This study investigates the potential interference between 5G New Radio (NR) and radar altimeters in various environments, focusing on the impact on different radar types at varying heights and distances in urban and rural macrocell settings. The research also explores the development of a Convolutional Neural Network (CNN) deep learning model to classify and identify 5G NR and radar altimeter signals, aiming to detect harmful interference and enhance overall aviation safety. The results reveal significant interference effects on radar altimeters from various 5G base station configurations, especially at lower altitudes, and demonstrate the exceptional performance of the CNN model in classifying signals with high accuracy, sensitivity, and specificity. These findings highlight the importance of ongoing research to address interference mitigation techniques and improve signal classification methods, ensuring the safe coexistence of 5G and radar altimeter systems.
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