Study and Comparison of Various Modulation Classification Techniques under Noisy Channel Conditions

Automatic Modulation Classification is a procedure performed at the receiver based on the received signal before demodulation when the modulation format is not known to the receiver. : Automatic Modulation Classification is also believed to play an important role in the implementation of Software Defined Radio (SDR) of the 4thGeneration (4G) communication system. The ability to automatically select the correct modulation scheme used in an unknown received signal is a major advantage in a wireless network. 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. The work aims in devising low complexity blind algorithm under noisy and frequency selective channel conditions Database has been generated for various modulated signals such as 2ASK, 4ASK, 2FSK,4FSK, BPSK and QPSK and QAM signals. Additive White Gaussian Noise (AWGN) noise is added and signals are analyzed for varying Signal To Noise (SNR) ranging from 30db to a lower-bound channel SNR of 0db. Keywords— Additive White Gaussian Noise, Automatic Modulation Classification, digital modulation, Statistical parameters, Wavelet transform,