Spectrum Sensing via Residual Dilated Network and Horizontal Shift Attention for Cognitive IoT

With the continuous growth of Internet of Things (IoT) deployments, various wireless devices and communication technologies coexist in industrial environments resulting in a crowded and dynamic frequency spectrum. Efficient spectrum sensing becomes essential to mitigate interference, enhance the communication reliability, and ensure the seamless coexistence of diverse wireless technologies. However, the extremely dense and varied signals bring challenges for the precise detection, estimation, and recognition of signals in complex and varied signal environments. The residual dilated network (RDN) and horizontal shift attention (HSA) mechanisms presented in this article offer innovative solutions to the challenges posed by this intricate spectrum landscape. Through multiscale dilated convolution and attention mechanisms, our approach aims to capture and locate the signals precisely, enabling enhanced spectrum utilization within IoT applications. Extensive experiments are conducted on the two data sets, and the results show that our proposed method can automatically extract discriminative features of signals, thereby improving the detection accuracy and recall rate in spectrum sensing. In addition, the probability of false negatives and the inference time can also be reduced simultaneously.