PROJECT TITLE :
Automatic modulation classification technique for radio monitoring
The automatic classification of the modulation format of a detected signal is that the intermediate step between signal detection and demodulation. If neither the transmitted data nor alternative signal parameters like the frequency offset, part offset and timing information are known, then automatic modulation classification (AMC) may be a challenging task in radio monitoring systems. The approach of clustering algorithms may be a new trend in AMC for digital modulations. A unique 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 primarily based on clustering. Finally, it's shown that the proposed algorithm works during a real monitoring environment.
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