A Sparse Feature-Based Mixed Signal Frequencies Detecting for Unmanned Aerial Vehicle Communications

As drone technology develops rapidly and many users emerge in airspace networks, various forms of interference have caused the wireless spectrum to exhibit a dense, diverse, and dynamic trend. This increases the probability of spectrum conflicts among users and seriously impacts the quality and transmission rate of communication. How to effectively improve the detection accuracy of each frequency component in the electromagnetic space mixed signals and avoid spectrum conflicts will become one of the crucial issues currently faced by unmanned aerial vehicle (UAV) communication technologies. However, the existing methods overlook the mutual interference among the component signals as well as the noise during the frequency detection process, which affects their detection performance. In this paper, we propose a mixed-signal frequency detection method based on the reconstruction of sparse feature signals. Without information such as frequency range, bandwidth, and the number of components, it can utilize the autoencoder network to learn the sparse features of each component signal in the high-dimensional frequency domain space and construct a nonlinear reconstruction function to reconstruct each component signal in the mixed signal, thereby realizing the separation of signals. On this basis, complex dilated convolution and deconvolution are used successively to perform feature extraction on the separated signals, which enhances the receptive field and frequency resolution ability of the network for signals, reduces the interference between noise and different component signals, and realizes the accurate estimation of the number of components and carrier frequencies. The simulation results show that when SNR≥ 6 dB, the detection accuracy of the number of component signals is greater than 96.3%. The detection error and detection accuracy of component frequencies are less than 3.19% and greater than 90.7%, respectively.