Modulation recognition in the 868Â MHz band using classification trees and random forests

Automatic modulation recognition (AMR) enables the detection of different data-transmission formats sharing the same frequency band. One such band is the European 868 MHz band that is dedicated for short-range devices. Recently, an AMR method that applies a feature-based tree has been proposed for this band. In this paper, we present alternative feature-based classifiers that enable a more accurate AMR. In particular, we propose the use of classification tree and random forest classifiers, and we devise an extended set of features for the modulation classification problem at hand. Through simulation experiments we demonstrate a significant improvement in recognition success rate for typical transmission types in the 868 MHz band.