Benchmarking Deep Learning Architectures for Radio Modulation Classification Using the Radioml Dataset in Wireless Network
Downloads
Automatic Modulation Classification (AMC) is an essential element of the current wireless communication system, which allows the proper recognition of modulation schemes in the dynamic conditions on the channel and changing signal-to-noise ratios (SNRs). This study presents the results of DL architectural benchmarking using RadioML 2016. The architectures included in the research are CNN+BiLSTM, deep 1D ResNet, and LSTM.data set 10a. The approaches that are integrated into the proposed framework include the critical signal preprocessing steps, including the high-SNR filtering, power normalization, label encoding, and systematic split of training and testing data to get a better quality of data and better generalization of the model. The convolutional layers are used to achieve effective space features of raw I/Q samples and residual links within the ResNet framework to train the network deeper and prevent the vanishing gradient issue. Model performance is evaluated using accuracy (ACC), precision (PRE), recall (REC), F1-score (F1), and Matthews Correlation Coefficient (MCC). The experimental such as, the proposed ResNet model, which scores test ACC of 92.63%, with lower test loss (0.2903), along with higher PRE (0.9424), REC (0.9263), F1 (0.9225), and MCC (0.9213), compared with existing models, including HMF (90.70%), DenseNet (0.868), GRU (0.7891), and DRMM (73.31%) the robustness and usefulness of the ResNet model in the classification of test modulations in wireless networks.
S. K. Davuluri, V. Challagulla, V. Mudapaka, and U. Konka, “Telcoformrix: An AI-Augmented Framework for Declarative and Scalable Provisioning of Real-Time Communication Infrastructure (Work in Progress),” in 2025 IEEE International Conference and Expo on Real Time Communications at IIT (RTC), Chicago, IL, USA: IEEE, 2025, pp. 1–4, October.
doi: 10.1109/RTC66985.2025.11211725.
S. Singh, Enabling Sustainable 5G Networks Through Energy-Efficient Open Radio Access Networks (ORAN) Ecosystems. 2026. doi: 10.1007/978-981-96-8793-0_11.
Q. Zheng et al., “MobileRaT: A Lightweight Radio Transformer Method for Automatic Modulation Classification in Drone Communication Systems,” Drones, vol. 7, no. 10, 2023,
doi: 10.3390/drones7100596.
V. K. Sharma, “STRATEGIC FRAMEWORK FOR AI-ENHANCED PORTFOLIOS IN WIRELESS ENGINEERING: A LITERATURE REVIEW,” Int. J. Core Eng. Manag., vol. 8, no. 03, pp. 77–83, 2025.
Vaidehi Shah, “Next-Gen Emergency Communication Using Low-Power Wide-Area and Software-Defined WANS,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 2, no. 1, pp. 600–609, Sep. 2022,
doi: 10.48175/IJARSCT-8349M.
H. P. Cyril, “EVENT-DRIVEN PROVISIONING ARCHITECTURES FOR MODERN TELECOM NETWORKS: OVERCOMING LEGACY LIMITATIONS AND ENABLING AUTONOMOUS 6G OPERATIONS,” Int. J. Adv. Res. Comput. Sci., vol. 16, no. 6, pp. 75–82, 2025.
S. Chen, S. Zheng, L. Yang, and X. Yang, “Deep Learning for Large-Scale Real-World ACARS and ADS-B Radio Signal Classification,” IEEE Access, vol. 7, pp. 89256–89264, 2019,
doi: 10.1109/ACCESS.2019.2925569.
L. Huang, Y. Zhang, W. Pan, J. Chen, L. P. Qian, and Y. Wu, “Visualizing Deep Learning-Based Radio Modulation Classifier,” IEEE Trans. Cogn. Commun. Netw., vol. 7, no. 1, pp. 47–58, 2021,
doi: 10.1109/TCCN.2020.3048113.
S. Singh, “Advancing Wireless Communications with Open Radio Access Network,” in 2025 6th International Conference on Data Intelligence and Cognitive Informatics (ICDICI), Tirunelveli, India: IEEE, Jul. 2025, pp. 682–687.
doi: 10.1109/ICDICI66477.2025.11135206.
