Deep Learning-Based Signal Classification in Wireless Fading Channels
Received: 7 September 2025 | Revised: 2 October 2025 | Accepted: 12 October 2025 | Online: 8 December 2025
Corresponding author: Beza Negash Getu
Abstract
Signal detection and identification are critical in today's shared wireless communication environments with diverse devices. These processes are fundamental to wireless, satellite, cognitive radio, and military communication systems. Automatic and real-time identification of radio traffic enables efficient utilization of resources, such as transmission bandwidth and power. It also facilitates dynamic spectrum access by identifying unused spectrum bands and maintaining fair coexistence among the communication devices. Furthermore, it contributes to spectrum surveillance by detecting unknown or unauthorized transmissions, and, hence, enhancing the Quality of Service (QoS) and network performance through effective noise and interference detection and mitigation. In this work, a Deep Learning (DL) approach is proposed for feature extraction and classification of received signals. In particular, a Convolutional Neural Network (CNN) is employed to classify multiple modulation schemes in wireless fading channel conditions. Simulation results show that the CNN provides high classification accuracy, indicating the effectiveness of the proposed method in realistic wireless channels.
Keywords:
signal detection, signal identification, Deep Learning (DL), modulation, fading channels, classification, Convolutional Neural Network (CNN)Downloads
References
S. Haykin, "Cognitive radio: brain-empowered wireless communications," IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201–220, Feb. 2005. DOI: https://doi.org/10.1109/JSAC.2004.839380
A. Swami and B. M. Sadler, "Hierarchical digital modulation classification using cumulants," IEEE Transactions on Communications, vol. 48, no. 3, pp. 416–429, Mar. 2000. DOI: https://doi.org/10.1109/26.837045
H. V. Poor, An Introduction to Signal Detection and Estimation. New York, NY, USA: Springer, 1994. DOI: https://doi.org/10.1007/978-1-4757-2341-0
M. Oner and F. Jondral, "Air interface recognition for a software radio system exploiting cyclostationarity," in 2004 IEEE 15th International Symposium on Personal, Indoor and Mobile Radio Communications, Barcelona, Spain, 2004, pp. 1947-1951 vol.3. DOI: https://doi.org/10.1109/PIMRC.2004.1368338
I. F. Akyildiz, W.-Y. Lee, M. C. Vuran, and S. Mohanty, "NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey," Computer Networks, vol. 50, no. 13, pp. 2127–2159, Sept. 2006. DOI: https://doi.org/10.1016/j.comnet.2006.05.001
A. G. Fragkiadakis, E. Z. Tragos, and I. G. Askoxylakis, "A Survey on Security Threats and Detection Techniques in Cognitive Radio Networks," IEEE Communications Surveys & Tutorials, vol. 15, no. 1, pp. 428–445, 2013. DOI: https://doi.org/10.1109/SURV.2011.122211.00162
A. E. Spezio, "Electronic warfare systems," IEEE Transactions on Microwave Theory and Techniques, vol. 50, no. 3, pp. 633–644, Mar. 2002. DOI: https://doi.org/10.1109/22.989948
P. He, Y. Zhang, X. Yang, X. Xiao, H. Wang, and R. Zhang, "Deep Learning-Based Modulation Recognition for Low Signal-to-Noise Ratio Environments," Electronics, vol. 11, no. 23, Dec. 2022, Art. no. 4026. DOI: https://doi.org/10.3390/electronics11234026
E. A. Kadir, R. Shubair, S. K. Abdul Rahim, M. Himdi, M. R. Kamarudin, and S. L. Rosa, "B5G and 6G: Next Generation Wireless Communications Technologies, Demand and Challenges," in 2021 International Congress of Advanced Technology and Engineering, Taiz, Yemen, 2021, pp. 1–6. DOI: https://doi.org/10.1109/ICOTEN52080.2021.9493470
M. Z. Asghar, S. A. Memon, and J. Hämäläinen, "Evolution of Wireless Communication to 6G: Potential Applications and Research Directions," Sustainability, vol. 14, no. 10, May 2022, Art. no. 6356. DOI: https://doi.org/10.3390/su14106356
N. Telagam, N. Kandasamy, A. K. Manoharan, P. Anandhi, and R. Atchudan, "Beyond 5G: Exploring key enabling technologies, use cases, and future prospects of 6 G communication," Nano Communication Networks, vol. 43, Mar. 2025, Art. no. 100560. DOI: https://doi.org/10.1016/j.nancom.2024.100560
L. U. Khan, I. Yaqoob, M. Imran, Z. Han, and C. S. Hong, "6G Wireless Systems: A Vision, Architectural Elements, and Future Directions," IEEE Access, vol. 8, pp. 147029–147044, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3015289
S. Sikkanan, S. T., K. P. S., K. M., and S. Mehta, "5G and 6G Wireless Communication," in Innovative Smart Materials Used in Wireless Communication Technology, R. Krishan and M. Kaur, Eds. Hershey, PA, USA: IGI Global Scientific Publishing, 2023. DOI: https://doi.org/10.4018/978-1-6684-7000-8.ch015
Z. Z. Yahya, D. M. Ali, and M. Y. Abdallah, "6G Automatic Modulation Classification using Deep Learning Models in the Presence of Channel Noise, CFO, and PN," Majlesi Journal of Electrical Engineering, vol. 18, no. 4, pp. 1–9, Dec. 2024.
