  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.
