Machine Learning-based RF Signal Classification for Cognitive Radio Networks: A Deep Learning Approach
Keywords:
Automatic modulation classification, Dynamic spectrum access, RF, Signal classification, Signal-to-Noise ratioAbstract
Machine Learning (ML) has become an essential enabler for improving the intelligence and adaptability of Cognitive Radio Networks (CRNs), especially in environments where spectrum availability is limited and highly dynamic. This study suggests a deep learning-driven structure for Radio Frequency (RF) signal classification to enhance spectrum sensing and decision-making processes within CRNs. Conventional signal classification methods largely depend on manually engineered features and prior domain knowledge, which restrict their effectiveness in complex and noisy conditions. The suggested approach uses DNNs, especially CNNs, to automatically extract discriminative and significant features from raw In-phase and Quadrature (I/Q) signal data to overcome these limitations. A variety of RF datasets with different modulation formats and SNR levels are used to train and test the model. To increase the model’s resilience and capacity for generalisation, preprocessing techniques including data augmentation and standardisation are used. According to experimental results, the proposed framework performs well even in noisy situations and provides superior classification accuracy, making it appropriate for real-time applications. When it comes to processing high-dimensional RF data, deep learning algorithms outperform more conventional ML techniques like Support Vector Machines (SVMs) and K-Nearest Neighbours (KNN). Moreover, integrating the proposed classification system into CRNs facilitates better spectrum utilisation, reduces interference, and supports dynamic spectrum access. Overall, this work highlights the potential of deep learning-based RF signal classification to significantly improve the efficiency and reliability of cognitive radio systems, offering a scalable solution for future wireless communication challenges.
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