Neural Network Architecture to Predict Radio Wave Attenuation in a 5G Network

The purpose of this study is to use neural networks to predict radio wave attenuation in 5G networks, addressing the challenges of complex wave propagation and optimizing network performance. The research developed and trained a neural network model that effectively predicts radio wave attenuation in the dynamic and complex context of 5G networks. The model combines spatial data obtained using convolutional neural networks (CNN) with sequential data analyzed using long short-term memory (LSTM) networks. This research contributes to the field of 5G network optimization by introducing an innovative approach for radio wave attenuation prediction. The key contributions of this study can be summarized as follows:–Improved prediction accuracy - The neural network model demonstrates particular accuracy in predicting the attenuation of radio waves. Its performance metrics, including MAE and RMSE, consistently outperform conventional models, making it a valuable tool for optimizing 5G network design;–Improved network performance - Accurate radio attenuation predictions allow network operators to optimize coverage and capacity, resulting in improved signal quality and reduced interference. This in turn improves the overall performance and quality of service in 5G networks.–Incorporation of spatial and sequence data - The combined use of CNN for spatial data and LSTM for sequence data enables a comprehensive analysis of the complex factors affecting radio wave attenuation in 5G networks. This new approach significantly improves the prediction accuracy.