Deep Learning-Based Spectrum Sensing Techniques for Cognitive Radio Networks

Spectrum sensing plays a pivotal role in Cognitive Radio (CR) systems by allowing secondary users toidentify and utilize unused spectrum bands without causing interference to licensed primary users. However,traditional sensing techniques often perform poorly under low signal-to-noise ratio (SNR) conditions andstruggle to adapt to rapidly changing environments. With the rise of deep learning (DL), spectrum sensinghas undergone significant transformation. DL models can autonomously extract meaningful patterns fromraw signal data, making them highly effective for this task. This paper explores the use of various DLarchitectures—such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LongShort-Term Memory (LSTM) networks, and Transformer models—for enhanced spectrum sensing. Itexamines their design, training methodologies, input feature strategies, and performance across differentSNR scenarios. The analysis demonstrates that DL-based methods considerably outperform conventionalapproaches, positioning them as essential tools for future intelligent and adaptive spectrum managementsystems.