Intelligent Recognition And Detection Of Wireless Signal In Mine Complex Environment

Wireless communication in mine roadways is an important technical means for coal mine safety production and mine emergency rescue.Reliability of wireless communication in complex mine environment is the key to coal mine safety production.With the promotion of green mining in coal mines and the construction of smart mines,underground wireless communication system is deployed on a large scale.With the rapid increase of underground wireless business demand,the problem of heterogeneous network,intelligent access of multi-service terminals and integration of various information systems become increasingly prominent.The identification and detection of underground radio signal is the key to solve this problem.The transmission of mine wireless signal is transmitted through antenna after channel coding,modulation and beam shaping.Due to the complex mine wireless signal propagation environment,radio frequency damage,channel fading,noise,vibration and other factors,the signal arriving at the receiver will have serious interference and fading,and the receiver will have a high error rate in the process of signal recognition and information recovery detection.Therefore,how to improve the recognition rate of wireless signals and the accuracy rate of information detection in complex mine environment,reduce the error rate of mine wireless communication system,and improve the reliability of transmission is the focus of this dissertation.The main contents and achievements of this dissertation are as follows:(1)In view of the difficulties and low recognition rate of multi-mode wireless signals in complex coal mine environment,this dissertation puts forward a mine multi-mode signal recognition algorithm based on high-order cumulants.Feature parameter extraction uses feature extraction algorithm based on high-order cumulative variables,and classifier design uses decision tree and genetic optimization support vector machine and deep neural network,respectively.The simulation results show that the correct recognition rate of multi-mode signals and the average recognition rate of all signals based on high-order cumulative and deep neural network are better than decision tree and genetic optimization support vector machine classification algorithm.The high-order cumulative algorithm model is simple,requires fewer data samples,and has a high timeliness.(2)In view of the problems that the selection of high-order cumulative value and characteristic parameters has a great influence on the recognition rate of multimode signals in coal mine environment and the poor recognition rate of signals under low signal-to-noise ratio,this dissertation uses complex analysis wavelet transform to extract the envelope and phase characteristics of signals,and constructs the recognition characteristics of multimode signals in coal mine.In order to enhance the recognition of spatial and temporal features of IQ signal data and eliminate carrier phase offset(PO)of OFDM signal,the parameters of convolution neural network(CNN)and long-term and short-term memory neural network(LSTM)network are adjusted and optimized,and a composite neural network(CLDNN)suitable for mine multimode signal recognition is designed.The simulation results show that the recognition rate of mine multimode signals based on CLDN is significantly better than that of single network models such as CNN and LSTM,in which the recognition rate of OFDM signals reaches 90%when the signal-to-noise ratio is 0 dB.(3)In order to solve the problem of high computational complexity,upper limit of recognition rate affected by characteristics and low recognition rate under low signal-to-noise ratio of mine multimode signal recognition based on high-order cumulative and wavelet transform,this dissertation combines IQ format data with its amplitude-phase format data as input of deep neural network to increase the signal’s characteristic quantity.The mechanism of learning and reasoning is established by using the active learning signal characteristics of deep learning model.With the increase of signal types and the requirement of high accuracy for signal detection and recognition,this dissertation optimizes ResNet by increasing residual blocks and convolution layer filter size to increase the "width" of residual neural network(ResNet)to improve the detection and recognition accuracy of mine multimode signals.The center loss function and the softmax loss function are improved to form a mixed loss function.DenseNet is optimized by training and adjusting the parameters according to the input data.The results show that the recognition rate of multi-mode signals in mine fading environment and the detection accuracy rate of data flow of mine TD-SCDMA and TD-LTE wireless signals are significantly improved.The detection accuracy rate of mine TD-SCDMA and TD-LTE signals based on DenseNet is over 91%when the signal-to-noise ratio is-5dB.In view of the problems that the current receiver performance is greatly affected by the mine environment interference,poor reception performance and high error rate,this dissertation puts forward for the first time the mine intelligent receiver model.This model uses a deep neural network to replace the end-to-end receiving process of the current receiver as a whole,and recovers the whole information from IQ signal to the original information bit stream.This deep neural network receiver is referred to as Deep Receiver in this dissertation.The depth receiver uses a densely connected neural network structure,uses global pooling to accommodate different input signal lengths,and uses multiple binary classifiers to recover multiple bits of information flow.The simulation results show that,in AWGN channel,the performance of the proposed smart receiver is greatly improved for OFDM receivers with different modulation modes and MIMO receivers with different antenna modes.In the mine Nakagami channel,the performance of the MIMO receiver proposed in this dissertation is improved with different attenuation coefficient,different modulation and encoding modes,different MIMO modes and different number of data loss.