in emergencies where the network traffic suddenly changes due to the dysfunction of some network elements.
In another context, Yigit et al. [27] present a digital twin-enabled framework aimed at Distributed denial of service (DDoS) attack detection for autonomous core networks. Since existing DDoS solutions are insufficient for data centers and edge networks in terms of scalability, detection rates, and latency, the authors develop an online ML-based algorithm for effective DDoS detection. This algorithm processes the data that can be captured in real time with the support of the digital twin designed for the core network. Moreover, the Yet another next generation (YANG) model and automated feature selection are also involved in reducing the complexity of the provided data before feeding it to the ML algorithm. Finally, the authors evaluate and show the outperformance of their proposed system using two different DDoS attack datasets as the simulation of the core network.

Lessons Learned

DTs have emerged as valuable tools in Next-generation networks, offering advanced capabilities in network optimization, monitoring, and security across various domains, including core networks, access networks, edge networks, and vehicular networks. In the context of optimization, digital twins function as centralized controllers that collect data from the physical network and propose optimal policies. For monitoring, they operate as a closed-loop autonomous system, ensuring effective network surveillance. Additionally, this autonomous system coupling with intrusion detection further emphasizes their importance in ensuring network security. Overall, digital twins play a pivotal role in enhancing network performance and safeguarding network integrity.

This entry is adapted from the peer-reviewed paper 10.3390/fi15120377

References

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