Research On Modeling And Detection Methods Of Interfering Signal Features Based On Eye Diagram Analysis

With the development of the information age,people have more and more demands for wireless communication,and the communication environment has become more and more complex.In the process of wireless communication,there will be some intentional or unintentional interference,which affect quality of normal communication.In order to ensure the quality of normal communication,it is necessary to accurately detect and identify the interference existing in the wireless communication system in order to take corresponding anti-interference measures.For the environment where the useful communication signals and interference signals exist at the same time,this paper studies the detection and recognition methods of interference signals based on eye diagram analysis.The main research contents are as follows:(1)An eye diagram quantization method based on Gaussian weight matrix mapping is proposed.For the coexistence conditions of useful communication signals and interference signals,the eye diagram is used to reflect the interference situation of the communication system,and the formation process and parameters of the eye diagram are analyzed.In order to facilitate the subsequent extraction of the characteristic parameters of the eye diagram,an eye diagram quantization method of weight matrix mapping is proposed.In this article,we choose to use Gaussian weight mapping.The simulation results show that compared with the traditional direct mapping method,the Gauss weight mapping method can get clearer texture in the eye image matrix.(2)An interference detection algorithm based on the texture feature of the eye pattern is proposed.The algorithm uses the eye diagram characteristics of different interference signals,and combined with the relevant knowledge of image processing,selects the entropy of the texture feature of the received signal's eye diagram to construct test statistics,and finished the detection of interference signals.The texture feature has a certain degree of noise resistance and stability,which increases the tolerance to background noise.The simulation experiment results show that the algorithm can effectively detect common interference signals,including noise modulation interference,single and multi-tone interference,and chirp interference under low interference signal ratio.(3)An interference recognition algorithm based on eye diagram and convolutional neural network is proposed.The algorithm designs a convolutional neural network framework based on the eye diagram characteristics,and presents different shape characteristics on the eye diagram of the communication signal according to different interference signals.The two-dimensional eye diagram matrix is used as the input of the convolutional neural network,and the eye diagram matrix is extracted features by the network,so as to realize the identification and classification of interference signals.The simulation results show that under low interference signal ratio,the algorithm can effectively recognize the six common interference signals,and has higher recognition accuracy rates than traditional interference recognition methods.