Towards a Robust and Efficient Classifier for Real World Radio Signal Modulation Classification

Automatic modulation classification for radio signals is an important task in many applications, including cognitive radio, radio spectrum monitoring and signal decoding in non-cooperative communications. Recent studies in this area apply various deep learning methods to achieve accurate classification. However, due to the nature of radio signals, distortions during transmission are often unforeseen and unpredictable, which poses a need for robust learning models. At the same time, there is the need for fast real-time modulation classification to meet strict timing requirements. In this work, we propose a lightweight deep learning model that accurately and quickly classifies the modulation of signals having different types of distortions, without the need to be trained using distorted signals. Our model trains 25% faster and classifies 36% faster compared to the state-of-the-art [1], with smaller accuracy degradation on datasets generated using distortion parameters that do not appear in the training set.