5G Wireless Communication System Using Machine Learning (ML)

Millimeter wave (mmWave) frequencies are under research for providing high speed data spectrum for upcoming 5th Generation (5G) communication system. ThemmWave frequencies suffer with high propagation attenuation due to various channel obstructions. There is a demand and need to have an effective method for efficient mmWave propagation. In this, we propose an innovative method for intelligently selecting a grid or group of grids having minimal mmWave propagation attenuation to overcome the effect of obstructions at the transmission end. The proposed 5G ML transmitter system keeps learning mmWave propagation vegetation attenuation values. The ML unit predicts the vegetation attenuation values using regression mode with the algorithms like K-Nearest Neighbors, Decision Tree and Random Forest and also predicts Shannon channel capacity (SCC). It significantly improves the 5G system performance by saving the transmitter power radiation and provides multigigabit data rates.