Applying Machine Learning for Generating Radio Channel Coefficients : Practical insights into the process of selectingand implementing machine learning algorithms for spatial channel modelling

Abstract: One cornerstone in building future 5G and beyond wireless systems is to mimic the real-world environment using a simulator. The simulator needs to reflect the experienced propagation environment by the device in different scenarios. Today, the methods used to generate such an environment and finding the signal qualities at certain locations can be time-consuming for large cities with many base stations and devices. The objective of this project is speed up an existing SCM channel generator by replacing certain time-critical numerical formulas with a machine learning (ML) model that can generate the channel coefficients directly. The expectation is that this setup will provide much faster generations than any existing solution. A machine learning paradigm is suggested and implemented. The results suggests that a model can learn and generalize from the training data, and that provided solution is a possible configuration for modelling radio channels. Conclusions regarding the implementational considerations are made as guidance for future work.