Machine- and deep-learning techniques can be applied to help with spectrum analysis in complex scenarios. To support this task, YOTASYS developed a process to generate and label synthetic, channel-impaired I/Q waveforms. These generated waveforms in turn provide the training data for a wide range of deep-learning networks.
Modulation identification is an important function for an intelligent signal receiver. It has numerous applications in cognitive radar, software-defined radio, and efficient spectrum management. To identify both communications and radar waveforms, it’s necessary to classify them by modulation type. For this, meaningful features can be input to a classifier.
While effective, this procedure requires extensive effort and domain knowledge to yield an accurate classification. The wide experience of YOTASYS is key to the success of the application.