Classification of digitally modulated signals in presence of non-Gaussian HF noise

Automatic Modulation Classification (AMC) is the process of classifying the received signals without prior information. This process is an intermediate step between signal detection and demodulations. It serves both military and civilian applications, such as spectrum monitoring and general-purpose universal demodulators. In this paper, we propose a Decision Tree (DT) algorithm to classify a wide class of the single carrier modulations used in High Frequency (HF) band. Specifically, the proposed algorithm addresses the classification of 2FSK, 4FSK, 8FSK, 2PSK, 4PSK, 8PSK, 16QAM, 32QAM, 64QAM, and OQPSK using three features: Temporal Time Domain (TTD), spectral peaks, and number of amplitude levels. Almost all previous research work in AMC assumes the noise model to be Additive White Gaussian Noise (AWGN). Although this assumption is valid in many communications environments, recent literatures show that the HF noise is fluctuating between AWG and Bi-kappa distributions. This work, first, considers the effect of noise model on the previously mentioned features, and then presents simulation results showing the performance of proposed algorithm in such an environment.