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Content №1-2012

The use of Rayleigh backscattering in a fiber to measure the spectrum of optical signals

Gorbulenko V.V., Nany O.E., Nesterov E.T., Ozerov, A.G, Treschikov V.N.

 

Abstract
The range of power modulation of the Rayleigh scattering in an optical fiber was investigated experimentally, its correlation with the spectrum of the input optical signal into the fiber is shown. Pulsed radiation spectrum broadening is measured by self-phase modulation in fiber, using a method based on measuring the spectrum of power modulation of the Rayleigh scattering in an optical fiber

 

References
1. Baney D.M., Sorin W.V. High resolution optical frequency analysis. In. Fiber optic test and measurement, editor D. Derickson, New Jersey, 1998.

2. Gysel P., Staubli R.K. J. Lightwave Technol., 8, №4, 561 (1990).

3. Nesterov Ye.T., Treshchikov V.N.,.Ozerov A.Zh., Sleptsov M.A., Kamynin V.A., Naniy O.Ye., Susyan A.A. PZHTF., 37, № 9, 55 (2011).

4. Nesterov Ye.T., Sleptsov M.A., Treshchikov V.N., Nanij O.Ye., Susyan A.A., T-Comm, Telecommunications and Transport, No 8 (2010). P.51.

5. Tozoni O., Aksenov S.B., Podivilov Ye.V., Babin S.A. Quant. Electronics, 40, No10, 887 (2010).


Compact loop diversity antenna system for wireless communications

Anikin Konstantin Valerevich, postgraduate student in the department of “Radio Systems”

at the Moscow Technical University of Communications and Informatics (MTUCI)

Belyansky Vladimir Borisovich, candidate of physico-mathematical sciences, docent in the department of “Radio Systems” at the Moscow Technical University of Communications and Informatics (MTUCI)

 

Abstract
This paper examines the compact loop antenna system with polarization diversity, the dimensions of which are 10 х 10 х 5 mm, consisting of two orthogonal loops. To reduce the mutual coupling (both inductive and capacitive) between two closely spaced to each other loops are closed using screens (partitions), made of magnetodielectric material with mr > 1 and er > 1, completely covering each of the loops. Further was demonstrated that there is an optimal ratio between the thickness of screens (partitions), the magnetic permeability er and the dielectric permeability er of the material from which they are made. The relative bandwidth of antenna system considered in this paper is 19%. The antenna system can be used in compact devices designed for wireless data transfer by Wi-Fi MIMO and WiMAX MIMO technologies.

 

Keywords: Compact loop antenna system, magnetodielectric screens, polarization diversity, decoupling factor.

 

References
1. N.N. Voytovich, B.Z. Katsenelenbaum, E.N. Korshunova, etc. Electrodynamics of Antennas with translucent surfaces: The methods of constructive synthesis / M.: Nauka. Gl. red. fiz.-mat. lit., 1989. 176 p.

2. C.K. Ko Samuel and R.D. MurchCompact Integrated Diversity Antenna for Wireless Communications, IEEE Transactions on Antennas and Propagation, Vol. 49, No6, pp. 954-960, June 2001.


New technical determinations on connectors of fiber optic

Portnov E.L., Grigorjan A. K., Kochemasov D.V., Moscow Technical University of Communications and Informatics (MTUCI)

Abstract
Consider modern determination on connectors and fiber-optic cables, developed for employ in field conditions and on any mobile objects. Offer special demands for its employ in any condition.

 

Keywords: Оptic fiber, connectors, field and mobile object, industial influence.

 

References
1. Fiber-optic technology: Current status and prospects. M.: Volokonno-opticheskaya tekhnika, 2005. 575 p.

2. Glenair Fiber Optic Inteconnect Solutions. USA, 2010.

3. Portnov E.L. Principles of construction of primary networks, and optical cable lines. M. Goryachaya liniya- Telekom 2009, 544 p.

4. Portnov E.L. Optical cables and passive components of the communication lines. Moscow Goryachaya liniya- Telekom, 2007, 464 p.

5. A. Othonos and K. Kalli.Fiber Bragg grating Fundamentals and applications in telecommunications and sensing Artech House, Norwood, MA, 1999, 419 p.


Separate call service to improve the efficiency of call centers

Andryeev R.V., Tatarinova N.M.

Abstract
New services for Contact Centers are introduced, these services allow you to increase the number of calls serviced, with a stable workload of operators, the proportion of calls served by IVR-system, as well as to automate the process of informing you of the debt.

 

References
1. Roslyakov A.V., Samsonov M.YU., Shibaeva I.V. Call Centre. M.: EKO-TRENDZ, 2002.

2. Andreev R.V., Tatarinova N.M. Improved performance of Contact Center routing customers in IVR-menu // Report on the X International STC “PT and TT-2009”. Samara, 2009.


