Content №5-2011

Measurements on a TSS network

Abstract
The list of necessary measurements is defined on TSS networks by means of which it is possible to guarantee reliable interaction of networks of telecommunication with a communication network of the general using (SSOP). The order of carrying out measurements is considered at connection of a network of telecommunication to SSOP. Standard techniques of carried-out measurements and limiting parameters of measured characteristics are given.

Keywords: Clock network synchronization, neio?ineaiae, time interval, primary reference generator, error of a time interval, maximum error of a time interval, deviation of a time interval, frequency drift.

References
1. Koltunov M.N., Legotin N.N., Shvarc M.L. Network synchronization in communication systems. [Setevaya sinhronizaciya v sistemah svyazi]. – I.: Syrus systems, 2007. – 240 p.

2. The order of the Ministry of Information Technologies and Communications ?161 from 07.12.2006 about the statement of Rules of use of the equipment of clock network synchronization.

3. Mel’nikova N.F. Metrological providing system of clock network synchronization on a digital network of the general using of the Russian Federation. [Metrologicheskoe obespechenie sistema taktovoy setevoy sinhronizacii na cifrovoy seti obshego pol’zovaniya RF] // Metrology and measuring equipment, 1999. – ?6. – pp.18-27.

4. RD 45.230.2001. Audit of system of clock network synchronization. [Audit sistemy taktovoy setevoy sinhronizacii]. Ministry of Communications of Russia. – I., 2001.

5. Recommendation R45.09-2001. Accession of networks of communications service providers to a base network of clock network synchronization. [Prisoedinenie setei operatorov svyazi k bazovoy seti taktovoy setevoy sinhronizacii]. Ministry of Communications of Russia. – I., 2001.

6. Recommendation ITU-O G. 811: Temporary characteristics of the primary reference generators suitable for pseudo-synchronous work of the international digital paths. [Vremennye harakteristiki pervichnyh etalonnyh generatorov, progodnyh dlya psevdosinhronnoy raboty mezhdunarodnyh cifrovyh traktov], 1998.

7. Recommendation ITU -O G. 812: Temporary characteristics of the conducted generators suitable for use as generators on knots of a network of synchronization. [Vremennye harakteristiki vedomyh generatorov, prigodnyh dlya ispol’zovaniya v kachestve generatorov na uzlah seti sinhronizacii], 2002.

8. Recommendation ITU -O G. 823. Management of trembling and phase wandering in the digital communication networks based on hierarchy of 2048 kbps. [Upravlenie drozhaniem I bluzhdaniem fazy v cifrovyh setyah svyazi, osnovannyh na ierarhii 2048 kbit/s], 2002.

9. ETSI EN 300 462-3-1. Transfer and multiplexing (TM); the General requirements to synchronization networks. Ch.3.1: Management of trembling and phase wandering in synchronization networks. [Peredacha I mul’tipleksirovanie (TM); Obshie trebovaniya k setyam sinhronizacii Ch.3.1: Upravlenie drozhaniem I bluzhdaniem fazy v setyah sinhronizacii], 2003.

10. ETSI EN 300 462-4-1. Transfer and multiplexing (TM); the General requirements to synchronization networks. ?.4.1: Temporary characteristics of setting generators for synchronization of equipment of synchronous digital hierarchy (STsI). [Peredacha I mul’tipleksirovanie (TM); Obshie trebovaniya k setyam sinhronizacii. Ch.4.1: Vremennye harakteristiki zadaushih generatorov dlya sinhronizacii apparatury sinhronnoy cifrovoy ierarhii (SCI) I pleziohronnoy cifriviy ierarhii (PCI)], 2002.

11. ETSI EN 300 462-6-1. Transfer and multiplexing (TM); the General requirements to synchronization networks. Ch.6.1: Temporary characteristics of primary reference generators. [Peredacha I mul’tipleksirovanie (TM); Obshie trebovaniya k setyam sinhronizacii. Ch.6.1: Vremennye harakteristiki pervichnyh etalonnyh generatorov], 1998.

12. ETSI EN 300 462-7-1. Transfer and multiplexing (TM); the General requirements to synchronization networks. Ch.7.1: Temporary characteristics of setting generators for synchronization of equipment of local communication centers. [Peredacha I mul’tipleksirovanie (TM); Obshie trebovaniya k setyam sinhronizacii. Ch7.1: Vremennye harakteristiki zadaushih generatorov dlya sinhronizacii apparatury mestnyh uzlov svyazi], 2001.

