Author: Huan Nguyen

The future of wireless communication is being shaped by the potentials of 6G networks, which promise faster speeds, lower latency, and more reliable connectivity to support an array of advanced applications, from autonomous vehicles to smart cities. However, the complexity of these networks poses significant challenges in terms of real-time optimisation, energy efficiency, and security. At the London Digital Twin Research Centre (LDTRC), we are exploring cutting-edge research that combines Quantum Computing (QC) and Digital Twin (DT) technologies to address these challenges in Open Radio Access Network (O-RAN) architecture.

O-RAN is an emerging framework that enables a flexible, cost-effective, and open approach to managing wireless networks. Unlike traditional telecom architectures, O-RAN is based on modular, disaggregated components that can be sourced from multiple vendors, increasing adaptability and innovation. However, this modularity also introduces challenges in coordinating real-time operations, optimising resources, and safeguarding data security across diverse systems.

DTs are virtual models that mirror the physical components of a network, allowing for real-time monitoring, simulation, and optimisation. They enable network operators to test “what-if” scenarios, optimise configurations, and predict potential issues without disrupting live operations. When enhanced by QC, DTs become even more powerful, capable of running multiple complex simulations simultaneously and solving optimisation problems at unprecedented speeds.

At LDTRC, we are now leading two projects that explore the integration of QC and DT technologies into O-RAN:

1. 1. Exploring the concept of QC-Based Digital Twin for O-RAN:We will study and explore the concept of QC-Based DT for O-RAN, with collaborations between partners from the UK and India. Learn more about the project here.
   2. Quantum-Enhanced Digital Twin for Secure Open RAN: This collaborative effort with researchers from the UK and Japan explores deeper how Quantum Computing can amplify the capabilities of Digital Twins in O-RAN. By using QC’s computational power, the project seeks to optimise network performance and enhance efficiency. A real-world case study will demonstrate how QC-assisted DTs can transform O-RAN operations, providing tangible benefits for network operators and end-users alike. Find out more about this project here.

These projects highlight LDTRC’s commitment to addressing the most pressing challenges in next-generation telecommunications. By combining the strengths of Digital Twin and Quantum Computing technologies, we aim to deliver practical solutions for O-RAN that are scalable, efficient, and secure.

Stay tuned for updates on these exciting projects!

Our PhD student Mahnoor Yaqoob has got a paper accepted at the ICC’23 workshop, as part of the work from the EPSRC project “Digital Twin for OpenRAN” through the UK-India Future Networks Initiative (UKI-FNI). The paper focuses on the DT approach for end-to-end network slicing solution which address both RAN and Core parts to improve automation and efficiency of the Beyond 5G network.

On 2nd February 2023, Professor Huan Nguyen was invited to join a panel discussion at the Workshop “Engineering Digital Twins in Practice” hosted at Factory 2050 in AMRC Sheffield. The panel, chaired by Professor David Wagg (University of Sheffield), included other panelists John Patsavellas (Cranfield University), Jan Wolber (GE Healthcare), and Ali Nicholl (IOTICS). The benefits, challenges and specific use cases on Digital Twin technology were discussed and shared with 100+ in-person and online audiences. Prof. Nguyen presented some examples of the DT work at LDTRC for applications on smart factory and 6G mobile networks. The event also featured several other talks on DT and was ended with a tour of fantastic facilities at AMRC’s Factory 2050.

Continue reading “Panel Discussion at AMRC Workshop “Engineering Digital Twins in Practice””

As part of the work from Project “Digital twin for Industry 4”, the project team have developed deep learning techniques and advanced/timely solutions of Industrial IoTs, communication (e.g., Reconfigurable Intelligent Surface) to address the automation and smart processes in manufacturing and factory settings. The outcomes were accepted for publications in two journal papers on the highly reputed IEEE Journal of Internets of Things.

Paper 1: RIS-aided Smart Manufacturing: Information Transmission and Machine Health Monitoring

Paper 2: Unsupervised Deep Learning-based Reconfigurable Intelligent Surface Aided Broadcasting Communications in Industrial IoTs

Continue reading “Publication News: Smart manufacturing with Industrial IoTs and machine learning”

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