Research On Mine Wireless Signal Modulation Recognition Algorithm Based On Deep Learning

The development of mine informatization and intelligence is closely related to wireless communication technology.There are many different wireless communication systems in the mine,and the signal modulation methods used by each communication system are different.Therefore,to form a safe and reliable mine communication network system,it is necessary to realize the recognition of signal modulation modes between different communication systems.This dissertation studies the recognition method based on artificial extraction of signal features and the modulation recognition method based on deep learning for the mine wireless channel environment.This thesis identifies and classifies the BPSK,QPSK,8PSK,16PSK,16QAM,64QAM,256QAM,4PAM and GMSK modulation signals in mine fading channel.Modulation recognition algorithm based on signal feature extraction.In this thesis,by analyzing the relationship between the high-order cumulant of the signal and the small-scale Nakagami fading of the mine,the calculation expression of the high-order cumulant of the signal passing through the fading channel is derived.Then select the fourth,sixth and eighth-order cumulants as the characteristic parameters and construct the eigenvalue vector.A decision tree,a support vector machine(SVM)and a fully connected neural network are designed as target classifiers to realize the classification and recognition of nine kinds of modulation signals in the fading channel of the mine.The simulation results show that the recognition effect of decision tree classifier is worse than SVM and fully connected neural network classifier.The recognition performance of the classifier based on SVM and fully connected neural network is equivalent,but the recognition effect is not ideal under low signal-to-noise ratio,and it is difficult to distinguish 64QAM and 256QAM signals.Aiming at the shortcomings of traditional modulation recognition methods,this thesis further proposes a modulation recognition method based on deep learning.The models based on Convolutional Neural Networks,Long Short-term Memory Networks and CLDNN(Convolutional,Long Short-term Memory,Fully Connected Deep Neural Networks)are designed respectively.These network models are applied to the field of modulation recognition in the mine channel environment,and the CLDNN model with better performance is designed by separately studying the influence of the number of layers of CNN and LSTM models on the recognition rate.The simulation results show that compared with a single network model,the recognition rate based on the combined neural network model is higher.Compared with the modulation recognition method based on signal characteristics,the recognition rate under low signal-to-noise ratio is improved.This thesis further proposes an improved AM-CLDNN network model based on the attention mechanism,and tests the performance of the deep learning network model through training data sets of different sizes.The results show that the improved AM-CLDNN model based on the attention mechanism has the best effect.And it effectively improves the recognition performance under low signal-to-noise ratio.This shows that the combined use of the attention mechanism model and the deep learning algorithm can effectively improve the modulation signal recognition performance under complex channels.