Analysis of Digitally Modulated Signals using Instantaneous and Stochastic Features, for Classification
Jaspal Bagga1, Neeta Tripathi2

1Jaspal Bagga Electronics and telecommunication, Shri Shankaracharya College of Engineering and Technology. Bhilai, India.
2Neeta tripathi, Principal, Shri Shankaracharya institute of Technology and Management, Bhilai, India.
Manuscript received on April 25, 2011. | Revised Manuscript received on May 02, 2011. | Manuscript published on May 05, 2011. | PP: 57-61 | Volume-1 Issue-2, May 2011. | Retrieval Number: A038051211
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Abstract: 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. AMR is also believed to play an important role in the implementation of Software Defined Radio (SDR) of the 4th-Generation (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 This paper describes one application that exploits the flexibility of a software radio. As compared to the previous work this approach uses stochastic features to distinguish modulated signals for varying Signal to Noise Ratio (SNR). The proposed method is simple effective and robust. It is based on the stochastic features derived from instantaneous features to classify digital modulation signals.. This method is capable of differentiating ASK2, ASK4, FSK2, FSK4, PSK2 and PSK4 signals at the output of a typical high frequency channel with white Gaussian noise, Unlike most other existing methods, proposed method assumes no prior information of the incoming signal (symbol rate, carrier frequency, amplitude etc.). Extensive simulation results demonstrate that this approach is robust in various practical situations in identifying the modulated signals. When SNR is less than 5 dB, the percentage of correct identification is about 97%which increases to almost 100% for SNR 20db.
Keywords: Digital signals, Modulation Classification, Signal To Noise Ratio, Stochastic features.