Temporal Neural Network Intervention Detection and Response Strategies in Anti-Interference Satellite Communication Systems

Communication signals are easily affected by various interference factors during transmission, which often poses a threat to the performance and stability of satellite communication systems. To address this issue, this paper investigates the anti-interference detection and response strategies of satellite communication systems using temporal neural networks. This article studies common types of interference in satellite communication systems and develops an interference detection model using a temporal neural network. The model is used to model the signal in a temporal manner, and the powerful learning ability of the algorithm is utilized to achieve automatic interference detection. Corresponding response strategies can be designed based on the results of interference detection. The experimental results show that six types of signal interference were extracted, and the average accuracy of detecting these six types of interference in this paper is 94.04%. The use of temporal neural networks for anti-interference research in satellite communication systems can identify and analyze various interference signals, improve the system's anti-interference ability and stability.