A mathematical model of the system operator Center for Emergency Services

Kiselev I.V., Moscow Technical University of Communications and Informatics (MTUCI)

Abstract
For operational decisions on calls coming into the centers of emergency services, the opera-tor’s system must be fixed collection of data about the location of the subscriber and the nature of the event. The article discusses the possibility of increasing the capacity of the call-center emergency services as an example of service “02” the city of Moscow. A mathematical model of the operator’s service as a queuing system with a regular character of service, transition to which is made possible by the automatic locating users.

 

References
1. Stepanova I.V., Kiselev I.V. Effect of intelligently routing calls to the function call-center  // T-Comm, 2010. No4. Pp.51-53.

2. Stepanova I.V., Kiselev I.V. The results of the development of specialized software handle the traffic emergency // T-Comm, 2010. No7. Pp.64-65.

3. Kornyshev Yu.N., Pshenichnikov A.P., Kharkevich A.D. Teletraffic Theory. M.: Radio and Communication, 1996. 272.

4. Shneps M.A. Information distribution systems. Calculation methods: A Reference Guide. M: Communications, 1979. 344 p.

5. Kleinrock L. Queueing Theory. Moscow: Mashinostroenie, 1979. 432 p.


Threshold characteristics of the arrival time estimate random pulse

Svidchenko S.S., Moscow Technical University of Communications and Informatics (MTUCI)

Abstract
Hardware implementation of a random measuring the arrival time of the pulse signal is considered. The limits of applicability of asymptotically exact formulas for the evaluation of characteristics are found.

 

References
1. Trifonov A.P., Zakharov A.V. Receiving a signal from an unknown time delay in the presence of the modulating noise // Math. universities. Sor. Electronics, 1986. V.29. No4. Pp.36-41.

2. Trifonov A.P., Zakharov A.V., Chernoyarov O.V. Estimate of the variance of the random pulse with unknown arrival time // Technology and Electronics, 1996. V.41. No10. Po.1207-1210.

3. Trifonov A.P., Nechaev E.P., Parfenov V.I. Detection of stochastic signals with unknown parameters. Voronezh, Voronezh State University, 1991. 246 p.

4. Kulikov E.I., Trifonov A.P. Parameter estimation for signals on the background noise. M.: Sov. Radio, 1978. 296 p.

5. Malakhov A.N. Cumulant non-Gaussian random processes, analyzes and transformations. M.: Sov. Radio, 1978. 376 p.


The dependence of the limiting distribution of the risk assessment thresholding wavelet coefficients of the signal on the type of noise variance estimation when selecting an adaptive threshold

Shestakov O.V., Moscow Technical University of Communications and Informatics (MTUCI)

Abstract
The asymptotic properties of the risk assessment process at the threshold of the coefficients of the wavelet decomposition of functions satisfying certain smoothness conditions is given. We consider the procedure for selecting the threshold that minimizes the risk assessment. We prove the asymptotic normality of the risk assessment for this choice of threshold. The dependence of the limiting variance of the risk assessment on the method of estimating the variance of noise is given.

 

Keywords: wavelets, threshold processing, adaptive threshold, an unbiased estimate of risk, asymptotic normality, the sample variance, the median absolute deviation from median.

 

References
1. Donoho D., Johnstone I.M. Adapting to Unknown Smoothness via Wavelet Shrinkage // J. Amer. Stat. Assoc., 1995. Vol.90. Pp. 1200-1224.

2. Donoho D., Johnstone I.M. Ideal Spatial Adaptation via Wavelet Shrinkage // Biometrika, 1994. Vol. 81. No3. Pp. 425-455.

3. Donoho D. L., Johnstone I. M., Kerkyacharian G., Picard D. Wavelet Shrinkage: Asymptopia? // J.R. Statist. Soc. Ser. B., 1995. Vol. 57. No2. Pp. 301-369.

4. Marron J. S., Adak S., Johnstone I. M., Neumann M. H., Patil P. Exact Risk Analysis of Wavelet Regression // J. Comput. Graph. Stat., 1998. Vol. 7. Pp. 278-309.

5. Antoniadis A., Fan J. Regularization of Wavelet Approximations // J. Amer. Statist. Assoc., 2001. Vol. 96. No 455. Pp. 939-967.

6. Markin A.V. Shestakov O.V. On the consistency of the risk assessment process at the threshold of wavelet coefficients // Vestn. Moscow. University. Sor. 15. Computing. Mathematics. and Cybernetics., 2010. Number 1. Pp. 26-34.

7. Markin A.V. Limit distribution of the risk assessment process at the threshold of wavelet coefficients  // Computer Science and Applications, 2009. V.3. No 4. Pp. 57-63.

8. Shestakov O. Approximation of the risk assessment thresholding wavelet coefficients of the normal distribution using the sample variance // Computer Science and Applications, 2010. V. 4. No 4. Pp. 73-81.

9. Jansen M. Noise Reduction by Wavelet Thresholding. Springer Verlag, Lecture notes in Statistics. Vol. 161. 2001.

10. Shestakov O.V. The asymptotic normality of the risk assessment thresholding wavelet coefficients of the adaptive threshold selection // Computer Science and Applications, 2012.

