A Blind Pre-Processor for Modulation Classification Applications in Frequency-Selective Non-Gaussian Channels

Abstract

This paper presents a new preprocessing stage that allows for the reliable classification of digital amplitude-phase modulated signals in a practical scenario where: 1) the classifier has no knowledge of the timing (symbol transition epochs) of the received signal; 2) the noise added in the channel is non-Gaussian; and 3) the fading experienced by the signal is frequency selective. The proposed preprocessor, which is based on the Gibbs sampling algorithm, is used to acquire timing information and to estimate the channel state and noise distribution parameters blindly, i.e., without knowledge of the received symbol sequence and the modulation scheme used. With the obtained estimates, in a second processing stage, the signal is then classified by using an appropriate (likelihood- or feature-based) classification algorithm. To quantify the performance of the proposed preprocessor, the probability of correct classification obtained by using the preprocessor with different classification algorithms is presented. It is shown that, by using the proposed preprocessor, modulation classification algorithms can perform well compared with clairvoyant classifiers assumed to be symbol synchronous with the received signal and to have perfect knowledge of the channel state and noise distribution.