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 similarity of up to 0.986 in throughput and 0.989 in Signal to Interference plus Noise Ratio (SINR).

翻译：无线网络仿真器正越来越多地用于开发和评估下一代无线网络的新方案。然而，在仿真平台上测试的解决方案的可靠性在很大程度上取决于仿真过程、模型设计和参数设置的精确性。为解决、避免或最小化仿真模型误差的影响，本文将数字孪生（DT）概念应用于大规模无线系统。具体而言，我们展示了全球最大的硬件在环无线网络仿真器Colosseum作为下一代无线规模化实验研究的数字孪生应用。作为概念验证，我们利用信道仿真场景生成与探测工具链（CaST）创建了公开可用的sub-6 GHz室内空口测试平台Arena的数字孪生。随后，我们在真实和数字系统上通过包括蜂窝网络和Wi-Fi节点干扰场景在内的仿真无线环境的实验活动验证了Colosseum数字孪生。实验表明，该数字孪生能够忠实还原真实环境设置，在吞吐量和信号与干扰加噪声比（SINR）上分别获得高达0.986和0.989的平均相似度。