maisМоделирование и анализ информационных системModeling and Analysis of Information Systems1818-10152313-5417Yaroslavl State University10.18255/1818-1015-2021-4-452-461mais-1570Research ArticleComputer System OrganizationАлгоритм оценивания частоты сигнала на выходе канала с управляемым информационным потоком в условиях фазового шумаAn Algorithm for Estimating the Signal Frequency at the Output of a Channel with a Controlled Information Flow under Phase Noise Conditionshttps://orcid.org/0000-0002-9348-4440КазаковЛеонид НиколаевичKazakovLeonid Nikolaevichkazakov@uniyar.ac.ruhttps://orcid.org/0000-0003-1796-0190КубышкинЕвгений ПавловичKubyshkinEvgenii Pavlovichkubysh.e@yandex.ruhttps://orcid.org/0000-0002-1751-8557ЛукьяновИлья ВикторовичLukyanovIlya Victorovichil-lukyanov@yandex.ruЯрославский государственный университет им. П. Г. ДемидоваРоссияP. G. Demidov Yaroslavl State UniversityRussian FederationООО «НПП «ЛАМА»РоссияOOO “NPP “LAMA” ”Russian Federation202118122021284452461Copyright © Казаков Л.Н., Кубышкин Е.П., Лукьянов И.В., 20212021Казаков Л.Н., Кубышкин Е.П., Лукьянов И.В.Kazakov L.N., Kubyshkin E.P., Lukyanov I.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.mais-journal.ru/jour/article/view/1570

Большой интерес вызывают исследования в области эффективных алгоритмов оценивания частоты. Причиной этому является перераспределение во многих современных радиотехнических приложениях роли аддитивного и фазового шумов. Примером может служить область измерительных радиоприборов, работающих, как правило, при высоких отношениях сигнал/шум (ОСШ). Ошибка оценки в большей степени определяется не широкополосным шумом, а частотными и фазовыми шумами гетеродинов приемных и передающих устройств. В частности, в более ранних работах [1] предложен эффективный вычислительный алгоритм оценивания частоты квазигармонического сигнала, основанный на итерационном вычислении АКП. В [2] этот алгоритм доработан и показана его близость к границе Рао-Крамера (источниками этих шумов являются задающие генераторы и синтезаторы частоты). Возможности оценивания частоты в радиоканалах позволяют существенно расширить функционал всей радиосети. Сюда можно отнести, например, задачу адаптивного распределения информационных потоков радиосети. Сюда же можно отнести задачи синхронизации и когерентной обработки сигналов. По этим причинам необходимы дополнительные исследования этого алгоритма, расчет теоретических границ и их сравнение с результатами моделирования.

Research in the field of efficient frequency estimation algorithms is of great interest. The reason for this is the redistribution of the role of additive and phase noise in many modern radio-engineering applications. An example is the area of measuring radio devices, which usually operate at high signal-to-noise ratios (SNR). The estimation error is largely determined not by the broadband noise, but by the frequency and phase noise of the local oscillators of the receiving and transmitting devices. In particular, earlier works \\cite{Nikiforov} proposed an efficient computational algorithm for estimating the frequency of a quasi-harmonic signal based on the iterative calculation of the autocorrelation sequence (ACS). In \\cite{Volkov}, this algorithm was improved and its proximity to the Rao-Cramer boundary was shown (the sources of this noise are master oscillators and frequency synthesizers). Possibilities of frequency estimation in radio channels make it possible to significantly expand the functionality of the entire radio network. This can include, for example, the problem of adaptive distribution of information flows of a radio network. This also includes the tasks of synchronization and coherent signal processing. For these reasons, more research is needed on this algorithm, the calculation of theoretical boundaries and their comparison with the simulation results.

