Communication Signal Recognition Technique Based on Fusion Deep Belief Network
Na Zhao, Zixiong Liu · 2019
The increasingly complex electromagnetic environment and limited spectrum resources pose serious challenges for the recognition of communication signal. The technique of manually feature extraction no longer meets the actual needs. In order to solve the problem of high error rate of communication signal recognition under low Signal-to-Noise Ratio (SNR), we propose a recognition technique that can recognize eight types of modulations. First, we propose Fusion Deep Belief Network (FDBN) that extracts shape features while extracting the essential features of the signal by self-training. Furthermore, considering the problem that the features extracted by FDBN at low SNR are susceptible to noise, we explore the denoising technique based on Variational Mode Decomposition (VMD). The simulation results show that when the SNR is 0dB, the overall recognition rate of the proposed algorithm reaches 98.78%. The proposed recognition algorithm can also be used to recognize the modulation of radar signal.