The creation of digital twins of experimental testbeds allows the validation of novel wireless networking solutions and the evaluation of their performance in realistic conditions, without the cost, complexity and limited availability of experimental testbeds. Current trace-based simulation approaches for ns-3 enable the repetition and reproduction of the same exact conditions observed in past experiments. However, they are limited by the fact that the simulation setup must exactly match the original experimental setup, including the network topology, the mobility patterns and the number of network nodes. In this paper, we propose the Machine Learning based Propagation Loss (MLPL) module for ns-3. Based on network traces collected in an experimental testbed, the MLPL module estimates the propagation loss as the sum of a deterministic path loss and a stochastic fast-fading loss. The MLPL module is validated with unit tests. Moreover, we test the MLPL module with real network traces, and compare the results obtained with existing propagation loss models in ns-3 and real experimental results. The results obtained show that the MLPL module can accurately predict the propagation loss observed in a real environment and reproduce the experimental conditions of a given testbed, enabling the creation of digital twins of wireless network environments in ns-3.

翻译：实验性试验床的数码双胞胎的创建使得能够验证新型无线联网解决方案,并在没有费用、复杂性和有限的试验试验床可用性的情况下,在现实条件下评估其性能,而没有费用、复杂性和有限的试验试验床的可用性能。目前以轨迹为基础的Ns-3模拟方法能够重复和复制过去实验中观察到的同样确切的条件。但是,由于模拟设置必须与最初的试验设置完全匹配,包括网络地形、移动模式和网络节点的数目。在本文件中,我们提议为ns-3使用机器学习促销损失模块。根据实验试验床收集的网络痕迹,MLPL模块估计传播损失是确定性路径丢失和随机快速毁损的总和。MLPL模块经过单位测试得到验证。此外,我们用真实的网络痕迹、移动模式和网络节点数与现有的传播损失模型在ns-3和实际实验结果进行比较。获得的结果表明,MLPL模块能够准确预测在实际试验环境中观测到的传播损失情况,并复制给定型双胞床试验环境的试验条件。