DOI: https://doi.org/10.15588/1607-3274-2020-4-1

THE FORMATION METHOD OF COMPLEX SIGNALS ENSEMBLES BY FREQUENCY FILTRATION OF PSEUDO-RANDOM SEQUENCES WITH LOW INTERACTION IN THE TIME DOMAIN

S. V. Indyk, V. P. Lysechko, O. S. Zhuchenko, V. S. Kitov

Abstract


Context. The problem of forming complex signal ensembles on the basis of frequency band filtering and research of their properties is considered. The object of research is the process of synthesis of signal ensembles based on frequency filtering of pseudo-random sequences of short video pulses with low interaction in the time domain.

Objective. It is to form complex signal ensembles with satisfactory values of intercorrelation properties, which are close to the signals with minimal energy interaction. 

Method. The results of the application of forming complex signal ensembles method by frequency filtering of pseudo-random sequences with low interaction in the time domain are presented. As a result of the spectral band selection of the studied pseudorandom short video pulse sequences due to the use of bandpass filters based on the Chebyshev filter of the first kind, new samples of sequences with spectrum restriction are obtained. By applying intercorrelation analysis to the obtained sequence samples, the values of the maximum emissions of the side lobes of the cross-correlation functions (CCF) for all possible signal pairs are estimated. If the values of the maximum emissions of the side lobes of the CCF signals exceed the limit values, the sequence of the analyzed pair with a smaller value of the number of pulses is removed from the ensemble. In case of satisfactory value – the received signals are accepted for the signal ensemble formation with the minimum power interaction. Thus, a new set of values of the maximum emissions of the side lobes of the CCF is formed. This approach increases the number of signals in ensembles with satisfactory values of statistical characteristics with limited signal spectrum width, and the correlation properties of such sequences approach the signals with minimal energy interaction, which reduces the level of multiple access interference. As a result, complex signal ensembles obtained by frequency filtering should be used in cognitive radio systems with code division multiplexing.

Results. Based on the software implementation of the method of forming complex signal ensembles by frequency filtering of pseudo-random sequences with low interaction in the time domain, signals with satisfactory values of statistical characteristics with limited signal spectrum width with intercorrelation properties close to signals with minimal energy interaction and higher ensemble volume were selected.

Conclusions. The application of frequency filtering to pseudo-random sequences of short video pulses with a low level of crosscorrelation allows to obtain complex signal ensembles, which will be similar in correlation properties to sequences with minimal energy interaction. It will reduce the level of multiple access interference. The analysis revealed that the use of frequency filtering of sequences will slightly worsen the mutual correlation properties of signals, possibly due to suboptimal synthesis of values of maximum emission levels of side lobes of CCF signals, but, nevertheless, it is possible to use such signals in modern cognitive systems radio access multiple access with code division multiplexing. 


Keywords


Complex signal, cross-correlation function, multiple access interference, videopulse, signal base, signal spectrum width, signal duration, duty cycle, impulse duration, minimal energy interaction.

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References


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GOST Style Citations


1. Ipatov V. P. Spread spectrum and CDMA: Principles and applications / V. P. Ipatov. – Chichester : John Wiley & Sons, 2005. – 385 p. DOI10.10020470091800.

2. Pandit Sh. Spectrum sharing in cognitive radio networks / Sh. Pandit, G. Singh. – Solan : Springer, 2017. – 426 p. DOI:10.1007/9783319531472.

3. Varakin L. E. Communication systems with noise-like signals / L. E. Varakin – M. : Radio and communication, 1985. – 384 p.

4. Indyk S. Method of permutation of intervals, taking into accountant correlation properties of segments / S. Indyk, V. Lysechko // Control, navigation and communication system. – 2020. – Issue 3 (61). – P. 128–130. DOI:10.26906/SUNZ.2020.3.

5. Indyk S. The study of ensemble properties of complex signals obtained by time interval permutation / S. Indyk, V. Lysechko // Advanced Information Systems. – 2020. – Vol. 4, № 3. – P. 85–88. DOI: 10.20998/25229052.2020.3.11.

6. Iacobucci M. S. Reconfigurable radio systems: network architectures and standards / M. S. Iacobucci. – Chichester : John Wiley & Sons, 2013. – 275 p. DOI:10.1002/9781118398401.

7. Cognitive radio communications and networking / [R. C. Qiu, Zh. Hu, H. Li, M. C. Wicks] – Chichester : John Wiley & Sons, 2012. – 514 p. DOI:10.1002/9781118376270.

8. Cameron R. J. Microwave filters for communication systems: fundamentals, design, and applications / R. J. Cameron, C. M. Kudsia, R. R. Mansour. – New York : Wiley & Sons, 2007. – 771 p. DOI:10.1002/9781119292371.

9. Palicot J. Radio engineering: from software to cognitive radio / J. Palicot, Pierre Noël Favennec. – Hoboken : John Wiley & Sons, 2011. – 378 p. DOI:10.1002/9781118602218.

10. Ghasemi A. Spectrum sensing in cognitive radio networks: Requirements, challenges, and design trade-off / A. Ghasemi, S. E. Sousa // IEEE Communications Magazine. – 2008. – Vol. 46. – P. 32–39.

11. Setoodeh P. Fundamentals of cognitive radio / P. Setoodeh, S. Haykin. – Hoboken : John Wiley & Sons, 2017. – 207 p. DOI:10.1002/9781119405818.

12. Arslan H. Cognitive radio, software defined radio and adaptive wireless systems / H. Arslan. – Dordrecht : Springer, 2007. – 453 p. DOI:10.10079781402055423. 







Copyright (c) 2020 S. V. Indyk, V. P. Lysechko, O. S. Zhuchenko, V. S. – Kitov

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