NONE 20260504101151948 wseas/wseas content-registration-form+ja@crossref.org content-registration-form WSEAS TRANSACTIONS ON COMPUTERS 1109-2750 2224-2872 G. Indumathi Department of Electronics and Communication Engineering, Mecpo Schlenk Engineering College, Sivakasi, INDIA Logeshwaran J Department of Electronics and Communication Engineering, Mecpo Schlenk Engineering College, Sivakasi, INDIA Sathya Narayanan G Department of Electronics and Communication Engineering, Mecpo Schlenk Engineering College, Sivakasi, INDIA Modulation recognition represents one of the key fields in the wireless communications field, as it is widely used in both criminal and peaceful communication forms, following the application area. Nonetheless, things get hard when there is a drop in the signal quality, especially when we deal with the low SNR (across -10dB), providing us with such poor results. This is why we offer an enlistment of the Modulation Recognition System using a Denoising Bidirectional Recurrent Neural Network (DBRNN). The initial layer examines signal restoration, which is the role of the DBRNN (Denoising Bidirectional RNN), and the goal is to reduce the effect of noise. Next, an autoencoder is used as the second phase of the system, where the signal is decomposed into the information without noise. The procedure in question involves transmission and then finally, a noiseless deletion. Finally, we use three stages of the Convolutional Neural Network (CNN) modules for real classification of the noise-reduced output of the autoencoder unit. By means of the application of our method, we can look for the modulations, spectral features or speech patterns within the noisy audio signals, which are very useful for cognitive radio systems and many other applications. The detection algorithm simulations that we conducted evidenced that our solution was showing a growing tendency to increase the efficiency of the recognition accuracy. Subsequent inspection of the diverse design with various structural groups and frameworks of the parameters helps in achieving an accuracy of 85% for classification, even for -20 dB signals , thus proving its sturdiness and efficiency. 12 31 2025 12 31 2025 309 33 https://creativecommons.org/licenses/by/4.0/deed.en_US 10.37394/23205.2025.24.33 https://wseas.com/journals/computers/2025/a665105-032(2025).pdf Du, R., Liu, F., Zhang, L, Ji Y., Xu J., Gao F., Modulation Recognition Based on Denoising Bidirectional Recurrent Neural Network. Wireless Pers Commun 132, 2437–2455 (2023). https://doi.org/10.1007/s11277-023-10725-5 E. Moser, M. K. Moran, E. Hillen, D. Li and Z. Wu, "Automatic modulation classification via instantaneous features," 2015 National Aerospace and Electronics Conference (NAECON), Dayton, OH, USA, 2015, pp. 218- 223, doi: 10.1109/NAECON.2015.7443070. S. Aer, M. Diao, X. Zhang and X. Zhang, "Automatic Modulation Classification Strategy Based on Novel Feature Processing," 2022 7th International Conference on Signal and Image Processing (ICSIP), Suzhou, China, 2022, pp. 74-80, doi: 10.1109/ICSIP55141.2022.9886359 Y. Wang, M. Liu, J. Yang and G. Gui, "DataDriven Deep Learning for Automatic Modulation Recognition in Cognitive Radios," in IEEE Transactions on Vehicular Technology, vol. 68, no. 4, pp. 4074-4077, April 2019, doi: 10.1109/TVT.2019.2900460. OSHEA T J, WEST N, “Radio machine learning dataset generation with GNU radio”, Vol.1, No.1, Proceedings of the GNU Radio Conference, USA (2016). Z. Qu, C. Hou, C. Hou, and W. Wang, “Radar signal intra pulse modulation recognition based on convolutional neural network and deep QLearning network”, IEEE Access, Vol.8, 49125– 49136(2020). DOI: 10.1109/ACCESS.2020.2980363. Yu W, Miao L, “Data driven deep learning for automatic modulation recognition in cognitive radios”, IEEE Transactions on Vehicular Technology. 68(4), 4074-4077(2019). doi: 10.1109/TVT.2019.2900460. J Shi, S Hong, C Cai, Y Wang, H Huang, G Gui, “ Deep Learning-Based Automatic Modulation Recognition Method in the Presence of Phase Offset”, IEEE Access, 8, 42841 – 42847(2020). doi: 10.1109/ACCESS.2020.2978094 W Shi, D Liu, X Cheng, Y Li, Y Zhao, “Particle swarm optimization-based deep neural network for digital modulation recognition”, IEEE Access. 7, 104591-104600(2019) doi: 10.1109/ACCESS.2019.2932266. Ivan A, Jafar W, Abdul Sadah, Thamir R. “Recognition of QAM signals with low SNR using a combined threshold algorithm”, IETE Journal of Research. 61(1), (2015). Swami A, Sadler B M, “Hierarchical digital modulation classification using cumulants”, IEEE Transactions on Communications. 48(3), 416-429(2000) doi: 10.1109/26.837045. Ali AK, Erçelebi E. Algorithm for automatic recognition of PSK and QAM with unique classifier based on features and threshold levels. ISA Trans. 2020 Jul;102:173-192. doi: 10.1016/j.isatra.2020.03.002. Epub 2020 Mar 5. PMID: 32169291