Spectrum Sensing Using CNN With Attention on Switch of Channel States

Abstract

This work addresses the issue of spectrum sensing with random arrival and departure of primary signals. We first design a convolutional neural network (CNN) with outputs as the posterior probabilities of the arrival and departure of primary signals, leading to a CNN-based detector with the ratio of the posterior probabilities (i.e., the outputs of the CNN) as a test statistic. To further enhance the attention of the network on the switch feature of channel states, we design a switch attention module (SAM) that adaptively weights the received signals. Replacing the convolution plus maximum pooling block in the CNN detector with the SAM block leads to an SAM-CNN detector. Simulations show that the proposed CNN detector significantly outperforms existing detectors, and further improvement of detection probability by 19% is achieved by the SAM-CNN detector.