Prediction of Path-loss Model for Millimeter Wave Communication using Deep Learning

Path loss represents a crucial element affecting the positioning of base stations within cellular networks. Consequently, a novel approach presented in the research for estimating signal attenuation in high-frequency wireless communication, specifically in millimeter-wave (mmWave) communication. The utilization of mmWave communication harbors the potential to enhance wireless data rates significantly owing to its expansive bandwidth. Nonetheless, it encounters impediments as the atmosphere can readily obstruct and diminish signals at these frequencies. A deep learning and artificial intelligence methodology was employed to address these obstacles. An extensive and varied dataset compiled, encompassing measurements derived from mmWave channels, environmental particulars, and signal attenuation data. This dataset encompassed diverse scenarios such as urban areas, suburban regions, and indoor settings, thus enabling the model to function effectively across varying contexts. Comprehensive data refinement and organization procedures established to prepare the dataset for the training of advanced learning models. Various types of neural networks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) investigated to ensure the model's ability to interpret the complex patterns within the data. Thorough testing conducted to validate the model's proficiency in accurately predicting signal attenuation across different scenarios. The outcomes demonstrated the superior performance of the deep learning model compared to conventional approaches, displaying its adaptability to diverse environments and underscoring its utility in the planning and deployment of mmWave communication networks. In summary, this study furnishes a valuable instrument for creating efficient and dependable mmWave communication systems capable of facilitating high-speed data transmission in various real-world contexts.