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Erschienen in:
2024 | OriginalPaper | Buchkapitel
Path Loss Prediction of 5G in the 24.25–27.5 GHz Band Based on Machine Learning
verfasst von : Zaid Abedeen, Sunday Cookey Ekpo, Fanuel Elias, Muhammad Ijaz, Umar Raza, Stephen Alabi, Liangxiu Han
Erschienen in: The Second International Adaptive and Sustainable Science, Engineering and Technology Conference
Verlag: Springer Nature Switzerland
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Abstract
Millimetre-wave 5G signals require accurate path loss predictions due to the low spectral and energy efficiencies of pre-5G networks. This paper proposes a hybrid machine learning technique comprising an environment classifier that determines the propagation environment using a convolutional neural network (CNN) in the TensorFlow machine learning framework and a path loss model using the XGBoost model. The results of the evaluation demonstrate the model’s exceptional accuracy in predicting path loss for the 5G n258 standard (24.25–27.5 GHz) band. Through extensive training and testing using a carefully constructed dataset, the model achieves a root mean square error (RMSE) under 1 dB when compared with the empirical 26 GHz band measurements. Moreover, the machine learning model demonstrates a low computational latency with the parameter sweep predictions of 0.23 s, yielding a 99.65% decrease in execution time compared with the conventional methods.