TwinRAN: Twinning the 5G RAN in Azure Cloud
<|reference_start|>TwinRAN: Twinning the 5G RAN in Azure Cloud: 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.<|reference_end|>
@article{deshpande2024twinran:,
    title={TwinRAN: Twinning the 5G RAN in Azure Cloud},
    author={Yash Deshpande, Eni Sulkaj, Wolfgang Kellerer},
    journal={arXiv preprint arXiv:2407.13340},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13340},
    primaryClass={cs.NI}
}