M-ary Pulse Ampplitude Modulation Recognition Using Discrete Meyer Wavelet and Reverse Biorthogonal Wavelet
DOI:
https://doi.org/10.22399/ijcesen.749Keywords:
Automatic modulation recognition, M-ary pulse amplitude modulation, reverse Biorthogonal wavelet, Meyer wavelet transformAbstract
Automatic modulation recognition (AMR) is a fundamental task in communication systems. Feature extraction (FE) is an essential part in the recognition system,the proper selection of FE will enhance the recognition accuracy, and reduce the complexity of the system. In this paper, Reverse Biorthogonal wavelet (RBW), andDiscrete Meyer Wavelet (DMW), followed by standard deviation are used for FE. They are used to reduce the FE sets, and complexity of the recognition system.Adaptive Neuro Fuzzy Inference system is used as a classifier, to classify the,M-ary Pulse Amplitude Modulation (PAM) signals (i.e.4PAM, 8PAM, 16PAM, 32PAM, 64PAM, and128PAM), in a wide range of signal to noise ratio (SNR). MATLAB programs were used to fulfill all the requested tasks.The results show that the recognition system of M-ary (PAM) signals exhibits a satisfactory level under low SNR, and the system can achieve success rates over 98% in SNR ( from -2 to 12) dB.
References
Song, G., Jang, M., & Yoon, D. (2022). CNN-based automatic modulation classification in OFDM systems. In Proceedings of IEEE International Conference on Computer, Information and Telecommunication Systems (CITS). IEEE. 1-4. https://doi.org/10.1109/CITS55221.2022.9832989.
Gao, W. (2024). Research on modulation recognition method of electromagnetic signal based on wavelet transform convolutional neural network. Mathematical Models in Engineering. 10(1);11-25. https://doi.org/10.21595/mme.2023.23746.
Alhadi, B. A., Hasan, T. M., & Hamed, H. A. (2020). Digitally modulated signal recognition based on feature extraction optimization and random forest classifier. In Proceedings of Springer International Conference on New Trends in Information and Communication Technology Applications. Communications in Computer and Information Science. Springer, Cham. 1183;75–84. https://doi.org/10.1007/978-3-030-55340-1_6.
Qi, P., Zhou, X., Ding, Y., Zheng, S., Jiang, T., & Li, Z. (2022). Collaborative and incremental learning for modulation classification with heterogeneous local dataset in cognitive IoT. IEEE Transactions on Green Communications and Networking. 7(2);881–893. https://doi.org/10.1109/TGCN.2022.3186898.
Wang, M., Fan, Y., Fang, S., Cui, T., & Cheng, D. (2022). A joint automatic modulation classification scheme in spatial cognitive communication. Sensors. 22;1-11. https://doi.org/10.3390/522176500
Hassan, K., Dayoub, I., Hamouda, W., & Berbineau, M. (2010). Automatic modulation recognition using wavelet transform and neural networks in wireless systems. EURASIP Journal on Advances in Signal Processing. 2010(1);1-13. https://doi.org/10.1155/2010/532898.
Zhang, H., Bi, G., Razul, S. G., & See, C. M. S. (2014). Supervised modulation classification based on ambiguity function image and invariant moments. In Proceedings of IEEE International Conference on Industrial Electronics and Applications. IEEE. 1461-1465. https://doi.org/10.1109/ICIEA.2014.6931399.
Muralidharan, J. (2024). Advancements in 5G technology: Challenges and opportunities in communication networks. Progress in Electronics and Communication Engineering. 1(1);1-6. https://doi.org/10.31838/ece/01.01.01.
Al-Nuaimi, D. H., Hashim, I. A., ZainalAbidin, I. S., Salman, L. B., & Mat Isa, N. A. (2019). Performance of feature-based techniques for automatic digital modulation recognition and classification: A review. Electronics. 8(12);1407. https://doi.org/10.3390/electronics8121407.
Zhang, F., Luo, C., Xu, J., Luo, Y., & Zheng, F.-C. (2019). Deep learning based automatic modulation recognition: Models, datasets, and challenges. Digital Signal Processing. 129;1-19. https://doi.org/10.1016/j.dsp.2022.103650.
Majeed, A. (2019). Improving time complexity and accuracy of the machine learning algorithms through selection or highly weighted top k features from complex data sets. Springer Germany. 599-621. https://doi.org/10.1007/s40745-019-00217-4.
Meng, L.-L., & Si, X.-J. (2007). An improved algorithm of modulation classification for digital communication signals based on wavelet transform. In Proceedings of IEEE International Conference on Wavelet Analysis and Pattern Recognition. IEEE. 1226-1231. https://doi.org/10.1109/ICWAPR.2007.4421621.
Raktur, H., & Jea, T. (2024). Design of compact wideband wearable antenna for health care and Internet of Things system. National Journal of Antennas and Propagation. 6(1);40-48. https://doi.org/10.31838/njap/06.01.06
Hassanpour, S., Pezeshk, A. M., & Behnia, F. (2015). A robust algorithm based on wavelet transform for recognition of binary digital modulations. In Proceedings of 38th IEEE International Conference on Telecommunications and Signal Processing (TSP). IEEE. 508-512. https://doi.org/10.1109/TSP.2015.7296315.
