Automatic Modulation Recognition for Digital Communication Signals
Bhawna1, Mukhwinder Kaur2, G. C. Lall3

1Mukhwinder Kaur, Department of Electronics & Communication Engineering., Kurukshtera University, Kurukshtera, Haryana College of Technology & Management, Kaithal, India.
2Bhawna, Department of Electronics & Communication Engineering, Kurukshtera University, Kurukshtera, , Haryana College ofTechnology & Management, Kaithal, India.
3G.C Lall, Department of Electronics & Communication Engineering.,Kurukshetra University, Kurukshtera, , Haryana College ofTechnology & Management, Kaithal, India.

Manuscript received on April 15, 2012. | Revised Manuscript received on April 20, 2012. | Manuscript published on May 05, 2012. | PP: 110-114 | Volume-2 Issue-2, May 2012 . | Retrieval Number: B0528042212/2012©BEIESP
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Abstract: Different modulation techniques are used for different signal transmission. These techniques give versatility to the transmission medium as well as make user easy to work in such computational field. With prior no knowledge of data transmitted and various unspecified parameters at receiver side like the carrier frequency, phase offsets and signal power etc., blind detection of the modulation is challenging. This becomes more difficult at the time of fading. That’s why recognizing these modulation schemes is useful for various technical purposes and especially quite significant for the military, wireless and COMINT applications. Digital modulation recognition is based on some parameters especially statistical parameters. Till now various recognition algorithms have been developed and still developing. The recognition algorithms can be divided into two major groups ‘maximum likelihood approach (MLA) and pattern recognition approach (PRA). In this paper we are emphasizing on the theoretical information of these techniques of modulation recognition along with ANN modulation recognizer for m-ary modulation techniques. A general application of modulation recognition in field of SDR is also proposed.

Keywords: Maximum likelihood, Pattern Recognition, Modulation Detection Scheme, Software Defined Radio, Artificial neural network