Northeastern University Wireless Networks and Embedded Systems Lab

Digital Twin for Advanced Wireless Research

Digital Twin  

Current wireless technologies are the key enablers of the digital world. They meet the ever-growing demand of connecting larger and larger groups of people, vehicles, wearables, robots,...virtually anything. In this context, powerful experimental wireless platforms have been recently developed to provide an ecosystem for advanced wireless research through repeatable and reproducible experimentation and the creation of large datasets. These platforms are becoming the nexus of AI-enabled wireless research, where researchers can design, develop, train, and test new solutions for NextG wireless systems. However, the reliability of the solutions tested on emulation platforms heavily depends on the precision of the emulation process, model design, and parameter settings.

Solution development and testing for NextG networks are evolving toward integrating actual networked systems with a digital model that provides a replica of the physical network to be used for continuous prototyping, testing, and self-optimization of the living network, namely a Digital Twin (DT). In this research, we develop and test a comprehensive set of tools to create an emulated DT of virtually any real-world wireless scenario in Colosseum; validate the emulated environment through channel sounding; and twin a standardized protocol stack through a CI/CD framework.

The WIoT digital twinning toolchain will enable advanced research in AI-assisted spectrum management; exploration and development of coexistence scenarios in unlicensed spectrum; integration of Commercial Off-the-Shelf (COTS) with the Colosseum channel emulation system, e.g., integration of commodity OBUs and RSUs for C-V2X research; and high-accuracy testing and reliable data collections to develop data-driven algorithms, e.g., orchestrated O-RAN xApps executing Deep Reinforcement Learning algorithms on a RAN Intelligent Controller (RIC) to optimize RAN slicing and radio resource management in coexistence scenarios.


Colosseum as a Digital Twin

Digital Twin Colosseum


Wireless network emulators are being increasingly used for developing and evaluating new solutions for Next Generation (NextG) wireless networks. However, the reliability of the solutions tested on emulation platforms heavily depends on the precision of the emulation process, model design, and parameter settings. To address, obviate or minimize the impact of errors of emulation models, in this work we apply the concept of Digital Twin (DT) to large-scale wireless systems. Specifically, we demonstrate the use of Colosseum, the world's largest wireless network emulator with hardware-in-the-loop, as a DT for NextG experimental wireless research at scale. As proof of concept, we leverage the Channel emulation scenario generator and Sounder Toolchain (CaST) to create the DT of a publicly-available over-the-air indoor testbed for sub-6 GHz research, namely, Arena. Then, we validate the Colosseum DT through experimental campaigns on emulated wireless environments, including scenarios concerning cellular networks and jamming of Wi-Fi nodes, on both the real and digital systems. Our experiments show that the DT is able to provide a faithful representation of the real-world setup, obtaining an average accuracy of up to 92.5% in throughput and 80% in Signal to Interference plus Noise Ratio (SINR).





CaST Sounder


Large-scale wireless testbeds are being increasingly used in developing and evaluating new solutions for next generation wireless networks. Among others, high-fidelity FPGA-based emulation platforms have unique capabilities for faithfully modeling real-world wireless environments in real-time and at scale, while guaranteeing repeatability. However, the reliability of the solutions tested on emulation platforms heavily depends on the precision of the emulation process, which is often overlooked. To address this unmet need in wireless network emulator-based experiments, in this paper we present CaST, a Channel emulation generator and Sounder Toolchain for creating and characterizing realistic wireless network scenarios with high accuracy. CaST consists of (i) a framework for creating mobile wireless scenarios from ray-tracing models for FPGA-based emulation platforms, and (ii) a containerized Software Defined Radio-based channel sounder to precisely characterize the emulated channels. We demonstrate the use of CaST by designing, deploying and validating multi-path mobile scenarios on Colosseum, the world's largest wireless network emulator. Results show that CaST achieves <= 20 ns accuracy in sounding Channel Impulse Response tap delays, and 0.5 dB accuracy in measuring tap gains.




Creating RF Scenarios

Creating RF Scenarios


Recent years have seen the introduction of large-scale platforms for experimental wireless research. These platforms, which include testbeds like those of the PAWR program and emulators like Colosseum, allow researchers to prototype and test their solutions in a sound yet realistic wireless environment before actual deployment. Emulators, in particular, enable wireless experiments that are not site-specific as those on real testbeds. Researchers can choose among different radio frequency (RF) scenarios for real-time emulation of a vast variety of different situations, with different number of users, RF bandwidth, antenna counts, hardware requirements, etc. Although very powerful, in that they can emulate virtually any real-world deployment, emulated scenarios are only as useful as how accurately they can reproduce the targeted wireless channel and environment. Achieving emulation accuracy is particularly challenging, especially for experiments at scale for which emulators require considerable amounts of computational resources. In this paper, we propose a framework to create RF scenarios for emulators like Colosseum starting from rich forms of input, like those obtained by measurements through radio equipment or via software (e.g., ray-tracers and electromagnetic field solvers). Our framework optimally scales down the large set of RF data in input to the fewer parameters allowed by the emulator by using efficient clustering techniques and channel impulse response re-sampling. We showcase our method by generating wireless scenarios for the Colosseum network emulator by using Remcom's Wireless InSite, a commercial-grade ray-tracer that produces key characteristics of the wireless channel. Examples are provided for line-of-sight and non-line-of-sight scenarios on portions of the Northeastern University main campus.





