Digital twins have been emerging as a hybrid approach that combines the benefits of simulators with the realism of experimental testbeds. The accurate and repeatable set-ups replicating the dynamic conditions of physical environments, enable digital twins of wireless networks to be used to evaluate the performance of next-generation networks. In this paper, we propose the Position-based Machine Learning Propagation Loss Model (P-MLPL), enabling the creation of fast and more precise digital twins of wireless networks in ns-3. Based on network traces collected in an experimental testbed, the P-MLPL model estimates the propagation loss suffered by packets exchanged between a transmitter and a receiver, considering the absolute node's positions and the traffic direction. The P-MLPL model is validated with a test suite. The results show that the P-MLPL model can predict the propagation loss with a median error of 2.5 dB, which corresponds to 0.5x the error of existing models in ns-3. Moreover, ns-3 simulations with the P-MLPL model estimated the throughput with an error up to 2.5 Mbit/s, when compared to the real values measured in the testbed.

翻译：数字孪生作为一种混合方法应运而生，它结合了仿真器的优势与实验测试平台的现实性。通过精确且可重复的设置复制物理环境的动态条件，无线网络数字孪生可用于评估下一代网络的性能。本文提出基于位置的机器学习传播损耗模型（P-MLPL），能够在ns-3中快速创建更精确的无线网络数字孪生。基于实验测试平台采集的网络轨迹，该模型通过考虑节点的绝对位置和流量方向，估算收发端数据包遭受的传播损耗。我们通过测试套件对P-MLPL模型进行了验证。结果表明：该模型预测传播损耗的中位误差为2.5 dB，仅为ns-3现有模型误差的0.5倍。此外，与测试平台实测值相比，采用P-MLPL模型的ns-3仿真对吞吐量的估计误差不超过2.5 Mbit/s。