Improving the Efficiency of UAV Communication Channels in the Context of Electronic Warfare

Authors

  • Serhii Semendiai Chernihiv Polytechnic National University
  • Yuliіa Tkach Chernihiv Polytechnic National University
  • Mykhailo Shelest Chernihiv Polytechnic National University
  • Oleksandr Korchenko National Aviation University
  • Ruslana Ziubina University of Bielsko-Biala
  • Olga Veselska University of Bielsko-Biala

Abstract

The article is devoted to the development of a method for increasing the efficiency of communication channels of unmanned aerial vehicles (UAVs) in the conditions of electronic warfare (EW). The author analyses the threats that may be caused by the use of electronic warfare against autonomous UAVs. A review of some technologies that can be used to create original algorithms for countering electronic warfare and increasing the autonomy of UAVs on the battlefield is carried out. The structure of modern digital communication systems is considered. The requirements of unmanned aerial vehicle manufacturers for onboard electronic equipment are analyzed, and the choice of the hardware platform of the target radio system is justified. The main idea and novelty of the proposed method are highlighted. The creation of a model of a cognitive radio channel for UAVs is considered step by step. The main steps of modeling the spectral activity of electronic warfare equipment are proposed. The main criteria for choosing a free spectral range are determined. The type of neural network for use in the target cognitive radio system is substantiated. The idea of applying adaptive coding in UAV communication channels using multicomponent turbo codes in combination with neural networks, which are simultaneously used for cognitive radio, has been further developed.

References

J. Chu, “Digital Communication Systems Engineering With Software-Defined Radio (Pu, D. and Wyglinski, A.M.; 2013) [Book/Software Reviews],” IEEE Microwave Magazine, vol. 16, no. 5, pp. 110–111, Jun. 2015, https://doi.org/10.1109/mmm.2015.2410612.

J. S.Banerjee and K. Karmakar, “A Comparative study on Cognitive Radio Implementation Issues,” International Journal of Computer Applications, vol. 45, no. 15, pp. 44–51, May 2012, https://doi.org/10.5120/6858-9477.

USRP B200mini Series. Product Overview. https://www.ettus.com/wp-content/uploads/2019/01/USRP_B200mini_Data_Sheet-1.pdf

S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201–220, Feb. 2005, https://doi.org/10.1109/jsac.2004.839380.

Valieva, I. (2020). Spectrum Sensing for Dynamic Spectrum Access in Cognitive Radio. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-52881.

N. Morozs, T. Clarke, and D. Grace, “Heuristically Accelerated Reinforcement Learning for Dynamic Secondary Spectrum Sharing,” IEEE Access, vol. 3, pp. 2771–2783, 2015, https://doi.org/10.1109/access.2015.2507158.

W. Jiang and H. D. Schotten, “Recurrent Neural Networks with Long Short-Term Memory for Fading Channel Prediction,” 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), May 2020, https://doi.org/10.1109/vtc2020-spring48590.2020.9128426.

Software Defined Cognitive Radio using Matlab. https://www.scribd.com/doc/103610191/Cognitive-Radio.

Usage Manual - GNU Radio. GNU Radio. https://wiki.gnuradio.org/index.php?title=Usage_Manual.

M. Rathika, P. Sivakumar, K. Ramash Kumar, and I. Garip, “Cooperative Communications Based on Deep Learning Using a Recurrent Neural Network in Wireless Communication Networks,” Mathematical Problems in Engineering, vol. 2022, pp. 1–12, Dec. 2022, https://doi.org/10.1155/2022/1864290.

J. SukhPaulSingh, J. Singh, and A. S. Kang, “Cognitive Radio: State of Research Domain in Next Generation Wireless Networks - A Critical Analysis,” International Journal of Computer Applications, vol. 74, no. 10, pp. 1–9, Jul. 2013, https://doi.org/10.5120/12918-9741.

Downloads

Published

2023-10-28

Issue

Section

Security, Safety, Military