Method of separation in time of the harmonic components of wideband signals. Abstract.

Abstract. By definition, wideband refers to the communication in which messages satisfy the following condition: FT >> 1, where T - duration of the message the F - band occupied by the message. The variety of applications and wideband capabilities determines the specificity of the different methods of implementation. Wideband technologies are more than sixty years of history, the first development (the second half of the thirties of the XX century) relates to systems for military use. Today, wideband systems are used in virtually all areas of information communication.
This work is devoted to posts recognition method in a wideband wireless systems, the proposed method realizes the separation in time of the autocorrelation of the harmonic components of wideband signals. The introduction includes links to a series of publications devoted to the issues of search and processing of signals received on the basis of a special form of autocorrelation functions. The work is a continuation of these publications, and together they form a complete methodology for the construction of wideband systems using signals obtained on the basis of matrices with the autocorrelation function of a special form with complex values of the elements. The water of this article set out the aims and objectives of the study. Special attention is paid to the formulation of research problems and possible alternative ways of solving the problem. In the next part of the paper directly to the method of separation in time of the autocorrelation of the harmonic components of wideband signals: a functional block diagram of an implementation of the method, described in detail and justified sequence of transformations. Next, we consider some of the results obtained in the practice of the method, a comparison with possible alternative solutions. At the end of the work set forth the results, noted the advantages of this method, in particular, it is shown that the proposed method can effectively divide the autocorrelation components recognizable messages, with the recognition it is possible to use the entire time-frequency band communication, and thus take advantage of the signals generated in the matrix basis with autocorrelation the function of a special form with complex values of the elements.

Keywords: code division signals, radio communications, digital signal processing, autocorrelation function.

1. Ipatov V. P. Shirokopolosnye sistemy i kodovoe razdelenie signalov. Principy i prilojeniya. [Spread Spectrum and CDMA: Principles and Applications]. Moscow: Technosphere, 2007. 488 p. (In Russian)

2. Grishentcev A. Y., Korobeynikov A. G. The search algorithm, some properties and applications of matrices with complex values of the elements for steganography and synthesis of wideband signals, Zhurnal Radioelektroniki - Journal of Radio Electronics, 2016, ą 5, Available at: http://jre.cplire.ru/jre/may16/11/text.pdf (In Russian)

3. Grishentcev A. Y., Korobeynikov A. G. The reduction of the dimensionality of the space of correlation and convolution of digital signals. Izvestiya vysshikh uchebnykh zavedeniy. Priborostroenie. 2016. Vol. 59, No. 3. P. 211—218. (In Russian) DOI 10.17586/0021-3454-2016-59-3-211-218

4. Grishentcev A. Y., Korobeynikov A. G., Velichko E. N., Nepomnyachaya H. K., Rozow S. V. Binary matrix synthesis for broadband communication signal shaping // Radiotekhnika, 2015, No. 9, P.: 51– 58. (In Russian)

5. Dyatlov A. P., Kulbikayan B. H. Korreljacionnaja obrabotka shirokopolosnyh signalov v avtomatizirovannyh kompleksah radiomonitoringa. [Correlation processing of wideband signals in automated radio monitoring complexes]. Moscow: Goryachaya liniya-Telecom, 2014. – 332 p. (In Russian)

6. Arslan H., Chen Zhi N., Bendetto M. Sverhshirokopolosnaja besprovodnaja svjaz'. [Ultra-Wideband Wireless Communications and Networks]. Moscow: Technosphere, 2012 – 640 p. (In Russian)

7. Yamanov D. N., Javoronkov C. C. Dynamic polarization division multiplexing. Scientific bulletin of the Moscow state technical university of civil aviation, 2006, No. 98(2), P.: 13 – 17. (In Russian)

8. Rodimov A. P., Popovskiy V. V. Statisticheskaja teorija poljarizacionno-vremennoj obrabotki signalov i pomeh. [Statistical theory of polarization-temporal processing of signals and noise]. Moscow: Radio and communication, 1984. – 272 p.

9. Bobkov V., Efimov M., Kiselev A. Polarizing seal – prospects of introducing the Use of polarization separation of signals in satellite communication systems Russia // Connect, No. 4 (2004), P.: 120 – 123. Available at: http://www.connect.ru/article.asp?id=4547 (In Russian)

10. Gerasimov I. V., Safyannikov N. M., Yakimovskiy D. O. Slozhno-funkcional'nye bloki smeshannyh sistem na kristalle: avtomatizacija funkcional'nogo proektirovanija: monografija. [Complex-function blocks of the mixed systems on a chip: automation of functional design: Monograph]. Saint-Petersburg: «ELMOR», 2012. – 237 p. (In Russian)

11. Solonina A. I., Klionskiy D. M., Merkucheva T. V., Petrov S. N. Cifrovaja obrabotka signalov i MatLab: uchebnoe posobie. [Digital signal processing and MatLab: a tutorial]. Saint-Petersburg: BHV- Petersburg, 2014. – 512 p. (In Russian)

12. Grishentcev A. Y., Korobeynikov A. G. Metody i modeli cifrovoj obrabotki izobrazhenij: monografija. [The models and methods of digital image processing]. Saint-Petersburg: Peter the Great St.Petersburg Polytechnic University, 2014. 190 p. (In Russian)