L. Jiao et al., "Advanced Deep Learning Models for 6G: Overview, Opportunities, and Challenges," IEEE Access, vol. 12, pp. 133245–133314, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3418900
A. Alhammadi et al., "Artificial Intelligence in 6G Wireless Networks: Opportunities, Applications, and Challenges," International Journal of Intelligent Systems, vol. 2024, no. 1, Mar. 2024, Art. no. 8845070. DOI: https://doi.org/10.1155/2024/8845070
A. Bazzi, R. Bomfin, M. Mezzavilla, S. Rangan, T. Rappaport, and M. Chafii, "Upper Mid-Band Spectrum for 6G: Vision, Opportunity and Challenges." arXiv, Sept. 08, 2025. DOI: https://doi.org/10.32388/XNRU0A
M. Delamou, A. Bazzi, M. Chafii, and E. M. Amhoud, "Deep Learning-based Estimation for Multitarget Radar Detection," in 2023 IEEE 97th Vehicular Technology Conference, Florence, Italy, 2023, pp. 1–5. DOI: https://doi.org/10.1109/VTC2023-Spring57618.2023.10200157
X. Zha, H. Peng, X. Qin, G. Li, and S. Yang, "A Deep Learning Framework for Signal Detection and Modulation Classification," Sensors, vol. 19, no. 18, Sept. 2019, Art. no. 4042. DOI: https://doi.org/10.3390/s19184042
W. Zhang, M. Feng, M. Krunz, and A. Hossein Yazdani Abyaneh, "Signal Detection and Classification in Shared Spectrum: A Deep Learning Approach," in IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, Vancouver, Canada, 2021, pp. 1–10. DOI: https://doi.org/10.1109/INFOCOM42981.2021.9488834
Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436–444, May 2015. DOI: https://doi.org/10.1038/nature14539
A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," Communications of the ACM, vol. 60, no. 6, pp. 84–90, May 2017. DOI: https://doi.org/10.1145/3065386
G. Litjens et al., "A survey on deep learning in medical image analysis," Medical Image Analysis, vol. 42, pp. 60–88, Dec. 2017. DOI: https://doi.org/10.1016/j.media.2017.07.005
Q. C. Lam, L. A. T. Nguyen, and H. K. Nguyen, "EEG Signal Identification Using Single-Layer Neural Network," International Journal on Cybernetics & Informatics, vol. 5, no. 5, pp. 1–10, Oct. 2016. DOI: https://doi.org/10.5121/ijci.2016.5501
H. Roh, S. Oh, H. Song, J. Han, and S. Lim, "Deep Learning-based Wireless Signal Classification in the IoT Environment," Computers, Materials & Continua, vol. 71, no. 3, pp. 5717–5732, Jan. 2022. DOI: https://doi.org/10.32604/cmc.2022.024135
B. N. Getu, "LSTM Based Time Series Forecasting of Noisy Signals," in 16th Asian Conference on Intelligent Information and Database Systems, Proceedings, Part II, Ras Al Khaimah, UAE, 2024, pp. 133–146. DOI: https://doi.org/10.1007/978-981-97-5934-7_12
H. Ouamna, A. Kharbouche, Z. Madini, and Y. Zouine, "Deep Learning-assisted Automatic Modulation Classification using Spectrograms," Engineering, Technology & Applied Science Research, vol. 15, no. 1, pp. 19925–19932, Feb. 2025. DOI: https://doi.org/10.48084/etasr.9334
T. S. Rappaport, Wireless communications: Principles and Practice, 2nd ed. Upper Saddle River, NJ, USA: Prentice Hall, 2001.
A. Goldsmith, Wireless Communications. Cambridge, UK: Cambridge University Press, 2005.
M. K. Simon and M.-S. Alouini, Digital Communication over Fading Channels, 2nd ed. Hoboken, NJ, USA: John Wiley & Sons, 2005. DOI: https://doi.org/10.1002/0471715220
B. N. Getu and J. B. Andersen, "MIMO Systems in Random Uncorrelated, Correlated and Deterministic Radio Channels," Wireless Personal Communications, vol. 30, no. 1, pp. 27–61, July 2004. DOI: https://doi.org/10.1007/s11277-004-4847-x
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