граница Рао-Крамераавтокорреляционная функциячастотная нестабильностьфазовый шуминформация ФишераRao-Cramer boundaryautocorrelation functionfrequency instabilityphase noiseFisher informationReferencesA. A. Nikiforov, “Identification and assessment of information parameters of navigation systems with code division,” 2014.A. A. Nikiforov, “Identification and assessment of information parameters of navigation systems with code division,” 2014.V. G. Volkov, Y. N. Krivov, and I. V. Lukyanov, “Improved frequency estimation algorithm based on iterative computation of autocorrelation sequence,” Journal of Radio Electronics, no. 10, 2016, Accessed: 01.22.2017. [Online]. Available: http://jre.cplire.ru/jre/oct16/6/text.html.V. G. Volkov, Y. N. Krivov, and I. V. Lukyanov, “Improved frequency estimation algorithm based on iterative computation of autocorrelation sequence,” Journal of Radio Electronics, no. 10, 2016, Accessed: 01.22.2017. [Online]. Available: http://jre.cplire.ru/jre/oct16/6/text.html.A. V. Ryzhkov and V. N. Popov, Frequency synthesizers in radio communication technique. Moscow: Radio and Communication, 1991, p. 264.A. V. Ryzhkov and V. N. Popov, Frequency synthesizers in radio communication technique. Moscow: Radio and Communication, 1991, p. 264.W. J. Riley, Handbook of Frequency Stability Analysis. USA - Washington: National Institute of Standards and Technology, 2008.W. J. Riley, Handbook of Frequency Stability Analysis. USA - Washington: National Institute of Standards and Technology, 2008.D. N. Swingler, “Approximations to the Cramer-Rao lower bound on frequency estimates for complex sinusoids in the presence of sampling jitter,” Signal Processing, vol. 48, no. 1, pp. 77-83, 1996.D. N. Swingler, “Approximations to the Cramer-Rao lower bound on frequency estimates for complex sinusoids in the presence of sampling jitter,” Signal Processing, vol. 48, no. 1, pp. 77-83, 1996.S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory (v.1). NJ: Prentice Hall, Upper Sadle River, 1998, p. 595.S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory (v.1). NJ: Prentice Hall, Upper Sadle River, 1998, p. 595.J. A. Barnes and others, “Characterization of frequency stability,” in IEEE Transactions on Instrumentation and Measurement, 1971, vol. IM-20, no. 2, pp. 105-120.J. A. Barnes and others, “Characterization of frequency stability,” in IEEE Transactions on Instrumentation and Measurement, 1971, vol. IM-20, no. 2, pp. 105-120.A. Tretter, “Estimating the Frequency of a Noisy Sinusoid by Linear Regression,” IEEE Transactions on Information theory, vol. 31, no. 6, pp. 832-835, 1985.A. Tretter, “Estimating the Frequency of a Noisy Sinusoid by Linear Regression,” IEEE Transactions on Information theory, vol. 31, no. 6, pp. 832-835, 1985.S. M. Kay, “A Fast and Accurate Single Frequency Estimator,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 37, no. 12, pp. 1987-1990, 1989.S. M. Kay, “A Fast and Accurate Single Frequency Estimator,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 37, no. 12, pp. 1987-1990, 1989.Y. V. Kryukov, E. V. Rogozhnikov, and D. A. Pokamestov, “Phase noise model taking into account the spectral mask of frequency synthesizers and signal generators,” Bulletin of the Tomsk Polytechnic University. Information Technology, vol. 325, no. 5, pp. 45-51, 2014.Y. V. Kryukov, E. V. Rogozhnikov, and D. A. Pokamestov, “Phase noise model taking into account the spectral mask of frequency synthesizers and signal generators,” Bulletin of the Tomsk Polytechnic University. Information Technology, vol. 325, no. 5, pp. 45-51, 2014.J. A. Barnes, “Atomic Timekeeping and the Statistics of Precision Signal Generators,” Proceeding of the IEEE, vol. 54, no. 2, pp. 207-220, 1966.J. A. Barnes, “Atomic Timekeeping and the Statistics of Precision Signal Generators,” Proceeding of the IEEE, vol. 54, no. 2, pp. 207-220, 1966.

The authors declare that there are no conflicts of interest present.