Optimized Spectrum Sensing Algorithm Using ML

Author(s)	Mr. Santhosh G, Mr. Teja Vardhan G, Prof. Ms. Ms.Shamini.G.I G I
Country	India
Abstract	This research enhances spectrum sensing in cog- nitive radio networks using machine learning. We evaluate models like KNN, SVM, LR, RF, CNN, MLP, and LSTM for detecting primary user signals under varying signal-to-noise ratio (SNR) conditions. Feature extraction techniques such as energy detection, differential entropy, geometric mean, and log power improve classification accuracy. Experimental results show that CNN and LSTM perform well in low-SNR environments, while Random Forest offers robust performance across conditions. This study contributes to optimized spectrum utilization for telecommunications, IoT, and radar applications.
Field	Engineering
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-04-07
DOI	https://doi.org/10.36948/ijfmr.2025.v07i02.40605
Short DOI	https://doi.org/g9dnbp

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International Journal For Multidisciplinary Research