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Wireless propagation model is of great significance for accurate 5G network deployment. Based on the data set provided by Huawei ModelArts platform, this paper uses the typical method of feature engineering to establish the intelligent wireless propagation model based on back propagation (BP) neural network. The first is feature design, which has designed 12 features from the traditional Cost 231-Hata empirical model and geometric location. Considering the coupling relationship between the features, the angle and distance related features are dimensioned. The second is data processing, using logarithmic transformation, One-Hot coding, missing value processing, etc. Then there is the feature selection, and the final feature set of the model is extracted by the standard deviation, the quantized value of the Pearson coefficient and the feature-reference signal receiving power (RSRP) scatter plot. Finally, the prediction model is built and verified. In order to compare the superiority of the designed features, this paper established and tested three BP neural network models with the same network structure but different inputs to predict the RSRP of different geographical locations. The experimental results verified the superiority of the designed feature set. And a suitable wireless propagation machine learning model was obtained.

Feature engineering based intelligent wireless propagation model for RSRP prediction