Hamed, H. A., Sadkhan, S. B., & Hameed, A. Q. (2017). Proposed adaptive neural fuzzy inference system (ANFIS) identifier for M-ary frequency shift keying (FSK) signals with low SNR. In Shaping the future of ICT: Trends in information technology, communication engineering, and management (1st ed.). Taylor & Francis Group. 259–268. https://doi.org/10.1201/9781315155241-19.
Kumar, T. M. S. (2024). Security challenges and solutions in RF-based IoT networks: A comprehensive review. SCCTS Journal of Embedded Systems Design and Applications. 1(1);16-19. https://doi.org/10.31838/esa/01.01.04.
Chopra, S., Dhiman, G., Sharma, A., Shabaz, M., Shukla, P., & Arora, M. (2021). Taxonomy of adaptive neuro-fuzzy inference system in modern engineering sciences. Computational Intelligence and Neuroscience. 2021(1);1-14. https://doi.org/10.1155/2021/6455592.
Xiong, F. (2003). M-ary amplitude shift keying OFDM system. IEEE Transactions on Communications. 51(10);1638-1642. https://doi.org/10.1109/TCOMM.2003.818103.
Wei, X., & Cao, Z. (2005). Fast identification of amplitude modulated signals at low SNR. In Proceedings of IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications. IEEE. 2;119-1122. https://doi.org/10.1109/MAPE.2005.1618117.
Acharya, T., & Chakrabarti, C. (2006). A survey on lifting-based discrete wavelet transform architectures. VLSI Signal Processing Systems for Signal, Image and Video Technology. 42;321-339. https://doi.org/10.1007/s11266-006-4191-3.
Zhang, X., Deng, C., & Han, Y. (2013). The image space of meyer wavelet transform. In Proceedings of 2nd IEEE International Conference on Measurement, Information and Control. IEEE. 1136-1139. https://doi.org/10.1109/MIC.2013.6758159.
Jyothi, R. L., & Rahiman, M. A. (2017). Comparative analysis of wavelet transforms in the recognition of ancient grantha script. International Journal of Computer Theory and Engineering. 9(4);235-241. https://doi.org/10.7763/IJCTE.2017.V9.1144
Addison, P. S. (2017). The illustrated wavelet transform handbook (2nd ed.). Taylor & Francis Group.
Stolojescu, C., Railean, I., Moga, S., & Isar, A. (2010). Comparison of wavelet families with application to WiMAX traffic forecasting. In Proceedings of 12th IEEE International Conference on Optimization of Electrical and Electronic Equipment. IEEE. 932-937. https://doi.org/10.1109/OPTIM.2010.5510403.
Rahim, R. (2024). Scalable architectures for real-time data processing in IoT-enabled wireless sensor networks. Journal of Wireless Sensor Networks and IoT. 1(1);28-31. https://doi.org/10.31838/wsniot/01.01.07.
Radhi, M., & Tahseen, I. (2024). An Enhancement for Wireless Body Area Network Using Adaptive Algorithms. International Journal of Computational and Experimental Science and Engineering, 10(3). https://doi.org/10.22399/ijcesen.409
Nagalapuram, J., & S. Samundeeswari. (2024). Genetic-Based Neural Network for Enhanced Soil Texture Analysis: Integrating Soil Sensor Data for Optimized Agricultural Management. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.572
D, jayasutha. (2024). Remote Monitoring and Early Detection of Labor Progress Using IoT-Enabled Smart Health Systems for Rural Healthcare Accessibility. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.672
M. Devika, & S. Maflin Shaby. (2024). Optimizing Wireless Sensor Networks: A Deep Reinforcement Learning-Assisted Butterfly Optimization Algorithm in MOD-LEACH Routing for Enhanced Energy Efficiency. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.708
S, P., & A, P. (2024). Secured Fog-Body-Torrent : A Hybrid Symmetric Cryptography with Multi-layer Feed Forward Networks Tuned Chaotic Maps for Physiological Data Transmission in Fog-BAN Environment. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.490
Ponugoti Kalpana, L. Smitha, Dasari Madhavi, Shaik Abdul Nabi, G. Kalpana, & Kodati , S. (2024). A Smart Irrigation System Using the IoT and Advanced Machine Learning Model: A Systematic Literature Review. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.526
J. Anandraj. (2024). Transforming Education with Industry 6.0: A Human-Centric Approach . International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.732
Achuthankutty, S., M, P., K, D., P, K., & R, prathipa. (2024). Deep Learning Empowered Water Quality Assessment: Leveraging IoT Sensor Data with LSTM Models and Interpretability Techniques. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.512
N. Vidhya, & C. Meenakshi. (2025). Blockchain-Enabled Secure Data Aggregation Routing (BSDAR) Protocol for IoT-Integrated Next-Generation Sensor Networks for Enhanced Security. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.722
Alkhatib, A., Albdor , L., Fayyad, S., & Ali, H. (2024). Blockchain-Enhanced Multi-Factor Authentication for Securing IoT Children’s Toys: Securing IoT Children’s Toys. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.417
P. Jagdish Kumar, & S. Neduncheliyan. (2024). A novel optimized deep learning based intrusion detection framework for an IoT networks. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.597
Vutukuru, S. R., & Srinivasa Chakravarthi Lade. (2025). CoralMatrix: A Scalable and Robust Secure Framework for Enhancing IoT Cybersecurity. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.825
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