An Efficient Integrated Radio Detection and Identification Deep Learning Architecture

The detection and identification of radio signals play a crucial role in cognitive radio, electronic reconnaissance, noncooperative communication, etc. Deep neural networks have emerged as a promising approach for electromagnetic signal detection and identification, outperforming traditional methods. Nevertheless, the present deep neural networks not only overlook the characteristics of electromagnetic signals but also treat these two tasks as independent components, similar to conventional methods. These issues limit overall performance and unnecessarily increase computational consumption. In this paper, we have designed a novel and universally applicable integrated radio detection and identification deep architecture and corresponding training method, which organically combines detection and identification networks. Furthermore, we extract signal features using only one-dimensional horizontal convolution based on the characteristics of the impact of wireless channels on time-domain signals. Experiments show that the proposed methods perform signal detection and identification more efficiently, which can not only reduce unnecessary computational consumption but also improve the accuracy and robustness of both detection and identification simultaneously. More specifically, the ability to distinguish different modulated signal categories tends to increase with the rise in SNRs, and the upper limit of detection accuracy can exceed 95% at SNRs above 0 dB. The proposed method can improve both signal detection and identification accuracy from 83.44% to 83.56% and from 61.27% to 62.32%, respectively.