Automatic modulation classification technique for radio monitoring

Abstract

The automatic classification of the modulation format of a detected signal is the intermediate step between signal detection and demodulation. If neither the transmitted data nor other signal parameters such as the frequency offset, phase offset and timing information are known, then automatic modulation classification (AMC) is a challenging task in radio monitoring systems. The approach of clustering algorithms is a new trend in AMC for digital modulations. A novel algorithm called 'highest constellation pattern matching' is introduced to identify quadrature amplitude modulation and phase shift keying signals. The obtained simulation and measurement results outperform the existing algorithms for AMC based on clustering. Finally, it is shown that the proposed algorithm works in a real monitoring environment.