Machine Learning-based Noise Classification and Decomposition in RF Transceivers

We propose a machine learning-based solution for noise classification and decomposition in RF transceivers. Wireless transmitters are affected by various noise sources, each of which has a distinct impact on the signal constellation points. The proposed approach takes advantage of the characteristic dispersion of points in the constellation by extracting key statistical and geometric features that are used to train a machine learning model. The trained model is, then, capable of identifying the noise source fingerprint, comprised by single or multiple noise sources, for each affected device. Effectiveness of the model has been verified using constellation measurements from a combined set of simulated and actual silicon devices.