DTRAN: A Special Use Case of RAN Optimization using Digital Twin

TL;DR

This paper introduces DTRAN, a comprehensive digital twin framework for RAN optimization in beyond 5G and 6G networks, enabling real-time data management and more accurate network modeling for autonomous network management.

Contribution

The paper presents a holistic digital twin framework for RAN optimization that improves accuracy and adaptability over existing solutions, covering core to edge networks.

Findings

Enables real-time data exchange with physical network

Provides detailed and accurate digital network replicas

Demonstrates applicability in RAN configuration optimization

Abstract

The emergence of beyond 5G (B5G) and 6G networks underscores the critical role of advanced computer-aided tools, such as network digital twins (DTs), in fostering autonomous networks and ubiquitous intelligence. Existing solutions in the DT domain primarily aim to model and automate specific tasks within the network lifecycle, which lack flexibility and adaptability for fully autonomous design and management. Unlike the existing DT approaches, we propose RAN optimization using the Digital Twin (DTRAN) framework that follows a holistic approach from core to edge networks. The proposed DTRAN framework enables real-time data management and communication with the physical network, which provides a more accurate and detailed digital replica than the existing approaches. We outline the main building blocks of the DTRAN and describe the details of our specific use case, which is RAN configuration optimization, to demonstrate the applicability of the proposed framework for a real-world scenario.