  Machine learning (ML) offers a promising solution to pathloss prediction.
However, its effectiveness can be degraded by the limited availability of data.
To alleviate these challenges, this paper introduces a novel
simulation-enhanced data augmentation method for ML pathloss prediction. Our
method integrates synthetic data generated from a cellular coverage simulator
and independently collected real-world datasets. These datasets were collected
through an extensive measurement campaign in different environments, including
farms, hilly terrains, and residential areas. This comprehensive data
collection provides vital ground truth for model training. A set of channel
features was engineered, including geographical attributes derived from LiDAR
datasets. These features were then used to train our prediction model,
incorporating the highly efficient and robust gradient boosting ML algorithm,
CatBoost. The integration of synthetic data, as demonstrated in our study,
significantly improves the generalizability of the model in different
environments, achieving a remarkable improvement of approximately 12dB in terms
of mean absolute error for the best-case scenario. Moreover, our analysis
reveals that even a small fraction of measurements added to the simulation
training set, with proper data balance, can significantly enhance the model's
performance.
