The use of Rayleigh backscattering in a fiber to measure the spectrum of optical signalsGorbulenko V.V., Nany O.E., Nesterov E.T., Ozerov, A.G, Treschikov V.N.
Abstract
References 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 communicationsAnikin 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
Keywords: Compact loop antenna system, magnetodielectric screens, polarization diversity, decoupling factor.
References 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 opticPortnov E.L., Grigorjan A. K., Kochemasov D.V., Moscow Technical University of Communications and Informatics (MTUCI) Abstract
Keywords: Оptic fiber, connectors, field and mobile object, industial influence.
References 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 centersAndryeev R.V., Tatarinova N.M. Abstract
References 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 ServicesKiselev I.V., Moscow Technical University of Communications and Informatics (MTUCI) Abstract
References 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 pulseSvidchenko S.S., Moscow Technical University of Communications and Informatics (MTUCI) Abstract
References 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 thresholdShestakov O.V., Moscow Technical University of Communications and Informatics (MTUCI) Abstract
Keywords: wavelets, threshold processing, adaptive threshold, an unbiased estimate of risk, asymptotic normality, the sample variance, the median absolute deviation from median.
References 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. 22. Shen X., Wong W.H. Convergence rate of sieve estimates // Ann. Statist., 1994. Vol. 22. No2. Pp. 580-615. Forward error correction on systems 10-100 Gbits/s with modulation formats NRZ, RZ, CRZGrigorian A.K., Moscow Technical University of Communications and Informatics (MTUCI) Abstract
References 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 cumulantsA.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 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 interferenceAfanasiev V.P., Korolkova T.W., Kosichkina T.P., Moscow Technical University of Communications and Informatics (MTUCI) Abstract
Keywords: signal recognition, asymptotically optimal algorithm, parametric a priori uncertainty, non-Gaussian noise.
References 2. Mahmoud Q.H.Cognitive Networks: Towards self-aware Networks, John Wiley and Sons, 2007. – 368 p. 3. Verdu S.Multiuser Detection. Cambridge, U.K.: Cambridge Univ. Press, 1998. – 497 p. 4. Chabdarov Sh.M., Zakirov Z.G. and other.Adaptive algorithmpermits multiple-signal // Telecommunications, 2003. No11. Pp. 2-5. 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. 6. LevinB.R.Theoreticalfoundationsofstatisticalradioengineering.M.:RadioISvyaz, 1989, 656 p. 7. Repin V.G., Tartakovsky G.P.Statistical synthesis with a priori uncertainty and adaptation of information systems. Sov. Radio, 1977, 432 p. |