Automatic modulation classification system for OFDM signals

Abstract

Automatic modulation classification (AMC) is the process of automatically determining the modulation technique used in a received signal. An important part of identifying an unknown signal is to determine its modulation parameters; therefore an important part of the automatic modulation classification (AMC) of orthogonal frequency-division multiplexing (OFDM) signals is to determine the modulation parameters of the signal. The purpose of this research is to explore and evaluate existing methods of blindly determining the parameters of an OFDM signal, as well as determining if OFDM is present in a signal of interest (SOI). Improvements to and novel uses of existing feature extraction and parameter recognition techniques are also explored and evaluated. These techniques include the Giannakis-Tsatsanis test and a MUltiple SIgnal Classification (MUSIC) based test to determine the number of OFDM sub-carriers. It was determined that the Giannakis-Tsatsanis test could not be used to distinguish OFDM signals from several other modulation techniques. A MUSIC algorithm based test to determine the number of sub-carriers present in an OFDM signal was also evaluated. A shortcoming of this test, namely that it is computationally intensive, was addressed by improving the test to use the Matlab® pmusic function. This greatly reduced the computational complexity of the test. Finally, it was determined that this pMUSIC algorithm could also be used to determine the presence of OFDM, thereby addressing a shortcoming of existing methods by developing a suitable method.