• VANAMA BHAVYA SRI
• TAMMISETTI HIMABINDU
• TATAKULA SURYA TEJA
• Sk. DIL AFROZ
• E. VARGIL VIJAY

This paper is about LSTM with ADAGRAD & ADAM optimizers which uses deep learning techniques to provide a unique method of spectrum sensing in cognitive radio networks. Conventional technique’s performance is less because they are unable to adjust to changing radio frequency environments. Our approach enables real-time spectrum detection by LSTM using ADAGRAD Optimizer within the Jupyter Notebook environment of Python. We have evaluated its effectiveness against conventional methods by extensive experimentation with RF dataset, showing notable increases in accuracy and adaptability. Our results point to the potential of deep learning to reduce interference and scarcity of spectrum, improving network performance and spectrum usage. We also discuss about potential possibility for future study, such as examining scalable large- scale cognitive radio networks and advanced structures. In addition to advancing cognitive radio technology and spectrum sensing, this research has applications for next-generation wireless communication systems.

"EFFECTIVE DATA TRANSMISSION USING SPECTRUM SENSING FOR WIRELESS COMMUNICATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.k201-k206, April-2024, Available :http://www.jetir.org/papers/JETIR2404A27.pdf

"EFFECTIVE DATA TRANSMISSION USING SPECTRUM SENSING FOR WIRELESS COMMUNICATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppk201-k206, April-2024, Available at : http://www.jetir.org/papers/JETIR2404A27.pdf