# On the Design of a Network Digital Twin for the Radio Access Network in 5G and Beyond
###### Abstract  
A Network Digital Twin (NDT) is a high-fidelity digital mirror of a real network. Given the increasing complexity of 5G and beyond networks, the use of an NDT becomes useful as a platform for testing configurations and algorithms prior to their application in the real network, as well as for predicting the performance of such algorithms under different conditions. While an NDT can be defined for the different subsystems of the network, this paper proposes an NDT architecture focusing on the Radio Access Network (RAN), describing the components to represent and model the operation of the different RAN elements, and to perform simulations. Different application use cases are identified, and among them, the paper puts the focus on the training of Reinforcement Learning (RL) solutions for the RAN. For this use case, the paper introduces a framework aligned with O-RAN specifications and discusses the functionalities needed to integrate the NDT. This use case is illustrated with the description of a RAN NDT implementation used for training an RL-based capacity-sharing solution for network slicing. Presented results demonstrate that the implemented RAN NDT is a suitable platform to successfully train the RL solution, achieving service-level agreement satisfaction values above 85%. 5G; Network Digital Twin; Radio Access Network; reinforcement learning; training: network slicing; capacity sharing +
Footnote †: journal: Sensors  
0000-0001-5000-0001-5000-00001-5000-00001  
The development of NDTs for 5G and beyond 5G (B5G) networks is still in an early stage. Some survey-like papers have provided initial visions on NDTs for B5G [3; 4; 5; 6; 7; 8; 9], identifying the main challenges, potential technologies and use cases of NDTs. Moreover, standardization bodies have also started working on the integration of NDTs. In this regard, IETF has prepared a draft on the NDT concept and architecture in [10], 3 GPP opened a studio item on NDT for network management in [11], and ITU has identified the use of NDT for \"intent assurance\" in [12], where the decisions to be made on the network are validated on an NDT. The NDT design needs to provide a high-fidelity mirror of the network, to be achieved with an efficient execution time and an efficient use of computational resources, achieving a balance between the NDT complexity and accuracy and providing a high level of flexibility to update the NDT in step with the continuous changes in the physical network [5]. An NDT can involve different subsystems of the network, including the core network and the Radio Access Network (RAN), but also the cloud edge and the transport network that includes fronthaul and backhaul subsystems or even the possibility of having satellite connectivity, as discussed in [2]. At the same time, each subsystem might include different components (e.g., for the case of the backhaul subsystem, node components and fiber links components, etc.). About this, previous works on the use of NDT for 5G networks in [5; 6; 7; 8; 9] have only proposed a high-level design of NDTs, mainly distinguishing the main subsystems. Only the work in [3] identified some specific components to be included in an NDT of the whole network. However, the particularities of each of the subsystems of an NDT will require further effort from the research community to specify a proper design. To the authors' best knowledge, this paper is the first one to focus on the design of an NDT for representing the RAN subsystem. The applications of NDT for the RAN embrace different RAN management functions, including planning, operation and optimization [5; 7]. For planning, NDTs can be used to assess different RAN deployment topologies and configurations before the real deployment is held out, allowing their assessment under different conditions (e.g., traffic load levels). In the case of network operation, NDTs can contribute to real-time monitoring and anomaly detection by using the prediction features of the NDT models.