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This paper proposes a method to predict received power in urban areadeterministically, which can learn a prediction model from small amount ofmeasurement data by a simulation-aided transfer learning and data augmentation.Recent development in machine learning such as artificial neural network (ANN)enables us to predict radio propagation and path loss accurately. However,training a high-performance ANN model requires a significant number of data,which are difficult to obtain in real environments. The main motivation forthis work was to facilitate accurate prediction using small amount ofmeasurement data. To this end, we propose a transfer learning-based predictionmethod with data augmentation. The proposed method pre-trains a predictionmodel using data generated from ray-tracing simulations, increases the numberof data using simulation-assisted data augmentation, and then fine-tunes amodel using the augmented data to fit the target environment. Experiments usingWi-Fi devices were conducted, and the results demonstrate that the proposedmethod predicts received power with 50% (or less) of the RMS error ofconventional methods.

Transfer Learning-Based Received Power Prediction with Ray-tracing Simulation and Small Amount of Measurement Data