Blockage Prediction of an Urban Wireless Channel Characterization Using Classification Artificial Intelligence

Abstract

The global deployment of 5G wireless networks has introduced significant advancements in data rates, latency, and energy efficiency. However, the rising demand for immersive applications (e.g., virtual and augmented reality) necessitates even higher data rates and lower latency, driving research toward sixth-generation (6G) wireless networks. This study addresses a major challenge in post-5G communication: mitigating signal blockage in high-frequency millimeter-wave (mmWave) bands. This paper proposes a novel framework for blockage prediction using AI-based classification techniques to enhance signal reliability and optimize connectivity. The proposed framework is evaluated comprehensively using performance metrics such as accuracy, precision, recall, and F1-score. Notably, the NN Model 4 achieves a classification accuracy of 99.8%. Comprehensive visualizations—such as learning curves, confusion matrices, ROC curves, and precision-recall plots—highlight the model’s performance. This study contributes to the development of AI-driven techniques that enhance reliability and efficiency in future wireless communication systems.