Multi-antenna intelligent spectrum sensing in the presence of non-Gaussian interference

Abstract

The secondary user (SU) only detects one feature of the primary user (PU) signal in conventional spectrum sensing. However, single feature detection does not fully explore the PU information. Meanwhile, non-Gaussian interference will also affect the spectrum sensing performance. In this paper, we propose a novel spectrum sensing method using multimodal fusion and convolutional neural networks (CNN). First, the received signal at multiple antennas is preprocessed, where the generalized covariance matrix and the generalized Wigner-Ville distribution are used to characterize two different modes of the received signals as the input of CNN. Then, a CNN model using multimodal fusion is constructed. In this model, the attention mechanism is first used to extract features of different modalities, and the multimodal fusion technique fuses the extracted features to obtain the feature vector. Finally, the detection statistics and detection thresholds for spectrum sensing are constructed and compared using the output feature vectors of CNN. Simulation results show that the performance of multi-mode fusion spectrum sensing is better than the single-mode counterpart. This method can effectively realize spectrum sensing with both Gaussian noise and non-Gaussian interference, and it has a high detection probability at low SNR.