Spectrum Sensing for Noncircular Signals Using Augmented Covariance-Matrix-Aware Deep Convolutional Neural Network

This work investigates spectrum sensing in cognitive radio networks, where multi-antenna secondary users aim to detect the spectral occupancy of noncircular signals transmitted by primary users. Specifically, we propose a deep-learning-based spectrum sensing approach using an augmented covariance-matrix-aware convolutional neural network (CNN). The core innovation of our approach lies in employing an augmented sample covariance matrix, which integrates both a standard covariance matrix and complementary covariance matrix, thereby fully exploiting the statistical properties of noncircular signals. By feeding augmented sample covariance matrices into the designed CNN architecture, the proposed approach effectively learns discriminative patterns from the underlying data structure, without stringent model constraints. Meanwhile, our approach eliminates the need for restrictive model assumptions and significantly enhances the detection performance by fully exploiting noncircular signal characteristics. Various experimental results demonstrate the significant performance improvement and generalization capability of the proposed approach compared to existing benchmark methods.