11. Dobechie I. Ten lectures on wavelets. Izhevsk: NITs Regular and Chaotic Dynamics, 2001.

12. Mallat S. A Wavelet Tour of Signal Processing. Academic Press, 1999.

13. Abramovich F., Silverman B.W. Wavelet Decomposition Approaches to Statistical Inverse Problems // Biometrika, 1998. Vol. 85. No1. Pp. 115-129.

14. Boggess A., Narkowich F. A First Course in Wavelets with Fourier Analysis. Prentice Hall, 2001.

15. Zakharova, T., Shestakov O. Wavelet analysis and its applications. Textbook. M.: MAX Press, 2009.

16. Serfling R. Approximation theorems of mathematical statistics, John Wiley and Sons. 1980.

17. Hall P., Welsh A.H. Limits theorems for median deviation // Annals of the Institute of Statistical Mathematics, 1985. Vol. 37. No1. Pp. 27-36.

18. W. Feller. An Introduction to Probability Theory and its Applications. M.: “The World”, 1984.

19. Vaart A.W., Wellner J.A. Weak convergence and empirical processes. Springer Verlag. New York. 1996.

20. Kolmogorov A.N. and Tikhomirov V.M. Entropy and capacity of sets in function spaces // Math 1959. V. 14. Number 2 (86). Pp. 3-86.

21. Alexander K. Probability inequalities for empirical processes and a law of the iterated logarithm // Ann. Probab., 1984. Vol. 12. No4. Pp. 1041-1067.

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Forward error correction on systems 10-100 Gbits/s with modulation formats NRZ, RZ, CRZ

Grigorian A.K., Moscow Technical University of Communications and Informatics (MTUCI)

Abstract
Сonsider forward error correction under  rate 10-100 GBit/s with modulation format. NRZ, RZ, CRZ. The problem put in that to demonstrate influence on forward error correction of modulation formats and polarization mode dispersion, also dependence dispersion length  from that characteristics.

 

References
1. G. Agrawal.Nonlinear Fiber Optics. Moscow: Publishing House “Mir”, 1996. 324 p.

2. Portnov E.L., Crop A.Ya., Zelyutkov E.A. Influence of polarization mode dispersion on the signal transduction // Proceedings of MTUCI. M.: “Foreign Media Pablisher”, 2008. Vol.1. Pp.341-344.

3. Portnov E.L., Zelyutkov E.A. On the dispersion length and the signal / noise // T-Comm – Telecommunications and Transport, 2008. No5. Pp.37-38.

4. Fiber-optic technology: Current status and prospects. Moscow: OOO “Fiber-optic technology”, 2005. P.575.


Modulation type recognition using high order cumulants

A.A. Stogov, M.V. Tereshonok, D.S. Chirov, G.V. Kuzmin, Moscow Technical University of Communications and Informatics (MTUCI)

Abstract 

The report concerns results of methods of digital modulation type recognition based on high order cumulants. It is shown that different order cumulants have different separating properties for different modulation types. The results of existing recognition methods are stated. A new method capable of wider variety of modulation types recognition is proposed. The proposed method advantages are conditioned by combination of cumulant analysis and phase distribution estimation techniques.

 

Keywords: cumulant analysis, digital modulation recognition, radio signal.

 

References
1. A. Swami and B. Sadler,“Hierarchical digital modulation classification using cumulants,” IEEE Trans. Commun., vol. 48, No3, pp. 416-428, March 2000.

2. G. Hatzichristos, M.P. Fargues,Classification of Digital Modulation Types in Multipath Environments, IEEE, pp. 1494-1498, 2001.

3. A.F. Young, Classification of Digital Modulation Types in Multipath Environments, Master’s Thesis, NAVAL POSTGRADUATE SCHOOL, June 2008, p.83.

4. Stepanov A.V., Matveev S.A. Methods of computer processing of radio communication systems signals. Moscow: SOLON-Press, 2003. 208 p.


Asymptotically optimal algorithms e-classification of signals in terms of a priori determination of the distribution of interference

Afanasiev V.P., Korolkova T.W., Kosichkina T.P., Moscow Technical University of Communications and Informatics (MTUCI)

Abstract
For the problem of recognition signal is formulated asymptotic optimality criterion. It is shown that asymptotically ?-optimal algorithm to recognition the signals have the property structural stability in relation to changes in the form of probability distributions of values of interference and random parameters of useful signals. For the case when the distribution of interference is known at the site receiving up to a parameter defined by rule of the nonlinear transformation of observations.

 

Keywords: signal recognition, asymptotically optimal algorithm, parametric a priori uncertainty, non-Gaussian noise.

 

References
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5. Chabdarov Sh.M., Nadeev A.F. and other.Distributed adaptive signal processing on a background of non-Gaussian noise // Nonlinear World, 2009. №5. Pp.355-360.

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