Spectrum analysis and signal identification is challenging because of the range of waveforms that exist in any given frequency band. In addition to the crowded spectrum, the environment tends to be diverse in terms of propagation conditions and non-cooperative interference sources.
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 will in turn provide the training data that can be used in a wide range of DL 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 from YOTASYS is ultimate for the success of the application.