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The classic wireless communication channel modeling is performed usingDeterministic and Stochastic channel methodologies. Machine learning (ML)emerges to revolutionize system design for 5G and beyond. ML techniques such assupervise leaning methods will be used to predict the wireless channel pathloss of a variate of environments base on a certain dataset. The propagationsignal of communication systems fundamentals is focusing on channel modelingparticularly for new frequency bands such as MmWave. Machine learning canfacilitate rapid channel modeling for 5G and beyond wireless communicationsystems due to the availability of partially relevant channel measurement dataand model. When irregularity of the wireless channels lead to a complexmethodology to achieve accurate models, appropriate machine learningmethodology explores to reduce the complexity and increase the accuracy. Inthis paper, we demonstrate alternative procedures beyond traditional channelmodeling to enhance the path loss models using machine learning techniques, toalleviate the dilemma of channel complexity and time-consuming process that themeasurements were taken. This demonstrated regression uses the measurement dataof a certain scenario to successfully assist the prediction of path loss modelof a different operating environment.

Predicting the Path Loss of Wireless Channel Models Using Machine Learning Techniques in MmWave Urban Communications