Deep Learning for Spectrum Sensing and Interference Mitigation in Wireless Networks
DOI:
https://doi.org/10.58445/rars.3515Keywords:
Spectrum Sensing, Interference Detection, Deep Learning, Wireless NetworksAbstract
Effective spectrum monitoring in the congested 2.4 GHz band, where ZigBee, Wi-Fi, Bluetooth, and others coexist, requires solutions that balance high accuracy with low computational complexity for real-time operation. Existing Deep Learning (DL) approaches, such as ResNet, are often characterized by high computational loads or are trained exclusively on synthetic data, limiting their robustness in real-world conditions.
This work proposes a compact and computationally efficient Convolutional Neural Network (CNN) architecture for simultaneous radio interference detection and classification. Short-Time Fourier Transform (STFT) spectrograms are utilized as input data.
A key element of novelty is the training methodology employing a hybrid dataset, which combines synthetic signals (sourced from RadioML and MathWorks) with an extensive array of real-world raw over-the-air recordings obtained via Software-Defined Radio (SDR).
Experimental results demonstrate that the proposed architecture achieves 94% accuracy in detection and classification tasks. The model significantly outperforms baseline CNN and ResNet architectures, particularly regarding stability and robustness across various Signal-to-Noise Ratios (SNR).
The findings confirm that the proposed lightweight approach, enhanced by hybrid training, constitutes a highly effective and practical solution for real-world deployment in dynamic radio resource management systems.
References
Zhu, J., Waltho, A., Yang, X., & Guo, X. (2007, August). Multi-radio coexistence: Challenges and opportunities. In 2007 16th International Conference on Computer Communications and Networks (pp. 358-364). IEEE.
Sharma, N., & Kaur, A. (2010, November). Coexistence: Threat to the Performance of Heterogeneous Network. In AIP Conference Proceedings (Vol. 1324, No. 1, pp. 90-94). American Institute of Physics.
Jha, U. S. (2002). Wireless Landscape–Need for Seamless Connectivity. Wireless Personal Communications, 22(2), 275-283.
Zheng, L., Lops, M., Eldar, Y. C., & Wang, X. (2019). Radar and communication coexistence: An overview: A review of recent methods. IEEE Signal Processing Magazine, 36(5), 85-99.
Su, Y., Lin, Y., Liu, S., Liwang, M., Liao, X., Wu, T., ... & Wang, X. (2024). Coexistence of hybrid VLC-RF and Wi-Fi for indoor wireless communication systems: an intelligent approach. IEEE Transactions on Network and Service Management.
Omotere, O., Fuller, J., Qian, L., & Han, Z. (2018, August). Spectrum occupancy prediction in coexisting wireless systems using deep learning. In 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall) (pp. 1-7). IEEE.
Miller, R. D., Kokalj-Filipovic, S., Vanhoy, G., & Morman, J. (2019, November). Policy based synthesis: Data generation and augmentation methods for rf machine learning. In 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) (pp. 1-5). IEEE.
O'shea, T. J., & West, N. (2016, September). Radio machine learning dataset generation with gnu radio. In Proceedings of the GNU radio conference (Vol. 1, No. 1).
Kattenborn, T., Leitloff, J., Schiefer, F., & Hinz, S. (2021). Review on Convolutional Neural Networks (CNN) in vegetation remote sensing. ISPRS journal of photogrammetry and remote sensing, 173, 24-49.
Lu, X., Tao, M., Fu, X., Gui, G., Ohtsuki, T., & Sari, H. (2021, September). Lightweight network design based on ResNet structure for modulation recognition. In 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall) (pp. 1-5). IEEE.
Podder, P., Zawodniok, M., & Madria, S. (2024, June). Deep learning for UAV detection and classification via Radio frequency signal analysis. In 2024 25th IEEE International Conference on Mobile Data Management (MDM) (pp. 165-174). IEEE.
Nguyen, H. N., Vomvas, M., Vo-Huu, T., & Noubir, G. (2021, November). Wideband, real-time spectro-temporal RF identification. In Proceedings of the 19th ACM international symposium on mobility management and wireless access (pp. 77-86).
Boegner, L., Vanhoy, G., Vallance, P., Gulati, M., Feitzinger, D., Comar, B., & Miller, R. D. (2022). Large scale radio frequency wideband signal detection & recognition. arXiv preprint arXiv:2211.10335.
Fan, C., Yuan, X., & Zhang, Y. J. (2019). CNN-based signal detection for banded linear systems. IEEE Transactions on Wireless Communications, 18(9), 4394-4407.
Scholl, S. (2022). RF signal classification with synthetic training data and its real-world performance. arXiv preprint arXiv:2206.12967.
Carlson, A., Skinner, K. A., Vasudevan, R., & Johnson-Roberson, M. (2019). Sensor transfer: Learning optimal sensor effect image augmentation for sim-to-real domain adaptation. IEEE Robotics and Automation Letters, 4(3), 2431-2438.
Hu, C., Hudson, S., Ethier, M., Al-Sharman, M., Rayside, D., & Melek, W. (2022, June). Sim-to-real domain adaptation for lane detection and classification in autonomous driving. In 2022 IEEE Intelligent Vehicles Symposium (IV) (pp. 457-463). IEEE.
Tandra, R., & Sahai, A. (2008). SNR walls for signal detection. IEEE Journal of selected topics in Signal Processing, 2(1), 4-17.
Jagannath, A., & Jagannath, J. (2021, June). Multi-task learning approach for automatic modulation and wireless signal classification. In ICC 2021-IEEE International Conference on Communications (pp. 1-7). IEEE.
Huang, Z., Pemasiri, A., Denman, S., Fookes, C., & Martin, T. (2023, June). Multi-task learning for radar signal characterisation. In 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) (pp. 1-5). IEEE.
Downloads
Posted
Categories
License
Copyright (c) 2025 Adilzhan Ayazbek, Olzhas Myrzakhmet
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
