TwinRAN: Twinning the 5G RAN in Azure Cloud

2024-07-18

arXiv.org

AI Summary

To address operational challenges arising from the scale and complexity of 5G networks, this paper proposes TwinRAN, a cloud-native digital twin system for RAN built on Microsoft Azure Digital Twins (ADT) and supporting multi-vendor O-RAN equipment. TwinRAN introduces a novel dual-graph architecture: a global inter-cell twin graph coupled with per-cell independently instantiated twin graphs—enabling end-to-end 5G RAN modeling and real-time closed-loop validation on ADT for the first time. Leveraging RESTful microservices, temporal synchronization mechanisms, and lightweight device shadow modeling, the system achieves end-to-end synchronization latency under 300 ms in a testbed with 800 users and eight gNBs. It supports three network management applications while maintaining bounded resource consumption. Experimental results demonstrate the feasibility and scalability of industrial-grade 5G RAN digital twins.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

The proliferation of 5G technology necessitates advanced network management strategies to ensure optimal performance and reliability. Digital Twins (DTs) have emerged as a promising paradigm for modeling and simulating complex systems like the 5G Radio Access Network (RAN). In this paper, we present TwinRAN, a DT of the 5G RAN built leveraging the Azure DT platform. TwinRAN is built on top of the Open RAN (O-RAN) architecture and is agnostic to the vendor of the underlying equipment. We demonstrate three applications using TwinRAN and evaluate the required resources and their performance for a network with 800 users and 8 gNBs. We first evaluate the performance and limitations of the Azure DT platform, measuring the latency under different conditions. The results from this evaluation allow us to optimize TwinRAN to the DT platform it uses. Then, we present the system's architectural design, emphasizing its components and interactions. We propose that two types of twin graphs be simultaneously maintained on the cloud. The first one is for intercell operations, keeping a broad overview of all the cells in the network. The second twin graph is where each cell is spawned in a separate Azure DT instance for more granular operation and monitoring of intracell tasks. We evaluate the performance and operating costs of TwinRAN for each of the three applications. The TwinRAN DT in the cloud can keep track of its physical twin within a few hundred milliseconds, extending its utility to many 5G network management tasks - some of which are shown in this paper. The novel framework for building and maintaining a DT of the 5G RAN presented in this paper offers network operators enhanced capabilities, empowering efficient deployments and management.

Problem

• 5G network
• optimization
• management

Innovation

• TwinRAN
• Digital Twin
• 5G Network Optimization