Blind Modulation Classification under Non-Gaussian Noise via Radio Frequency Analytics

Blind modulation classification has emerged as a promising technology in many military and civilian applications, such as cognitive radios, satellite systems, etc. However, it is very challenging to support this blind mechanism within non-Gaussian noise environments, which recently have been identified in a variety of electromagnetic communication networks. Also, start-of-the-art classification methods are mainly based on neural networks or deep learning, which inevitably induces heavy computation loads and thus cannot proactively learn from wireless data in real time. To address the challenges, this paper introduces a series of low- computation radio frequency analytics, including generalized cyclic spectrum (GCS), principal component analysis (PCA), and support vector machine (SVM), which enables the blind modulation classification under non-Gaussian noise. First, based on raw sensory signals and the designed bounded nonlinear function, GCS is extracted as the radio frequency feature to facilitate discrimination of modulation schemes. This GCS can also effectively suppress the burstiness impact of non-Gaussian noise. Then, PCA method is adopted to optimally reduce the dimensionality of GCS features, and a simple and efficient SVM classifier is employed to identify the exact modulation of received signals. Both Monte Carlo simulations and real- data experiments confirm that the proposed design outperforms existing solutions with higher classification accuracy and robustness, i.e., at least 13\% improvement of recognition accuracy in very low (-2 dB) generalized signal-to-noise ratio scenario.