Optimizing Automatic Modulation Classification through Gaussian-Regularized Hybrid CNN-LSTM Architecture | ||
| JES. Journal of Engineering Sciences | ||
| Article 5, Volume 52, Issue 4, July 2024, Pages 46-61 PDF (1.25 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.21608/jesaun.2024.271102.1315 | ||
| Authors | ||
| Mohamed Elsagheer* ; Khairy Abd Elsayed; safwat Ramzy | ||
| Electrical Engineering Department, Faculty of Engineering, Sohag University, Sohag, Egypt. | ||
| Abstract | ||
| This paper presents an innovative deep-learning model for Automatic Modulation Classification (AMC) in wireless communication systems. The proposed architecture integrates Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks, augmented by a Gaussian noise layer to mitigate overfitting. The integration of both networks seeks to enhance classification accuracy and performance by leveraging the unique capabilities of CNNs and LSTMs in capturing spatial and temporal features, respectively. The model is expected to distinguish between eight digital and two analog modulation modes. Experimental evaluation on the RadioML2016.10b dataset demonstrates a peak recognition accuracy of 93.2% at 18 dB SNR. Comparative analyses validate the superior performance of the proposed architecture. The Gaussian noise layer contributes significantly to a 3% performance improvement at 18 dB SNR. The model achieves recognition accuracy exceeding 96% for most modulation modes, highlighting its robustness. Finally, computational complexity analysis underscores the efficiency of the proposed architecture, reinforcing its practical viability. | ||
| Keywords | ||
| deep-learning; Automatic Modulation Classification; CNN; LSTM; SNR | ||
| References | ||
|
[1] J.C. Clement, N. Indira, P. Vijayakumar, R. Nandakumar, "Deep learning-based modulation
Classification for 5G and beyond wireless systems," Peer-- Netw. Appl., vol. 14, pp. 319–
332, 2021.
[2] L. Hong, K.C. Ho, "Antenna array likelihood modulation classifier for BPSK and QPSK
Signals," in MILCOM 2002 Proc., 2002, pp. 647–651 vol.1.
[3] B.F. Beidas, C.L. Weber, "Asynchronous classification of MFSK signals using the higher order
Correlation domain," IEEE Trans. Commun., vol. 46, pp. 480–493, 1998.
[4] J.A. Sills, "Maximum-likelihood modulation classification for PSK/QAM," in MILCOM 1999
IEEE Mil. Commun. Conf. Proc. Cat No99CH36341, 1999, pp. 217–220 vol.1.
[5] K. Kim, A. Polydoros, "Digital modulation classification: the BPSK versus QPSK case,"
MILCOM 88 21st Century Mil. Commun. - Whats Possible Conf. Rec. Mil. Commun. Conf.,
1988.
[6] Z. Sun, S. Wang, X. Chen, "Feature-Based Digital Modulation Recognition Using Compressive
Sampling," Mob. Inf. Syst., vol. 2016, 2016, e9754162.
[7] M.V. Subbarao, P. Samundiswary, "Automatic Modulation Recognition in Cognitive Radio
Receivers using Multi-Order Cumulants and Decision Trees," vol. 7, 2018, p. 10.
[8] A. Puengnim, T. Robert, N. Thomas, J. Vidal, "Hidden Markov Models for digital modulation
Classification in unknown ISI channels," in 2007 15th Eur. Signal Process. Conf., 2007, pp1882– 1886.
[9] W. Dai, Y. Wang, J. Wang, "Joint power estimation and modulation classification using second-
And higher statistics," in 2002 IEEE Wirel. Commun. Netw. Conf. Rec. WCNC 2002 Cat
No02TH8609, 2002, pp. 155–158 vol.1.
[10] Khan et al., "3D convolutional neural networks based automatic modulation classification in
The presence of channel noise," IET Communications, 2022. [Online]. Available:
https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/cmu2.12269 [Accessed: January 2,
2023].
[11] A. Shah, A. Miry, T. Salman, "Automatic modulation classification based deep learning with
Mixed feature," Int. J. Electr. Comput. Eng., vol. 13, pp. 1647–1653, 2023.
[12] N. Wang, Y. Liu, L. Ma, Y. Yang, H. Wang, "Automatic Modulation Classification Based on
CNN and Multiple Kernel Maximum Mean Discrepancy," Electronics, vol. 12, 2023, p. 66.
[13] F. Meng, P. Chen, L. Wu, X. Wang, "Automatic Modulation Classification: A Deep Learning
Enabled Approach," IEEE Trans. Veh. Technol., vol. 67, pp. 10760–10772, 2018.
[14] M. Lotfy, M.H. Essai, H.A. Atallah, "Automatic Modulation Classification: Convolutional
Deep Learning Neural Networks Approaches," SVU-Int. J. Eng. Sci. Appl., vol. 4, pp. 48–54,
2023.
[15] T. Huynh-The, Q.-V. Pham, T.-V. Nguyen, T.T. Nguyen, D.B. da Costa, D.-S. Kim, "RanNet:
Learning Residual-Attention Structure in CNNs for Automatic Modulation Classification,"
IEEE Wirel. Commun. Lett. vol. 11, pp. 1243–1247, 2022.
[16] T. O’Shea, J. Corgan, T. Clancy, "Convolutional Radio Modulation Recognition Networks," in
2016. [17] T.J. O’Shea, T. Roy, T.C. Clancy, "Over-the-Air Deep Learning Based Radio Sign Classification," IEEE J. Sel. Top. Signal Process. vol. 12, pp. 168–179, 2018.
[18] X. Hao, Y. Luo, Q. Ye, Q. He, G. Yang, C.-C. Chen, "Automatic Modulation Recognition
Method Based on Hybrid Model of Convolutional Neural Networks and Gated Recurrent
Units," Sens. Mater. vol. 33, pp. 4229–4243, 2021.
[19] Y. Wang, M. Liu, J. Yang, G. GUI, "Data-Driven Deep Learning for Automatic Modulation
Recognition in Cognitive Radios," IEEE Trans. Veh. Technol., vol. 68, pp. 4074–4077, 2019.
[20] D. Inc, "RF Datasets for Machine Learning," DeepSig. [Online]. Available:
[21] "Google Colaboratory," [Online]. Available: https://colab.research.google.com/.
[22] M.M. Elsagheer, S.M. Ramzy, "A hybrid model for automatic modulation classification based
On residual neural networks and long short-term memory," Alex. Eng. J., vol. 67, pp. 117–128,
2023.
[23] J.N. Njoku, M.E. Morocho-Cayamcela, W. Lim, "CGDNet: Efficient Hybrid Deep Learning
Model for Robust Automatic Modulation Recognition," IEEE Netw. Lett. vol. 3, pp. 47–51,
2021.
[24] J. Xu, C. Luo, G. Parr, Y. Luo, "A Spatiotemporal Multi-Channel Learning Framework for
Automatic Modulation Recognition," IEEE Wirel. Commun. Lett. pp. 1–1, 2020.
[25] Y. Lin, Y. Tu, Z. Dou, "An Improved Neural Network Pruning Technology for Automatic
Modulation Classification in Edge Devices," IEEE Trans. Veh. Technol., vol. 69, pp. 5703–
5706, 2020. | ||
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