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Petrov E.P., Kharina N.L., Kononova V.Y.
Viatka state university, Russia,
EPPetrov@mail.ru, natal_res@mail.ru, KononovaVictoria@mail.ru

Nonlinear filtering of digital non-stationary random fields

Abstract
In this paper the problem of mathematical model constructing of virtual binary random field has been solved. Virtual binary random fields are formed at the delta modulator output in the remote sensing system. A virtual binary random field is represented by a random Markov field with variable statistical characteristics. The statistical characteristics are localized in adjacent image areas. They contain both areas with non-correlated elements (constant brightness areas) to areas with correlated elements (variable brightness areas). The basic mathematical model is a two-dimensional Markov chain with two states and some different matrixes of transition probabilities from one state to the next one. The developed mathematical model allows us to use the existing nonlinear filtering algorithms of multidimensional binary Markov fields distorted by a white Gaussian noise.

Keywords: nonlinear filtration, transfer of digital grayscale images, multidimensional processes.

References
1. Derin KH., Kelli P. Casual processes of markov type with discrete arguments. [Sluchainye protsessy markovskogo tipa s diskretnymi argumentami] // TIIER, 1989. – ?10. – Vol.77. – P.42.

2. Petrov Ye.P., I.S. Trubin. Mathematical models of video sequences of digital grayscale images. [Matematicheskie modeli vidyeoposledovatelnostyei tsifrovykh polutonovykh izobrazhenii] // Uspekhi sovremennoi radioelektroniki, 2007. – ?6. – pp.3-31.

3. Medvedeva Ye.V., Kharina N.L., Metelev A.P. Mathematical models of video images on the basis of Markov’s multidimensional chains. [Matematicheskie modeli vidyeoizobrazhenii na osnove mnogomernykh tsepyei Markova] // Sb. nauchn. trudov XIII Mezhdunar. konf. “Tsifrovaya obrabotka signalov i yee primenenie”. – M., 2011. – Vol.1. – pp.277-281.

4. Petrov Ye.P., Medvedeva Ye.V., Kharina N.L. Models and algorithms of image processing. [Modeli i algoritmy obrabotki izobrazhenii]. – VyatGU. – Kirov: O-Kratkoe, 2008. – 88 p.

5. Dyeikhin L.Ye. Methods of statistical processing of images and fields. [Metody statisticheskoi obrabotki izobrazhenii i polyei]. – Novosibirskii elektrotekhn. int, 1986. – pp.67-69.

6. Petrov Ye.P., Medvedeva Ye.V., Metelev A.P. Adaptive nonlinear filtration of statistically connected video sequences. [Adaptivnaya nelinyeinaya filtratsiya statisticheski svyazannykh vidyeoposledovatelnostyei] // T-Comm – Telekommunikatsii i transport, 2009. – ?5. – pp.18-21.

7. Petrov Ye.P., Chastikov A.V. Method of an adaptive filtration of the binary pulse correlated signals. [Metod adaptivnoi filtratsii dvoichnykh impulsnykh korrelirovannykh signalov] // Radiotekhnika i elektronika, 2001. – ?10. – Vol.46. – pp.1155-1158.

8. Petrov Ye.P., Trubin I.S., Medvedeva Ye.V., Chastikov I.A. Adaptive nonlinear filtration of strongly zashumlenny video sequences. [Adaptivnaya nelinyeinaya filtratsiya silno zashumlennykh vidyeoposledovatelnostyei] // Informatika, 2009. – ?2. – pp.49-56.

9. Rabiner L.R. The hidden Markovsky models and their application in the chosen appendices at speech recognition. [Skrytye Markovskie modeli i ikh primenenie v izbrannykh prilozheniyakh pri raspoznavanii rechi] // TIIER, 1989. – Vol.77. – ?2. – pp.86-120.

10. Petrov Ye.P., Trubin I.S., Tikhonov I.Ye. Nonlinear digital filtration of grayscale images. [Nelinyeinaya tsifrovaya filtratsiya polutonovykh izobrazhenii] // “Radiotekhnika”, 2003. – ?5. – pp.7-10.

