ISSN: A/F
JANOLI International Journal of
Computer Science and Engineering ( JIJCSE )
Login/Register
For Reference
About Journal Aim and Scope Editorial Board Publication Ethics Indexing And Abstracting Peer Review Process About Journal Aim and Scope Editorial Board Publication Ethics Indexing And Abstracting Peer Review Process

Enhancing Wireless Communication Systems through Robust RFI Detection and Classification

Authors: Lalit Sharma

Published: 2025-03-05 23:55:46.017

Download PDF

Abstract

Enhancing the Quality of Service (QoS) and security of wireless communication systems necessitates a robust Radio Frequency Interference (RFI) detection mechanism, enabling effective mitigation strategies. This study presents a novel multi-class Multi-Layer Perceptron (MLP) neural network designed for real-time classification of jamming signals in digital video broadcasting based on DVB-S2 standards. The communication signal is assumed to coexist with one of three major interference types: Continuous Wave Interference (CWI), Multiple CWI (MCWI), or Chirp Interference (CI). The proposed algorithm effectively distinguishes interference types and identifies the Signal of Interest (SoI). Principal Component Analysis (PCA) is employed to optimize feature selection, improving classifier performance. Additionally, various learning methods—online, full-batch, and mini-batch—are analysed to determine the most effective approach for real-time applications. A key contribution of this study is the creation of a novel real-time jamming signal dataset, distinct from those used in similar research, allowing for a more comprehensive evaluation of classifier robustness. Performance assessments, conducted at varying Signal-to-Noise Ratios (SNR), demonstrate the classifier's effectiveness in recognizing both known and unknown interference signals. Comparisons with the Support Vector Machine (SVM) technique reveal superior classification and detection capabilities of the proposed MLP-based design. These findings highlight the potential of the MLP approach for real-time RFI detection and mitigation, providing a significant advancement in ensuring the reliability and security of modern wireless communication systems.

Keywords

Radio Frequency Interference RFI), Jamming Signal Detection Multi-Layer Perceptron (MLP), Continuous Wave Interference (CWI), Chirp Interference (CI), Principal Component Analysis (PCA).

References

  1. K. Jayakumar, T. Revathi, and S. Karpagam, "Intrusion Detection using Artificial Neural Networks with Best Set of Features," International Arab Journal of Information Technology (IAJIT), vol. 12, 2015.
  2. F. Amato, N. Mazzocca, F. Moscato, and E. Vivenzio, "Multilayer perceptron: An intelligent model for classification and intrusion detection," in 2017 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA), 2017: IEEE, pp. 686-691.
  3. A. Iversen, N. K. Taylor, K. E. Brown, and J. Karstad, "Classification of Communication Signals and Detection of Unknown Formats Using Artificial Neural Networks," HERIOT-WATT UNIV EDINBURGH (UNITED KINGDOM), 2006.
  4. C. Siaterlis and B. Maglaris, "Detecting DDoS attacks using a multilayer Perceptron classifier," in Proc. 20th Int. Conf. Artif. Neural Netw., III, 2004, pp. 118-123. [12] F. Haddadi, S. Khanchi, M. Shetabi, and V. Derhami, "Intrusion detection and attack classification using feed-forward neural network," in 2010 Second International Conference on Computer and Network Technology, 2010: IEEE, pp. 262-266.
  5. A. Smith, M. Evans, and J. Downey, "Modulation classification of satellite communication signals using cumulants and neural networks," in 2017 Cognitive Communications for Aerospace Applications Workshop (CCAA), 2017: IEEE, pp. 1-8.
  6. E. E. Azzouz and A. K. Nandi, "Modulation recognition using artificial neural networks," in Automatic Modulation Recognition of Communication Signals: Springer, 1996, pp. 132-176.
  7. National Instruments. "USRP N210 Kit." http://www.ettus.com/all-products/un210-kit/ (accessed.
  8. Ettus Research. "UHD." https://kb.ettus.com/UHD (accessed. K. D. S. Solutions. "T400CS Channel Simulator." http://www.rtlogic.com/products/rf-link-monitoring-andprotection-products/t400cs-channel-simulator (accessed Aug 28, 2019).
  9. T.Smyth, "CMPT 468:Frequency Modulation (FM) Synthesis," Simon Fraser University,, School of Computing Science, , 2013,.
  10. O. Mosiane, N. Oozeer, A. Aniyan, and B. A. Bassett, "Radio Frequency Interference Detection using Machine Learning," in IOP Conference Series: Materials Science and Engineering, 2017, vol. 198, no. 1: IOP Publishing, p. 012012.
  11. G. Doran, "Characterizing interference in radio astronomy observations through active and unsupervised learning," 2013.
  12. S. Misra et al., "Development of an On-Board Wide-Band Processor for Radio Frequency Interference Detection and Filtering," IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 6, pp. 3191- 3203, 2018.
  13. Schölkopf, B., & Smola, A. J. (2002). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press.
  14. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction. 2nd Edition. MIT Press.
  15. Tan, H., Yu, Z., & Wang, H. (2019). Robust interference detection and classification in wireless communication using deep neural networks. IEEE Transactions on Wireless Communications, 18(10), 4567–4579.
  16. Singh, R., Kumar, S., & Sharma, N. (2021). Enhancing feature selection for interference detection with PCA: A machine learning approach. Journal of Wireless Networks, 27(4), 981–994.
  17. Yang, Z., Zhang, Y., & Chen, X. (2020). Multi-class classification of jamming signals in wireless communication networks. Journal of Communications and Networks, 22(6), 479–490.
  18. A. M. Llenas, J. Riihijarvi, and M. Petrova, "Performance evaluation of machine learning based signal classification using statistical and multiscale entropy features," in 2017 IEEE Wireless Communications and Networking Conference (WCNC), 2017: IEEE, pp. 1-6. [23] Z. Zhu and A. K. Nandi, Automatic mdulation classification: principles, algorithms and applications. John Wiley & Sons, 2015.
Download PDF

How to Cite

Lalit Sharma, (2025-03-05 23:55:46.017). Enhancing Wireless Communication Systems through Robust RFI Detection and Classification. JANOLI International Journal of Computer Science and Engineering , Volume rn1ql9uo4BygpjFCAoIa, Issue 1.

Follow Us
Get In Touch

Vitthal Namdev Nagar, Opp Jain Temple Sanganer Sanganer Bazar Jaipur 302029 Rajasthan (India)

mail@janolijournals.org

© JANOLI International Journal. All Rights Reserved.