Colosseum Architecture


Colosseum is an open-access and publicly-available large-scale wireless testbed for experimental research via virtualized and softwarized waveforms and protocol stacks on a fully programmable, "white-box" platform. Through 256 state-of-the-art software-defined radios and a massive channel emulator core, Colosseum can model virtually any scenario, enabling the design, development and testing of solutions at scale in a variety of deployments and channel conditions. These Colosseum radio-frequency scenarios are reproduced through high-fidelity FPGA-based emulation with finite-impulse response filters. Filters model the taps of desired wireless channels and apply them to the signals generated by the radio nodes, faithfully mimicking the conditions of real-world wireless environments. In this paper, we introduce Colosseum as a testbed that is for the first time open to the research community. We describe the architecture of Colosseum and its experimentation and emulation capabilities. We then demonstrate the effectiveness of Colosseum for experimental research at scale through exemplary use cases including prevailing wireless technologies (e.g., cellular and Wi-Fi) in spectrum sharing and unmanned aerial vehicle scenarios. A roadmap for Colosseum future updates concludes the paper.




  • D. Villa, M. Tehrani-Moayyed, C. Robinson, L. Bonati, P. Johari, M. Polese, T. Melodia, "Colosseum as a Digital Twin: Bridging Real-World Experimentation and Wireless Network Emulation," IEEE Transactions on Mobile Computing, in press, 2024. [pdf] [bibtex]

  • D. Villa, M. Tehrani-Moayyed, P. Johari, S. Basagni, T. Melodia, "CaST: A Toolchain for Creating and Characterizing Realistic Wireless Network Emulation Scenarios", Proc. of the 16th ACM Workshop on Wireless Network Testbeds, Experimental evaluation & CHaracterization (WiNTECH 2022), Sydney, Australia, October 2022. [pdf] [bibtex]

  • M. Tehrani-Moayyed, L. Bonati, P. Johari, T. Melodia, and S. Basagni, "Creating RF Scenarios for Large-Scale, Real-Time Wireless Channel Emulators," in Proc. of Mediterranean Communication and Computer Networking Conference (MedComNet), Virtual Conference, June 2021. [pdf] [bibtex]

  • L. Bonati, P. Johari, M. Polese, S. D'Oro, S. Mohanti, M. Tehrani-Moayyed, D. Villa, S. Shrivastava, C. Tassie, K. Yoder, A. Bagga, P. Patel, V. Petkov, M. Seltser, F. Restuccia, A. Gosain, K.R. Chowdhury, S. Basagni, T. Melodia, "Colosseum: Large-Scale Wireless Experimentation Through Hardware-in-the-Loop Network Emulation," Proc. of IEEE Intl. Symp. on Dynamic Spectrum Access Networks (DySPAN), Virtual Conference, December 2021. [pdf] [bibtex]

Colosseum Related Works

  • L. Bonati, M. Polese, S. D'Oro, S. Basagni, T. Melodia, "OpenRAN Gym: AI/ML Development, Data Collection, and Testing for O-RAN on PAWR Platforms," Computer Networks, vol. 220, pp. 1-11, January 2023. [pdf] [bibtex]

  • S. D'Oro, M. Polese, L. Bonati, H. Cheng, and T. Melodia, "dApps: Distributed Applications for Real-time Inference and Control in O-RAN," IEEE Communications Magazine, vol. 60, no. 11, November 2022. [pdf] [bibtex]

  • M. Polese, L. Bonati, S. D'Oro, S. Basagni, T. Melodia, "ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms", IEEE Transactions on Mobile Computing, pp. 1-14, July 2022. [pdf] [bibtex]

  • L. Bonati, S. D'Oro, M. Polese, S. Basagni, and T. Melodia, "Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networks", IEEE Communications Magazine, vol. 59, no. 10, pp. 21–27, October 2021. [pdf] [bibtex]

  • C. Robinson, L. Bonati, T. van Nieuwstadt, T. Reiss, P. Johari, M. Polese, H. Nguyen, C. Wat- son, T. Melodia, "eSWORD: Implementation of Wireless Jamming Attacks in a Real-World Emulated Network", IEEE Communications and Networking Conference (WCNC), Glasgow, Scotland, March 2023. [pdf] [bibtex]

  • L. Bonati, M. Polese, S. D'Oro, S. Basagni, T. Melodia, "Intelligent Closed-loop RAN Control with xApps in OpenRAN Gym," Proc. of European Wireless 2022, Dresden, Germany, September 2022. [pdf] [bibtex]

  • S. D'Oro, L. Bonati, M. Polese, T. Melodia, "OrchestRAN: Network Automation through Orchestrated Intelligence in the Open RAN," Proc. of IEEE Intl. Conf. on Computer Communications (INFOCOM), May 2022. [pdf] [bibtex]

  • L. Bonati, M. Polese, S. D'Oro, S. Basagni, T. Melodia, "OpenRAN Gym: An Open Toolbox for Data Collection and Experimentation with AI in O-RAN," Proc. of IEEE WCNC Workshop on Open RAN Architecture for 5G Evolution and 6G, Austin, TX, USA, April 2022. [pdf] [bibtex]

  • T. Melodia, S. Basagni, K.R. Chowdhury, A. Gosain, M. Polese, P. Johari, and L. Bonati, "Tutorial: Colosseum, the World’s Largest Wireless Network Emulator," in Proc. of ACM Intl. Conf. on Mobile Computing and Networking (MobiCom), New Orleans, LA, USA, October 2021. [pdf] [bibtex]

  • B. Casasole, L. Bonati, S. D'Oro, S. Basagni, A. Capone, and T. Melodia, "QCell: Self-optimization of Softwarized 5G Networks through Deep Q-learning," in Proc. of IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, December 2021. [pdf] [bibtex]

  • L. Bonati, S. D'Oro, S. Basagni, and T. Melodia, "SCOPE: An Open and Softwarized Prototyping Platform for NextG Systems," in Proc. of ACM Intl. Conf. on Mobile Systems, Applications, and Services (MobiSys), Virtual Conference, June 2021. [pdf] [bibtex]