# Interference Signal Feature Extraction and Pattern Classification Algorithm Based on Deep Learning
## 1 Introduction  
With the rapid development of satellite communication, LEO satellite systems have become an important supplement to ground communication systems, which are very advantageous in supporting mobile communication, basic communication in remote areas and high-speed user access [1, 2]. LEO satellite systems can not only meet the access needs of the global non-popular Internet areas, but also promote industrial development and economic growth in various fields by integrating with 5G technology, Internet of Things, cloud data and smart cities [3, 4, 5]. In recent decades, with the continuous development of aerospace and communication technology, thousands of LEO satellite constellations, such as OneWeb and Starlink, have sprung up, which has greatly increased the number of non-geostationary orbit satellites declared in the International Telecommunication Union (ITU) [6, 7]. In order to improve the efficiency of spectrum utilization, the frequency bands used by LEO satellites will inevitably overlap with other satellites. There are different degrees of spectrum compatibility problems between LEO satellite constellations, LEO satellites and geostationary satellites, and LEO satellites and terrestrial communication systems. Therefore, it is necessary to perceive the characteristics of the space electromagnetic spectrum, search idle channels, identify enemy interference patterns, and build anti-interception communication links, so as to improve the robustness of space-based information support links [2, 8]. Aiming at the scene in cooperative communication with external interference, this paper studies the interference signal recognition algorithm. The interference signals considered in the passage include single frequency interference, chirp interference and hopping interference. The algorithm in this paper, which is based on the monitoring method of interference energy, carrier position and bandwidth of powerspectrum, can obtain the interference signal parameters and provide guidance for satellite-to-ground anti-interference communication link construction. In recent years, with the vigorous development of artificial intelligence algorithms, deep learning algorithms have also been applied in the field of signal detection [9; 10]. Convolutional Neural Networks (CNN), Recurrent Neural Network (RNN), Deep Belief Networks (DBN) and other methods have been used as signal classifiers. The data input into the classifier can be the original sampled data or the processed features, such as instantaneous time features, statistical characteristics, spectral correlation function features, etc. Literature [11] studies the modulation classification problem of distributed wireless spectrum sensing network, and proposes a new data-driven automatic modulation classification model based on long and short-term memory (LSTM). The model learns the classification from the time-domain amplitude and phase information of the signal. Literature [12] selects 21 statistical features of the signal that showed good separation and variability in the empirical distributions of all the considered modulation formats (i.e., BPSK, QPSK, 8 PSK, 16 QAM and 64 QAM) and inputs them into a fully connected neural network with three hidden layers. Literature [13] proposes an automated modulation classification method based on deep learning. The method combines spectral correlation function with deep belief nets, applied to pattern recognition and classification. In addition to inputting one-dimensional features, the original signal can also be transformed into image features, such as a constellation map or a time-frequency map. Deep learning has been effectively used in constellation-based automatic modulation classification methods.