S. K. Chintagunta and S. Amrale, “A Deep Learning Framework for Adaptive E- Learning : Integrating Learning Style Detection in Web-Based Platforms,” Int. J. Adv. Res. Sci. Commun. Technol., pp. 716–727, 2024, doi: 10.48175/IJARSCT-19397.
L. Huang, W. Pan, Y. Zhang, L. Qian, N. Gao, and Y. Wu, “Data Augmentation for Deep Learning-Based Radio Modulation Classification,” IEEE Access, vol. 8, pp. 1498–1506, 2020,
doi: 10.1109/ACCESS.2019.2960775.
S. Singh, S. A. Pahune, P. Chatterjee, and R. Sura, “Advanced Machine Learning Methods for Churn Prediction and Classification in Telecom Sector,” in 2025 IEEE 6th India Council International Subsections Conference (INDISCON), Rourkela, India: IEEE, 2025, pp. 1–7, December.
doi: 10.1109/INDISCON66021.2025.11252233.
M. M. Tahir et al., “HFDNN : A Hybrid Fusion Deep Neural Network for Robust Automatic Modulation Classification in Adverse Wireless Environments,” IEEE Access, vol. 14, no. February, 2026.
K. A. Mohamed et al., “FPGA-Optimized CNN Architecture for Automatic Modulation Classification,” in 2025 37th International Conference on Microelectronics (ICM), 2025, pp. 1–6.
doi: 10.1109/ICM66518.2025.11321328.
J. Gurucharan, M. Padmapriya, M. R. Grace, S. B. Krishna, P. Ganesan, and V. Elamaran, “Deep Learning on Modulation Classification using Google’s Teachable Machine,” in 2025 1st International Conference on Radio Frequency Communication and Networks (RFCoN), 2025, pp. 1–6.
doi: 10.1109/RFCoN62306.2025.11085309.
[16] B. E. Altuntas, O. Aksu, M. Y. Celik, and M. H. Durak, “Deep Learning Based Automatic Modulation Recognition Using GELU Activation Function,” in 2024 4th International Conference on Emerging Smart Technologies and Applications (eSmarTA), IEEE, Aug. 2024, pp. 1–4.
doi: 10.1109/eSmarTA62850.2024.10638998.
M. Abdollahi, R. Sabzalizadeh, S. Javadinia, S. Mashhadi, S. S. Mehrizi, and A. Baniasadi, “Automatic Modulation Classification for NLOS 5G Signals with Deep Learning Approaches,” in 2023 10th International Conference on Wireless Networks and Mobile Communications (WINCOM), 2023, pp. 1–6. doi: 10.1109/WINCOM59760.2023.10322928.
M. B. Mahieddine, N. Mellah, A. Bassou, and M. Khelifi, “Implementation of CNN-Inception Deep Learning for Cognitive Radio Based on Modulation Classifications,” in 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), IEEE, Mar. 2022, pp. 1–5.
doi: 10.1109/IRASET52964.2022.9738405.
T. Bayan, D. Das, and N. Deb, “Deep Hybrid CNN–BiLSTM–Attention Model for EEG Classification Using Wavelet Features,” Ann. Neurosci., Jan. 2026, doi: 10.1177/09727531251396337.
N. Wang, Y. Liu, L. Ma, Y. Yang, and H. Wang, “Multidimensional CNN-LSTM Network for Automatic Modulation Classification,” Electronics, vol. 10, no. 14, 2021,
doi: 10.3390/electronics10141649.
S. Hou, Y. Fan, B. Han, Y. Li, and S. Fang, “Signal Modulation Recognition Algorithm Based on Improved Spatiotemporal Multi-Channel Network,” Electronics, vol. 12, p. 422, 2023,
doi: 10.3390/electronics12020422.
S. Ansari et al., “Attention-Enhanced Hybrid Automatic Modulation Classification for Advanced Wireless Communication Systems: A Deep Learning-Transformer Framework,” IEEE Access, vol. 13, no. June, pp. 105463–105491, 2025,
doi: 10.1109/ACCESS.2025.3580574.
Y. Peng et al., “Automatic Modulation Classification Using Deep Residual Neural Network with Masked Modeling for Wireless Communications,” Drones, vol. 7, no. 6, 2023, doi: 10.3390/drones7060390.
