Research On Performance Monitoring Of Geosynchronous Orbit Satellite Communication System

Nowadays,with the rapid growth of the amount of information and the excellent development of the Internet and big data,people’s high demand for communication quality and communication capacity is imminent.Satellite communication has entered people’s field of vision due to its advantages of wide coverage,little influence by terrain,and large beam coverage.The performance monitoring technology is an important basic guarantee for the safe and reliable operation of the geosynchronous orbit satellite communication system.In order to ensure the efficient operation of the system,how to effectively monitor the complex and changeable geosynchronous orbit satellite communication system,predict system risks,and diagnose and locate system faults have become key issues that need to be urgently solved.Performance monitoring can prevent communication system failures between satellite and ground,speed up fault location and system recovery,and avoid major losses.However,traditional performance monitoring relies on a large amount of manual experience for monitoring with the help of spectrum detectors.It is difficult to obtain information such as physical damage in the system,and the labor cost is high,the efficiency is low,and it is easy to cause inevitable human errors.Recently,deep learning,one of the core technologies in the field of artificial intelligence,has been widely used in performance monitoring in various scenarios,but it is rarely studied in satellite communications.Based on the powerful adaptive learning ability of deep learning,this paper focuses on the performance monitoring technology of geosynchronous orbit satellite communication system.The main research contents are as follows:Firstly,for the signal transmission process of the geosynchronous orbit satellite communication system,a Ka-band geosynchronous orbit satellite communication simulation system is built,which can simulate six different types of rain in the real atmospheric channel:sunny,dark clouds,frost,light rain,heavy rain,and heavy rain.decline.Four common satellite system damages,including system nonlinear damage,different weather conditions,I\Q imbalance and receiver thermal noise,are simulated under ten different modulation formats.Secondly,to address the problem that traditional performance monitoring relies on human experience and lacks accuracy,a scheme for intelligent damage monitoring using convolutional neural networks(CNNs)is proposed.In the constellation damage monitoring of Quadrature Phase Shift Keying(QPSK)signals transmitted by the geosynchronous orbit satellite communication system,the monitoring accuracy of multiple damage degrees under four different damage types is 99.14%.Thirdly,the traditional algorithm for modulation format recognition cannot realize the problem of automatic feature extraction.This paper proposes a modulation format monitoring scheme based on CNNs.In the monitoring of the constellation pattern modulation format of ten common signals in the geostationary orbit satellite communication,100%correct identification of the modulation format is achieved when the Signal Noise Ratio(SNR)is 8dB.In the case of SNR as low as-4dB,it can also achieve 90.5%correct identification of its modulation format modulation.Thus,it is verified that intelligent modulation format monitoring can be realized quickly and efficiently with the help of CNNs.