Code Type Identification and Parameter Estimation of Satellite Jamming Channel Based on Machine Learning

The interference signal in satellite communication may seriously affect the efficiency and stability of data transmission. In order to solve the interference problem, it is necessary to encode the satellite interference channel and estimate its parameters. Traditional coding and parameter estimation methods have weak adaptability and robustness, and cannot effectively deal with complex interference situations. As an effective tool, machine learning has been widely used in the field of signal processing, which can realize adaptive identification and parameter estimation of interference channel coding types and improve data transmission efficiency and stability. In this paper, machine learning is used to adaptively identify the coding type of satellite jamming channel, and the accuracy of identification is improved by parameter estimation method. The comparative experimental results show that the application of machine learning algorithm in satellite jamming channel coding type identification and parameter estimation has achieved remarkable results in improving data transmission efficiency and stability. Compared with the traditional fixed coding method, machine learning can improve the transmission efficiency by about 4.76%. The results highlight the importance of machine learning in coding type identification and parameter estimation of satellite jamming channels, and provide new ideas and directions for the development of satellite communication technology.