A system and method for applying supervised learning to model a second wireless channel environment based upon data collected for a first wireless channel environment. In various embodiments, regression techniques are used to overcome known channel modeling issues. Using the data of one particular communication environment, it is possible to predict a path loss model of a different communication environment. As such, the required number of measurements and the complexity of the model prediction is greatly reduced.Traditional channel modeling to enhance the path loss models are becoming more complex and time consuming due to the deployment of new frequency bands and increased traffic data. Channel modeling can be viewed as data mining, with machine learning techniques being viewed as an alternative to analytical and deterministic approaches for predicting model. This new method of machine learning to predict the wireless path loss minimizes the complexity, increases the accuracy, and reduces the number of measurements from a computational standpoint. It has the potential to revolutionize the system design of 5G and beyond.
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