ADAPTIVE INTERFERENCE SUPPRESSION IN WIRELESS NETWORKS BASED ON ARTIFICIAL NOISE

Authors

DOI:

https://doi.org/10.18372/2410-7840.26.20015

Abstract

Adaptive interference suppression based on artificial noise is a promising approach to enhancing the security of wireless networks. In traditional cryptographic protection systems, attackers can exploit physical-layer attacks to intercept signals. One of the effective protection methods is the use of artificial noise (AN), which generates specially designed interference to complicate unauthorized access. This paper investigates the principles of adaptive artificial noise power control using the gradient descent method. The proposed approach enables dynamic noise level regulation based on communication channel parameters such as attacker distance, signal strength, and environmental interference. The modeling was conducted using an open-source wireless sensor network (WSN) dataset, allowing us to evaluate the impact of adaptive noise on packet loss and signal strength. The results demonstrate that the optimized method effectively reduces the probability of interception without significantly degrading the communication quality for legitimate users. The proposed model can be applied in modern mobile communication systems, IoT networks, and critical infrastructures requiring an increased level of data protection.

Author Biographies

Stanislava Kudrenko, State university «Kyiv aviation institute»

Candidate of Technical Sciences, associate professor of the Department of Computer Systems and Networks of the State non-commercial company state university «Kyiv aviation institute», Kyiv, Ukraine.

Valeriy Kozlovsky, State university «Kyiv aviation institute»

Doctor of Technical Science, Professor, head of the Department of Technical Information Protection of the State non-commercial company state university «Kyiv aviation institute», Kyiv, Ukraine.

Anna Stoliar, State university «Kyiv aviation institute»

PhD student of the Department of  Computer Systems and Networks of the State non-commercial company state university «Kyiv aviation institute», Kyiv, Ukraine.

References

Phan N. N., Nguyen S. Q. Artificial Noise-aided Adaptive Secure Transmissions. 2022 International Conference on Advanced Technologies for Communications. Ha Noi, Vietnam, 20-22 October 2022. P. 230-

doi: 10.1109/ATC55345.2022.9942978.

Kang T. et al. Federated Low-Rank Adaptation with Differential Privacy over Wireless Networks. arXiv preprint arXiv:2411.07806, 2024. [Online]. Available: https://arxiv.org/abs/2411.07806.

Rajabi M.E., Khaleghi Bizaki H., Shafiee M. Adaptive Wireless Covert Communication with Full/Half- Duplex Transceiver using Artificial Noise. Arab J Sci Eng. 2024. doi: 10.1007/s13369-024-09800-1.

Khodadadi H. R., Falsafi S. Improvement of security in wireless communication networks with directional modulation and artificial noise. Scientific Journal of Electronical & Cyber Defence. 2023. [Online]. Available: https://www.sid.ir/fileserver/jf/1134-267714-fa-1082998.pdf

Ju Y. et al. Artificial noise hopping: A practical secure transmission technique with experimental analysis for millimeter wave systems. IEEE Systems Journal. 2020. Vol. 14, iss. 4. P. 5121-5132. doi: 10.1109/JSYST.2020.2976852.

Wang S. et al. Artificial noise aided hybrid analog-digital beamforming for secure transmission in MIMO millimeter wave relay systems. IEEE Access. 2019. Vol. 7. P. 28597-28606. doi: 10.1109/ACCESS.2019.2902144.

Yang N., Elkashlan M., Duong T. Q. Optimal transmission with artificial noise in MISOME wiretap channels. IEEE Transactions on Wireless Communications. 2015. Vol. 14, no. 5. P. 2476-2490. [Online]. Available: http://www.eecs.qmul.ac.uk/~maged/Optimal%20transmission%20with%20artificial%20noise%20in%20MI SOME%20wiretap%20channels.pdf

Wang W. et al. On the impact of adaptive eavesdroppers in multi-antenna cellular networks. IEEE Transactions on Communications. 2017. Vol. 65, no. 8. P. 3424-3437. doi: 10.1109/TIFS.2017.2746010.

Zhou X., McKay M. R. Secure transmission with artificial noise over fading channels: Achievable rate and optimal power allocation. IEEE Transactions on Vehicular Technology. 2010. Vol. 59, no. 6. P. 3831-3842. [Online]. Available: https://arxiv.org/pdf/1006.5938

Wang P. et al. Resource allocation optimization for secure multidevice wirelessly powered backscatter communication with artificial noise. IEEE Transactions on Wireless Communications. 2022. Vol. 21, no. 4.

P. 2532-2547. doi: 10.1109/TWC.2022.3162137.

Khemapech I., Miller A., Duncan I. A survey of transmission power control in wireless sensor networks. Proceedings of PGNet, 2007. P. 15-20.

Hung C. W. et al. Transmission power control in wireless sensor networks using fuzzy adaptive data rate. Sensors. 2022. Vol. 22, no. 24. [Online]. Available: https://www.mdpi.com/1424-8220/22/24/9963.

Published

2025-05-20