Deep Learning-based Microwave Signal Classification for Intelligent Spectrum Management: A Review
Keywords:
Cognitive radio networks, Convolutional neural networks, Deep learning, Microwave signal classification, Spectrum managementAbstract
The rapid expansion of wireless communication systems and the proliferation of connected devices have significantly increased demand for efficient spectrum utilisation. Microwave frequency bands, widely used in radar, satellite, and wireless communications, are becoming increasingly congested, necessitating intelligent spectrum management strategies. Deep Learning has emerged as a transformative method for analysing complex microwave signal environments and enabling automated decision-making. A thorough examination of deep learning-based microwave signal categorisation methods for intelligent spectrum management is provided in this review study. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long short-term memory (LSTM) networks, and hybrid models are among the deep learning architectures that are examined in this paper, and their efficacy in extracting significant characteristics from unprocessed microwave signals is highlighted. Different signal representations, such as in-phase and quadrature (I/Q) data, spectrograms, and time-frequency features, are also discussed. The review further explores key applications, including spectrum sensing, interference detection, modulation classification, and dynamic spectrum allocation in cognitive radio systems. Additionally, the paper identifies major challenges such as data scarcity, low Signal-to-Noise Ratio (SNR) conditions, computational complexity, and real-time deployment constraints. Emerging trends, including transfer learning, federated learning, and edge-based AI, are also examined as potential solutions. Comparative analysis of existing methods demonstrates that deep learning significantly outperforms traditional signal processing techniques in terms of accuracy and adaptability. In order to facilitate effective and intelligent spectrum management in next-generation wireless communication systems, this review attempts to give researchers and practitioners a comprehensive understanding of recent developments and future research directions in deep learning-based microwave signal classification.
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