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Kydryavcev A.A., Shestakov O.V.,
Lomonosov Moscow State University,
nubigena@hotmail.com, oshestakov@cs.msu.su

Asymptotic distribution of an assessment of risk of threshold processing of veyglet-factors of a signal at unknown noise level

Abstract
The assessment of function of the signal passed through the linear homogeneous converter in model with additive noise is studied. Asymptotic properties of an assessment of risk of procedure of threshold processing of factors veyglet-veyvlet signal decomposition in the assumption are investigated that dispersion of noise is unknown. Situations of an assessment of dispersion on an independent sample and on veyvlet-factors of a signal are considered. Conditions at which the risk assessment asymptoticly is normal are given.

Keywords: threshold processing, risk of an assessment of a signal, asymptotic normality, linear homogeneous transformation, steady basis.

References
1. Abramovich F., Silverman B.W. Wavelet Decomposition Approaches to Statistical Inverse Problems // Biometrika, 1998. Vol.85. ?1. pp.115–129.

2. Lee N. Wavelet-vaguelette decompositions and homogenous equations: PhD dissertation. Purdue University, 1997.

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9. Kydryavcev A.A., Shestakov I.V. Risk assessment asimptotic at veyglet-veyvlet decomposition of an observable signal. [Asimptotika ocenki riska pri veyglet-veyvlet razlozhenii nabludaemogo signala]. Asibmptotika ocenki riska pri veyglet-veyvlet razlozhenii nabludaemogo signala] // T-Comm – Telecommunikacii I transport, 2011. ?2. ??.54–57.

10. Markin A.V., Shestakov I.V. Risk assessment asimptotic at threshold processing veyvlet-veyglet factors in a problem of a tomography. [Asimptotika ocenki riska pri porogovoy obrabotke veyvglet-veyvlet koefficientov v zadache tomografii] // Informatoka I ee primenenie, 2010. Vol.4. Ch.2. pp.36-45.

11. Markin A.V., Shestakov I.V. About a solvency of an assessment of risk at threshold processing of veyvlet-factors. [O sostoyanetl’nosti ocenki riska pri porogovoy obrabotke veyvlet-koefficientov] // Messenger of the Moscow university. Vol.15. Calculus mathematics and cybernetics, 2010. ?1. ??.26–34.

12. Shestakov I.V. Approximation of distribution of an assessment of risk of threshold processing of veyvlet-factors by normal distribution when using selective dispersion. [Approksimaciya raspredeleniya ocenki riska porogovoy obrabotki veyvlet-koefficientov normal’nym raspredeleniem pri ispol’zovanii vyborochnoy dispersii] // Informatika i ee primeneniya, 2010. Vol.4. Ch.4. pp.73-81.

13. Markin A.V. Limiting distribution of an assessment of risk at threshold processing of veyvlet-factors. [Predel’noe raspredelenie ocenki riska pri porogovoy obrabotke veyvlet-koefficientov] // Informatika i ee primeneniya, 2009. Vol.3. Ch.4. pp.57-63.

14. Mallat S. A wavelet tour of signal processing. Academic Press, 1999.

15. Dobeshi I. Ten lectures on veivlet. [Desyzt’ lekciy po veivletam]. – Izhevsk: NIC «Regular and chaotic dynamics», 2001.

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

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20. Feller V. Introduction in probability theory and its appendices. [Vvedenie v teoriu veroyatnostey I ee prilozheniya]. I.: Mir, 1967. – Vol.1.

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Legkov K.E, Donchenko A.A.
Moscow technical university of communication and informatics (MTUCI), Russia
info@srd-mtuci.ru

Probability of loss of a package in wireless networks with casual multiple access to the transfer environment

Abstract
In networks where not all knots are in a zone of radio visibility of each other, losses of packages Because of work of the MAC protocol can arise under various conditions. Four various categories of losses of a package because of the MAC protocol work which analysis is presented in the present article differ.

Keywords: the MAS protocol, losses of packages, wireless local networks, networks with casual multiple access.

References
1. Tobagi F.A. Modeling and the analysis of characteristics of multiflying package radio networks Application of methods of switching of packages in tactical radio networks. [Modelirovanie I analiz harakteristik mnogoproletnyh paketnyh radiosetey. Primenenie metodov kommutacii paketov v takticheskih radiosetyah]. – TIIAR. – Vol.75. -?1, 1987. – pp.162-185.

