Advancing Next Generation Wireless Networks With Digital Twin: Construction, Validation, and Real-World Applications on an Indoor Over-the-Air Testbed - Berk Akgun; Aditya Jolly; Balwinder Sachdev; Divya Ravichandran; Roohollah Amiri; Vikas Jain; Muruganandam Jayabalan; Yitao Chen; Hetal Pathak; Vinay Chande; Mohammad Fahim; Srinivas Yerramalli; et al. (IEEE Access)
Digital Twin (DT) technology has recently emerged as a powerful tool with the potential to revolutionize wireless systems as it enables accurate simulations, better decision-making, and tangible operational improvements. Prior studies on DT within the context of next generation wireless technologies have primarily focused on identifying potential use cases, application scenarios, standardization challenges, and conceptual implementation steps. However, the existing research is limited in translating theoretical ideas into real-world applications. Our research, in this paper, contributes to the practical realization of DT technology in the context of 6G wireless networks, demonstrating its potential impact on network planning, performance, and user experience. In particular, we explore the construction, validation, and applications of DT utilizing an indoor over-the-air (OTA) 5G NR testbed powered by an in-house developed Next Generation Radio Access Network (NG-RAN) that is fully compliant with 3rd Generation Partnership Project (3GPP) and Open RAN standards. First, we explain the integration and implementation steps followed to integrate Qualcomm EdgewiseTM Suite and Service Management and Orchestration (SMO) tools into the NG-RAN architecture, that will eventually enable the applicability of DT for wireless network operations. We then describe our procedure to construct and validate a high-fidelity DT of our OTA testbed modeling both Radio Frequency (RF) environment and system components. We demonstrate two pre-deployment use cases by describing our extensive coverage estimation and network capacity planning tests in OTA. Lastly, we explore how DT enables practical machine learning solutions for post-deployment use cases and share our comprehensive OTA performance results, highlighting that our proposed mobility and positioning techniques outperform the classical approaches in terms of throughput, number of undesired handovers, and positioning accuracy.