Compact Spectrum Sensing With RPNet+CLRu: Reducing Model Size for 5G-LTE Signal Identification

Compact spectrum sensing (SS) is crucial for optimizing wireless network performance, particularly within dynamic 5 G NR and LTE environments. TThe effectiveness of traditional SS methods in real-world scenarios is often limited by challenges such as low signal-to-noise ratio, multipath fading, and high computational complexity. Deep learning-based approaches have emerged as promising solutions by offering superior adaptability and performance. However, existing DL models often suffer from computational costs and excessive storage requirements, thus constraining deployment on resource-constrained devices. To address these challenges, this paper introduces a novel Resolution-Preserving Network (RPNet), integrated with a Convolutional Layer Reuse (CLRu) technique, to enhance SS efficiency while reducing model size significantly. RPNet is designed to maintain the resolution of spectrogram input throughout its architecture, preserving critical spatial details for segmentation. CLRu optimizes memory usage by reusing convolutional layers, effectively reducing the number of trainable parameters with very slightly compromising performance. Extensive simulations on a synthetic 5G-LTE dataset demonstrate that RPNet+CLRu achieves a greater than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$9 \times$</tex-math></inline-formula> reduction in model size while maintaining 87.45% segmentation accuracy. In addition, the proposed model outperforms existing deep models in storage efficiency and computational feasibility, hence presenting it as a promising candidate for real-time SS in next-generation wireless networks.