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Non-parametric Vertical Box Algorithm for Detecting Amplitude Information in the Received Digital Signal
Maria Beljaeva
Pages - 1 - 15     |    Revised - 15-01-2013     |    Published - 28-02-2013
Volume - 7   Issue - 1    |    Publication Date - June 2013  Table of Contents
MORE INFORMATION
KEYWORDS
Modulation Recognition, Modulation Classification, Vertical Box Control Chart, Non-parametric.
ABSTRACT
Vertical boxes algorithm (VBA) for non-cooperative automatic discrimination between digital signals with amplitude information (AI) from those without AI is presented. The problem is not new and several solutions have been proposed. Unlike them VBA needs no information about propagation conditions and the received signal parameters. No SNR and noise distribution assumptions are made, no carrier frequency and no thresholds are required. The only assumption made is the symbol rate interval which may be as wide as desired. The signal is considered only in the time domain. VBA also distinguishes between some other classes of signals.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
1 O. A. Dobre, A. Abdi,. Y. Bar-Ness, and W. Su. “Survey of automatic modulation classification techniques: classical approaches and new trends.” Communications, IET, vol. 1, iss. 2, 2007, pp.137-156.
2 J. H. Pollard. Handbook of Numerical and Statistical Techniques. Cambridge: Cambridge University Press, 1977, pp. 144-145.
3 E. E. Azzouz and A. K. Nandi. Automatic Modulation Recognition of Communication Signals.Boston, Mass, USA: Kluwer Academic Publishers, 1996.
4 R. Wilcox., Introduction to Robust Estimation and Hypothesis Testing. 2nd ed, San Diego, CA:Elsevier Academic Press, 2005, p. 2.
5 S. O. Rice. “Mathematical analysis of random noise.” Bell Systems Technical Journal, vol. 23,1944, pp. 282–332.
6 G. Lippmann. “Conversation with Henri Poincaré.” In Henri Poincaré, Calcul des Probabilités,1896, p. 171.
7 J. Giesbrecht, R. Clarke, and D. Abbot. “An empirical study of the probability density function of HF noise.” Fluctuation and Noise Letters, vol. 6, no. 2, 2006, pp.117-125.
8 V G. Chavali and C.R.C.M. da Silva. “Maximum-likelihood classification of digital amplitudephase modulated signals in flat fading non-Gaussian channels.” IEEE Transactions on Communications, vol. 59, iss. 8, 2011, pp. 2051-2056.
9 A. Hazza, M. Shoaib, A. Saleh, and A. Fahd. “Classification of Digitally Modulated Signals in Presence of Non-Gaussian HF Noise.” in Proc. of ISWCS, 2010, pp.815-819.
10 B. G. Mobasseri. “Constellation Shape as a Robust Signature for Digital Modulation Recognition.” In Proc. of IEEE Veh. Technol. Conf., vol.1, 1999, pp.442-446.
11 D. Boudreau, C. Dubuc, F. Patenaude, M. Dufour, J. Lodge, and R. Inkol, “A fast automatic modulation recognition algorithm and its implementation in a spectrum monitoring application.” in Proc. of IEEE MILCOM’2000, 2000, pp. 732-736.
12 L. De Vito, S. Rapuano, and M. Villanacci. “An improved method for the automatic digital modulation classification.” in Proc. of IEEE IMTC’2008, 2008,pp. 1441-1446.
13 N. Björsell, P. Daponte, L. De Vito, and S. Rapuano. “Automatic signal recognition for a flexible spectrum management.” in Proc.of IEEE IMEKO, 2009, pp. 568-573.
14 L. Anton and I. Vizitiu. “Some aspects of signal recognition.” in Proc. of Distance Learning,Simulation and Communication International Conference, 2011, Brno, Czech Republic, pp .9-15.
15 Y. Xu L. Ge, and B. Wang. “Digital modulation recognition method based on tree-structured neural networks.” in Proc of ICCSN’09, 2009, pp. 708 – 712.
16 J. Aisbett. “Automatic modulation recognition using time-domain parameters.” Signal Processing, vol. 13, No. 3, 1987, pp. 323-329.
17 Y. T. Chan and L. G. Gadbois. “Identification of the modulation type of a signal.” Signal Processing, vol. 16, No. 2, 1989, pp. 149-154.
18 H. Ketterer, F. Jondrall, and A. H. Costa, “Classification of modulation modes using timefrequency methods,” in Proc. of IEEE ICASSP’1999, vol.5, 1999, pp. 2471-2474.
19 Y. Jin, S. Li, Z. Yang, and Y. Wang. “Study of a novel key feature in non-cooperative modulation automatic recognition.” in Proc. of WiCom’2007, 2007, pp. 1240-1243.
20 J. Bagga and N. Tripathi. “Analysis of digitally modulated signals using instantaneous and stochastic features, for classification.” International Journal of Soft Computing and Engineering (IJSCE), vol.1, no. 2, May, 2011, pp. 57-61.
21 M. Kang, C. Lee, and J. Joo. “Automatic recognition of analog and digital modulation signals using DoE filter.” in Proc. of ISCIT’2009, 2009, Incheon, Korea, pp. 609-614.
22 U. Jensen and C. LÄutkebohmert. “Change-Point Models,” in Encyclopedia of Statistics in Quality and Reliability, vol. 1, New York: Wiley, 2007, pp. 306-312.
23 E. Rafajlowicz, M. Pawlak, and A. Steland. “Nonparametric sequential change-point detection by a vertically trimmed box method.” IEEE T Inf Th, vol 56, no 7, 2010, pp 3621-3634.
24 S. Siegel and J. Castellan. Nonparametric Statistics for the Behavioral Sciences. New York:McGraw-Hill Book Company, 1988, pp. 128-137.
Miss Maria Beljaeva
- Russia
maria.beljaeva@gmail.com