# Interference Signal Feature Extraction and Pattern Classification Algorithm Based on Deep Learning
## 1 Introduction
Literature [14] proposes a low-complexity graphical constellation projection algorithm for automatic modulation classification, where the recovered symbols are projected into artificial graphic constellations; subsequently, deep belief networks are used to learn the underlying features in these constellations and identify their corresponding modulation schemes. Literature [15] presents an intelligent constellation graph analysis method to realize modulation format recognition and optical signal-to-noise ratio estimation using deep learning techniques based on convolutional neural networks. CNN has the ability of feature extraction and self-learning, and it can process constellations in the form of raw data (i.e., pixels of the image) from the perspective of image processing, without manual intervention. Short-Time Fourier Transform (STFT) is known as a classical time-frequency representation, and has been widely used for signal processing. Literature [16] proposes a hybrid radar and communication signal recognition and classification method based on a deep convolutional neural network for feature extraction and classification. The main idea of this method is to convert the modulated signal into a time-frequency map for recognition. To overcome the single-input single-output property of the CNN to output multiple labels of mixed signals, a repeated selection strategy is proposed that segment the time-frequency map and classify select regions by repeatedly utilizing deep convolutional neural networks. Literature [17] preprocesses time-domain signals into spectral waterfalls, describes the Long Term Evolution (LTE) uplink interference recognition problem as an image classification task, and designs a learning system based on a convolutional neural network to identify uplink interference. Literature [18] proposes a two-stage hybrid method combining short-time Fourier transform with convolutional neural networks. In the first stage, as a data source, the time-frequency information of these modulated signals has been extracted through STFT to obtain two-dimensional images. In the second stage, the obtained 2-D time-frequency information is used as input to the CNN algorithm to classify the modulation types. The main contributions of this paper are in the following:  
Firstly, only one deep learning network can obtain the predicted values of all parameters (including the style of the interference signal, start and ending time, frequency change range, capability and bandwidth), without additional network or computational overhead prediction parameters.In addition, the experiment proved that the method has excellent detection effect, the accuracy and recall rate of 5 types of interference signals are nearly 100%, the prediction error is less than 4 ms, and the prediction error is less than 6 KHz. Secondly, the monitoring bandwidth range reaches 2 MHz. Existing deep learning-based signal detection methods basically identify signal categories in a single channel. The present method can detect interference in any frequency band within 2 MHz, including single-tone interference at a single frequency point, and sweep interference and frequency hopping interference in any frequency range. In addition, it effectively solves the problem of large aspect ratio and large difficulty in detecting the change range. Due to the random duration and frequency change range of the signal, this will cause a large aspect of the signal on the time and frequency map and the change range, which causes great difficulties to the training of the network, and will lead to incomplete predictions or even unpredictable phenomena. This party has successfully solved the detection problem of long duration monotone interference and small slope interference, making the detection accuracy and recall rate close to 100%.