{'fraction_non_alphanumeric': 0.015580736543909348, 'fraction_numerical': 0.0056657223796034, 'mean_word_length': 4.991525423728813, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Machine Learning-based Path Loss Modeling in Urban Propagation Environments

This paper applies a deep learning approach to model the mechanism of path loss based on the path profile in urban propagation environments. Simulation results show that the proposed path loss model outperforms the conventional models when operating in the 3.5 GHz frequency band. To explain the internal behavior of the proposed deep-learning-based model, which is a black box in nature, eight relevant features were selected to model the path loss based on a linear regression approach. Simulation results show that the accuracy of the explanatory model reached 72% when it was used to explain the proposed deep learning model.