2. Vishnevsky V.M, Lyahov A.I., Portnoy S.L, Shahnovich I.V. Broadband wireless networks of information transfer. [Shirikopolosnye besprovodnye seti peredachi informacii]. – I: Technosfera, 2005.

3. Bianchi G. Performance Analysis of IEEE 802.11 Distributed Coordination Function// IEEE Journal on Selected Areas in Communications 18(3) (March 2000). – pp.535-547.

4. M. Garetto, T. Salonidis, and E. Knightly. Modeling Per-flow Throughput and Capturing Starvation In CSMA Multi-hop Wireless Networks. In Proc. IEEE INFOCOM, Barcelona, Spain, 2006.

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Kolesnikov A.
Kolesnikov A.
aleksander.kolesnikov@gmail.com

Combined Channel Estimation and Demodulation Algorithm for MIMO OFDM Communication Systems

Abstract
OFDM communication systems commonly use channel estimation techniques based on transmitting known signals in between information signals in time-frequency domain. That known signals are called pilot-signals. Author has already developed and published combined channel estimation and demodulation algorithm. To improve performance of channel estimation, it uses both pilot-signals and the estimates of information symbols, calculated at the previous iteration. The issue of the channel estimation in multiple antenna systems (MIMO) is that there is a correlation between signals transmitted over different antennas. In order to achieve desired performance, that correlation should be considered in the estimation algorithm. In this paper author generalised combined channel estimation and demodulation algorithm, previously published, onto MIMO case. The simulation results show its high estimation efficiency. The algorithm also has acceptable computation complexity against traditional approaches.

Keywords: orthogonal frequency multiplexing (OFDM), MIMO, filtration of parameters of a communication channel.

References
1. Prokis Dzh. Digital communication. [Tsifrovaya svyaz]. – M.: Radio i svyaz, 2000.

2. Sklyar B. Digital communication. Theoretical bases and practical application. [Tsifrovaya svyaz. Tyeoreticheskie osnovy i prakticheskoe primenenie]. – M.: ID “Vilyams”, 2003.

3. Yarlykov M.S. Application of the markov theory of a nonlinear filtration in the radio technician. [Primenenie markovskoi tyeorii nelinyeinoi filtratsii v radiotekhnike]. – M.: Sov. radio, 1980.

4. Tikhonov V.I., Kharisov V.N. Statistical analysis and synthesis of radio engineering devices and systems. [Statisticheskii analiz i sintez radiotekhnicheskikh ustroistv i system]. – M.: Radio i svyaz, 1991.

5. Ramjee Prasad. OFDM for wireless communications systems. Boston, Artech House, 2004, 272 p.

6. Tikhvinskii V.O., Terentev S.V., Yurchuk A.B. Networks of mobile communication of LTE: technologies and architecture. [Seti mobilnoi svyazi LTE: tekhnologii i arkhitektura]. – M.: Eko-Trendz, 2010. – 284 p.

7. Syeidzh Dzh., Mel·EE. The theory of estimation and its application in communication and management. [Tyeoriyaotsenivaniyaiyee primenenie v svyazi iupravlenii]. – M.: Svyaz, 1976.

8. Brammer K., Ziffling G. Filter of Kalman-byyusi: the determined supervision and a stochastic filtration. [Filtr Kalmana Byusi: determinirovannoe nablyudenie i stokhasticheskayafiltratsiya]. – M.: Nauka, 1982.

9. Tikhonov V.I. Statistical radio engineering. [Statisticheskaya radiotekhnika]. – M.: Radio i svyaz, 1982.

10. Kryeindelin V.B. Soft demodulation of signals from the multiitem it is peak phase modulations. [Myagkaya demodulyatsiya signalov s mnogopozitsionnoi amplitudno-fazovoi modulyatsii] // V sb. nauchnykh trudov uchebnykh zavedenii svyazi”, ?173, Sankt-Peterburg, 2005;

11. Kryeindelin V.B., Kolesnikov A.V. Iterative algorithm of joint demodulation and filtration of parameters of a communication channel in communication systems with orthogonal frequency multiplexing (OFDM). [Iteratsionnyi algoritm sovmestnoi demodulyatsii i filtratsii parametrov kanala svyazi v sistemakh svyazi s ortogonalnym chastotnym multipleksirovaniem (OFDM)] / Tsifrovaya obrabotka signalov. – ?2, 2009. – pp.12-16.

