Optimizing Cognitive Radio Networks with Deep Learning-Based Semantic Spectrum Sensing

Spectrum aggregation in 4G and 5G networks is a technique used to combine multiple frequency bands to boost communication performance. The cognitive radio feature improves the ability to combine spectrum in LTE and 5G environments by enabling dynamic spectrum sensing. Spectrum sensing is a major problem in spectrum aggregation due to the presence of various types of interference, such as noise. Phase noise is an issue due to its 1 MHz frequency offset experienced within 5G's 28 GHz operating band, with the distorted signal generating more spectrum sensing-related errors. To solve this problem, the proposed work suggests an optimized deep learning-based semantic spectrum sensing model using three sets of optimizers (ResNet-50, DeepLab V3 and sand cat) offering a high detection accuracy of 99.7% with the optimized training parameter of a high signal-to-noise ratio equaling 40 dB.