Abstract:
Automatic classification of analog and digital modulation signals plays an important role in communication applications such as an intelligent demodulator, interference identification and monitoring. The automatic recognition of the modulation format of a detected signal, the intermediate step between signal detection and demodulation, is a major task of an intelligent receiver, with various civilian and military applications.
Obviously, with no knowledge of the transmitted data and many unknown parameters at the receiver, such as the signal power, carrier frequency and phase offsets, timing information, etc., blind identification of the modulation is a difficult task.
In this project various statistical parameters are used to classify digitally modulated signals like BPSK, QPSK, BFSK, ASK and QAM. Instantaneous features of signals like Instantaneous frequency, phase, bandwidth and amplitude are extracted from the given unknown digitally modulated signal, then various statistical parameters like mean, skewness, kurtosis and Standard deviation are applied on the estimated instantaneous features. By applying appropriate threshold to the estimated statistical parameters, the modulation of given unknown signal is recognized automatically. MATLAB environment will be used for the simulation of proposed algorithm.
The developed algorithm will be tested with simulated test inputs of different digital modulation types. The work also involves study of various other modulation classification algorithms and merits & demerits of proposed algorithm.