Abstract
This paper addresses Automatic Blind Modulation Recognition (ABMR)problem, utilizing a Mean Square Error (MSE) decision rule to recognize and differentiate M-ary PSK modulated signals in presence of noise and fading. The performance of the modulation recognition scheme has been evaluated by simulating different types of PSK signals. By putting appropriate Mean Square Error Difference Threshold (MSEDT) on Mean Square Error (MSE), the proposed scheme has been found to recognize the different modulated signals with 100% recognition accuracy at Signal to Noise Ratio (SNR) as low as 1 dB in AWGN channels. The data samples required to be used for performing recognition is very small, thereby greatly reducing the time complexity of the recognizer. For fading signal Constant Modulus (CM) equalization has been applied prior to performing recognition. It has been observed that when CM equalization is used, 100% recognition can be achieved at SNR as low as 6 dB.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Azzouz, E.E., Nandi, A.K.: Automatic modulation recognition of communication signals. Kluwer Academic Publishers, Dordrecht (1996a)
Azzouz, E.E., Nandi, A.K.: Procedure for automatic recognition of analog and digital modulations. In: IEE Proceedings on Communication, IEE, UK (1996b)
Boiteau, D., Martret, C.L.: A generalized maximum likelihood framework for modulation classification. In: ICASSP’98, IEEE International conference on Acoustics, Speech, and Signal Processing, IEEE, USA (1998)
Chen, S., Chng, E.S.: Concurrent constant modulus algorithm and soft decision directed scheme for fractionally-spaced blind equalization. In: ICC ’04, IEEE international conference on Communication, IEEE, USA (2004)
Halmi, M.H., Abdalla, A.G.E.: Detection of modulation scheme for software defined radio systems in 4th generation mobile network. In: APCC’03, The 9th Asia-Pacific conference on communications (2003)
Hsue, S.Z., Soliman, S.S.: Automatic modulation classification using zero crossing. In: IEE Proceedings on Radar and Signal Processing, IEE, UK (1990)
Liedtke, F.F.: Computer simulation of an automatic classification procedure for digitally modulated communication signals with unknown parameters. In: Signal Processing, Elsevier North-Holland, Inc. Amsterdam (1984)
Mobasseri, B.G.: Constellations shape as a robust signature for digital modulation recognition. In: MILCOM ’99, IEEE Military Communication Conference Proceedings, IEEE, USA (1999)
Mobasseri, B.G.: Digital modulation classification using constellation shape. In: Signal Processing, Elsevier North-Holland, Inc. Amsterdam (2000)
Proakis, J.G.: Digital Communication, 4th edn. McGraw-Hill, New York (2001)
Soliman, S.S., Hsue, S.Z.: Signal classification using statistical moments. In: IEEE Transactions on Communications, IEEE, USA (1992)
Umebayashi, K., et al.: Blind adaptive estimation of modulation scheme for software defined radio. In: PIMRC’00, The 11th IEEE International symposium on Personal, Indoor and Mobile Radio Communication, IEEE, USA (2000)
Weaver, C.S., et al.: The automatic classification of modulation types by pattern recognition. In: Standford electronics laboratories Technical report No.1829-2. Standford electronics laboratories (1969)
Wei, W., Mendel, J.M.: A fuzzy logic method for modulation classification in non-idle envoronment. In: IEEE Transaction on Fussy Syatems, IEEE, USA (1999)
Wong, M.L.D., Nandi, A.K.: Automatic digital modulation recognition using artificial neural network and genetic algorithm. In: Signal Processing, Elsevier North-Holland, Inc. Amsterdam (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Naik, M.V., Mahanta, A., Bhattacharjee, R., Nemade, H.B. (2007). An Automatic Blind Modulation Recognition Algorithm for M-PSK Signals Based on MSE Criterion. In: Filipe, J., Coelhas, H., Saramago, M. (eds) E-business and Telecommunication Networks. ICETE 2005. Communications in Computer and Information Science, vol 3. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75993-5_22
Download citation
DOI: https://doi.org/10.1007/978-3-540-75993-5_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75992-8
Online ISBN: 978-3-540-75993-5
eBook Packages: Computer ScienceComputer Science (R0)