Research on Machine Learning and Signal Processing of IQ Signals

IQ signals are a core technology of the zero-IF architecture in modern communication technology, but they are often unstable due to factors such as amplitude and phase imbalances. Utilizing the characteristics of IQ two-way signals, common IQ signal calibration algorithms use compensation, frequency domain calibration, etc. This article uses the characteristics of Machine Learning and Signal Processing to train the existing IQ signals and predict the signals based on the training set. Through simulation experiments and actual data measurement, it can be known that different prediction models will have different correction effects. Among them, the Neural Networks model, which is mainly adopted in this article, has a relatively ideal effect on signal processing and prediction, and can also significantly make the signal smoother.