12. ITUITU -RM.1225,”Guidelinesforevaluations of radio transmission technologies for IMT-2000,”1997.

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Sobolev A.A., Saltykov A.R.,
The St. Petersburg state university of prof. M.A.Bonch-Bruevich

Data Throughput and Coverage Modelling for LTE Mobile Network Base StationInvestigation of Modulator-Demodulator of Multicarrier M-ary Orthogonal Chaotic Spread Spectrum Signals for 3rd Generation and 4th Generation Mobile Networking Using Computer Simulations

Abstract
In this article the method of generation and reception of novel Multi-carrier M-ary Orthogonal Chaotic Spread Spectrum Signals (MC-MO-CSSS) with size of signal ensemble from 2 up to 256 and more is supposed. The analysis of some MC-MO-CSSS properties is carried out. The structure of MC-MO-CSSS modulator-demodulator which can be used for deployment of both 3rd Generation and 4th Generation communication systems is offered. Results of computer simulations for MC-MO-CSSS are shown. Adequacy of computer model is proven by means of comparison of the reception noise immunity estimation against white Gaussian noise for the channel with the constant parameters. The new approach of comb noise rejection at MC-MO-CSSS reception has been approved. Results of the reception noise immunity estimation which have been given for a various number of affected MC-MO-CSSS subcarriers confirm efficiency of the offered approach.
Key words: Multicarrier M-ary Orthogonal Chaotic Spread Spectrum Signals, modulator-demodulator, multiple access, noise immunity, computer simulation,
channel with constant parameters, comb noise.

Keywords: orthogonal multifrequency chaotic broadband signals, the modulator demodulator, multiple access, a noise stability, modeling on the personal computer, the channel with constant parameters, a hindrance of edge type.

References
1. Hara S., Prasad R. Multicarrier Techniques for 4G Mobile Communications. Artech House, 2003.

2. Babkov V.Yu., Voznyuk I.A., Nikitin A.N., Sivers I.A. Communication systems with code division of channels. [Sistemy svyazi s kodovym razdeleniem kanalov]. – Spb.: Spbgut, 1999. – 120 p.

3. Prokis Dzh. Digital communication. [Cifrovaya svyaz’]. – I.: Radio I svyaz’, 2000.

4. Bortnikov V.V. Noise stability of the quasicoherent carried reception of markovsky signals in digital communication lines. [Pomehoustoichivost’ kvazikogerentnogo raznesennogo priema markovskih signalov v cifrovyh liniyah svyazi]. – I.: Radiotehnika / Izvestiya vuzov. Vol.28, 1985. ?4. – pp.72-74.

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6. Kislov V.Ya., Kislov V.V. New class of signals for information transfer. Broadband chaotic signals. [Novyi klass signalov dlya peredachi informacii. Shirokopolosnye haoticheskie signaly]. – I.: Radiotehnika i elektronika. – Vol.42, ?8, 1997. – pp.962-973.

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Lunyashin I.V.,
Moscow technical university of communication and informatics (MTUCI), Russia
ilyalunyashin@yahoo.com

The analysis of occupancy of technical resources during distance educational process

Abstract
The research and analysis of occupancy factors and workload of network technical resources, which are among the main parameters of distance e-learning, are considered in the article. Because of needs of preliminary scheduling of network traffic due to amount of transmitting data and resource-intensiveness of line-operated channels, examination and creation of math models, which determines the way of dynamic distance educational process organization, should be done. Statistics data about most important characteristics of network like: delay, jitter, loss of packets, parameters of nodes are the results of simulation model.

Keywords: data throughput, network traffic, distances educational process, simulation modeling, resource-requirements, video- audio conference.

References
1. Kalashnikov V.V. Modeling organization of difficult systems. [Organizaciya modelirovaniya slozhnyh sistem]. — I.: Vysshaya shkola, 1982.

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8. Petrov V. Information systems. [Informacionnye sistemy]: The textbook for higher education institutions. -2-a izd. // Piter, 2005.