Wireless communication channel modeling using machine learning

Channel modeling is crucial in the development of wireless communication systems. Analyzing a large amount of data is typical practice before using statistical methods to construct appropriate channel models. Using channel estimate on top of channel modeling, physical layer transmission at huge bandwidth is a major challenge in current mobile communications. New 5G and diverse Internet of Things necessitate more effective channel modeling and estimation techniques. Machine learning (ETM-ML) has been suggested in this research as a way to evaluate the transmission medium. With the arrival of 6G and the heterogeneous Internet of Things (H-IoT), many challenging application scenarios will arise, necessitating the development of a more effective channel modeling and estimate approach. The suggested framework's trustworthiness is proven by the simulation analysis, which is based on correctness and efficiency.