Research On Modulation Recognition Of Communication Signals Based On Deep Learning And Software Defined Radio

Modulation recognition is a key technology to ensure the reliability of software defined radio communication,and has important application value in the fields of electronic countermeasures,military communication reconnaissance and link adaptation.Due to the lack of current wireless spectrum resources and the diversification of modulation patterns,multiple types of modulation signals coexist in complex channels,and it is difficult for traditional modulation recognition algorithms to effectively classify received signals.In order to improve the effectiveness and practicability of the modulation recognition algorithm,this thesis combines the deep learning method to study the modulation recognition problem of communication signals under the software defined radio platform.The main contents are as follows:(1)Aiming at the problem that it is difficult for neural network to excavate key features of signal in complex electromagnetic environment,which leads to low recognition accuracy,a modulation recognition algorithm based on multi-attention mechanism network is designed.The algorithm extracts the amplitude and phase information in the signal preprocessing link,and forms dual-channel data with the I/Q sequence as input to obtain rich signal features.The important spatial features of the signal are learned by the residual dense block introducing improved convolutional attention mechanism,the output feature vector is fused and sent to the bidirectional gated recurrent unit to obtain temporal information,and the sequence attention mechanism is added to capture the key temporal features of the signal.The software defined radio platform GNU Radio is used to simulate nine kinds of modulation signals under smallscale fading channels for verification.The experimental results show that the proposed algorithm can effectively improve the recognition accuracy of high-order QAM and PSK signals.Compared with other deep learning algorithms,the proposed algorithm can achieve higher recognition accuracy when the signal-to-noise ratio is greater than-6dB.(2)In order to solve the problem of low recognition accuracy of communication signal modulation recognition by deep learning supervision algorithm under limited label data,a modulation recognition algorithm based on residual semi-supervised generative adversarial network is designed.The algorithm directly takes the I/Q sequence as input,replacing twodimensional convolutional layers of traditional semi-supervised generative adversarial networks with spectral normalized residual units,obtaining the multi-scale features of the signal and enhancing the stability of network training.The latent information in unlabeled data is excavated to improve the recognition accuracy under limited label data.In order to verify the practicability of the algorithm,two software defined radio hardware peripherals HackRF,and GNU Radio,are used to build a data collection platform to complete the transmission and reception of seven modulated signals in a real electromagnetic environment and prepare a measured data set.The experimental results show that the proposed algorithm outperforms deep learning supervised algorithms and other semi-supervised generative adversarial networks modulation recognition algorithms in recognition accuracy when the same amount of labeled data is provided.