Automatic digital modulation classification using extreme learning machine with local binary pattern histogram features

Abstract

Discrimination of the Local Binary Pattern (LBP) in the classification of different digital modulation types was investigated in this study. It has been shown that LBP can be used as a feature extraction method for AMC schemes. A new AMC scheme is proposed using Extreme Learning Machine (ELM) as a classifier, which has a faster learning process and better generalization performance than conventional machine learning methods. The study also investigated the stability of the proposed AMC scheme, which is affected by variation in the values of the roll-off factor, frequency and phase offset that can affect the stability and performance of the system. Through simulation, a classification accuracy of over 95% was achieved at low SNR levels such as −2 dB. It was also shown that the proposed AMC scheme is more successful under similar conditions when making comparisons to other studies. © 2019 Elsevier Ltd