Increasing the noise immunity of communication systems under conditions of pulsed quasi-harmonic interference using blind signal processing methods


DOI: 10.34759/trd-2023-128-13

Аuthors

Tyapkin P. S.*, Vazhenin N. A.**

Moscow Aviation Institute (National Research University), 4, Volokolamskoe shosse, Moscow, А-80, GSP-3, 125993, Russia

*e-mail: tjapkinp@yandex.ru
**e-mail: N.Vazhenin@mai.ru

Abstract

In this paper, to compensate impulse noise, we consider the use of blind signal separation methods. Blind methods are based on high-order statistics and operate under multichannel reception conditions. Currently, this direction is being actively researched and implemented in medicine and image processing. A simulation model of a digital radio information transmission system was developed. The purpose of simulation modeling was to study blind signal separation algorithms to compensate for impulse noise, as well as to analyze the effect of Gaussian noise on the quality of blind separation. Blind source separation algorithms have some limitations, such as: number of received sources must be more than number of signals, which must be statistically independent between themselves. As a result of simulation modeling, it was revealed that the use of blind signal processing methods in the fight against pulsed quasi-harmonic interference makes it possible to achieve an energy gain depending on the duty cycle of the pulsed interference, the normalized interference frequency detuning relative to the bandwidth, the bit signal-to-noise ratio and the interference-signal ratio. For example, when demodulating a BPSK signal in a mixture with pulsed quasi-harmonic noise, a duty cycle of 0.005 and an interference-to-signal ratio of 15 dB, the use of the SOBI (Second-order blind identification) blind source separation algorithm makes it possible to achieve an energy gain with respect to mixture demodulation without blind separation at bit signal-to-noise ratio of 8.1 dB or more. So, for example, with the same simulation parameters, with a bit signal-to-noise ratio of 13 dB, a gain in bit error probability is achieved by more than в 2‧103 times.

Keywords:

blind signal processing, simulation modeling, improvement of radio channel noise immunity, impulse noise

References

  1. Yu Xianchuan, Dan Hu, and Jindong Xu. Blind source separation: theory and applications, John Wiley & Sons, 2013, 386 p.
  2. Ramli Dzati Athiar, Yeoh Hong Shiong and Norsalina Hassan. Blind source separation (BSS) of mixed maternal and fetal electrocardiogram (ECG) signal: A comparative study, Procedia Computer Science, 2020, vol. 176, pp. 582-591. DOI:10.1016/j.procs.2020.08.060
  3. Jutten C., Hérault J. Une solution neuromimétique au problème de séparation de sources, Traitement du signal, 1988, no. 6 (5), pp. 389-403.
  4. Belouchrani A., Amin M.G. Blind source separation based on time-frequency signal representations, IEEE transactions on signal processing, 1998, no. 11 (46), pp. 2888-2897.
  5. Belouchrani A. et al. A blind source separation technique using second-order statistics, IEEE Transactions on signal processing, 1997, no. 2 (45), pp. 434-444.
  6. Comon P. Independent component analysis, a new concept? Signal processing, 1994, no. 3 (36), pp. 287-314.
  7. Comon P. Independent Component Analysis, Elsevier, 1992, 11 p.
  8. Comon P., Jutten C. (ed.). Handbook of Blind Source Separation: Independent component analysis and applications, Academic press, 2010.
  9. Cichocki A., Amari S.I. Adaptive Blind Signal and Image Processing, England, John Wiley & Sons, 2000, 587 p.
  10. Amari S., Cichocki A. Complex Blind Source Extraction From Noisy Mixtures Using Second-Order Statistics, IEEE Transactions on Circuits and Systems, July 2010, pp. 1404-1414.
  11. Duarte L.T., Suyama R., Rivet B. Blind Compensation of Nonlinear Distortions: Application to Source Separation of Post-Nonlinear Mixtures, IEEE Transactions on Signal Processing, 2012, pp. 5832-5844. DOI:10.1109/TSP.2012.2208953
  12. Hyvärinen A., Oja E. A fast fixed-point algorithm for independent component analysis, Neural Computation, 1997, no. 9, pp. 1483-1492. DOI:10.1162/neco.1997.9.7.1483
  13. Hyvärinen A., Karhunen J., Oja E. Independent Component Analysis, John Wiley, 2001, 505 p.
  14. Belouchrani A., Moulines E., Abed-Meraim K. Second order blind separation of temporally correlated sources, Proceeding of the International Conference on Digital Signal Processing, 1993.
  15. Cardoso J.F., Soulomiac A. Blind beamforming for non-Gaussian signals, IEE Proceedings-F, 1993, no. 6 (140), pp. 362-370. DOI:10.1049/ip-f-2.1993.0054
  16. Shi X. Blind Signal Processing, Shanghai, Shanghai JiaoTong University Press, 2011, 368 p.
  17. Goryachkin O.V. Metody slepoi obrabotki signalov i ikh prilozheniya v radiotekhnike i svyazi (Blind Signal Processing Methods and Their Applications in Radio Engineering and Communications), Moscow, Radio i svyaz’, 2004, 230 p.
  18. Manokhin A.E. Metody slepogo podavleniya pomekh pri obrabotke poleznyh signalov (Methods of blind interference suppression in processing useful signals), Ekaterinburg, Izd-vo Ural’skogo universiteta, 2021, 204 p.
  19. Tyapkin P.S., Vazhenin N.A. Applying Blind Signal Processing Methods in Data-Transmission Systems, 2021 Systems of Signals Generating and Processing in the field of on Board Communications, IEEE, 2021. DOI:10.1109/IEEECONF51389.2021.9416105
  20. Brodskii M.S., Zvonarev V.V., Khubbiev R.V., Sherstyuk A.V. Trudy MAI, 2022, no. 127. URL: https://trudymai.ru/eng/published.php?ID=170340. DOI: 10.34759/trd-2022-127-10
  21. Serkin F.B., Vazhenin N.A. Veitsel’ A.V. Trudy MAI, 2022, no. 86. URL: https://trudymai.ru/eng/published.php?ID=67824
  22. Gusev S.N. Miklin D.V. Moroz A.V. Sakhno I.V. Sherstyuk A.V. Trudy MAI, 2020, no. 113. URL: https://trudymai.ru/eng/published.php?ID=118128. DOI: 10.34759/trd-2020-113-08

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