## 1 Introduction  
In the 6G communication field, the 6G network is able to realize the integration of cellular positioning and communication [1]. And the reliability of 6G communication systems is extremely important for these features. Yet, with the development of radio jamming technology, the 6G communication system is facing a more difficult challenge [2]. For example, in [3], the authors could destroy the availability of spectrum resources through radio jamming and then disrupt the packet transmission in 6G communication. Also, in [4], the authors proved a method that could interfere with the users' equipment and reduce the positioning accuracy by transmitting jamming signals. Jamming recognition technology could help the communication system to better cope with various electromagnetic jamming, strengthen the anti-jamming ability of the communication system, and maintain stability even in a harsh environment [5]. According to different classification methods, jamming recognition algorithms can be divided into traditional jamming recognition algorithms and deep-learning-based jamming recognition algorithms. The traditional jamming recognition algorithm works mainly through the extraction of jamming features, and then completes the jamming signal recognition through machine learning and other methods [6]. The extracted jamming features could be time-domain features, such as time-domain kurtosis, time-domain peak-to-average ratio, and time-domain compression gain; frequency-domain features, such as frequency-domain peak-to-average ratio, spectrum flatness index, and frequency-domain kurtosis; or transform-domain features, such as time-frequency line slope, compression gain, and waveform fractal dimension. The work in [7] proposed a jamming recognition algorithm based on Support Vector Machine (SVM). The algorithm extracted the moment coefficient of kurtosis in the frequency domain and the skewness of four kinds of jamming signals, and then sent the two coefficients to the SVM recognition model to complete the recognition of the jamming signals. In [8], genetic algorithm (GA) was used to optimize SVM classifiers. A GA-SVM was proposed which helped the jamming recognition rate reach 93.34%. The work in [9] established a mixed entropy dataset by extracting four different entropy features of three kinds of GPS jamming signals, and then used Random Forest (RF) to complete the classification and recognition of datasets. The experimental results proved that RF could guarantee the recognition rate of all three kinds of GPS jamming signals. In conclusion, even though the traditional jamming recognition algorithms have the advantage of low complexity, they still need users to manually extract jamming features. In addition, the feature compression of jamming signals is large. Such feature compression will lead to the loss of information, resulting in low accuracy of jamming recognition. In recent years, deep learning has played an extremely important role in the field of jamming recognition. Through convolutional neural network (CNN), the features of various jamming signals can be automatically extracted to complete the classification task. As shown in Figure 1, according to the difference of the extracted jamming features, jamming recognition algorithms based on deep learning could be divided into four categories: algorithms based on time-domain feature extraction; algorithms based on frequency-domain feature extraction; algorithms based on time-frequency-domain feature extraction; and algorithms based on joint-domain feature extraction. The jamming recognition algorithm based on time-domain feature extraction takes the time-domain sequence of the jamming signal as the algorithm input, and then processes the signal in the time domain, combining the original I path and the Q path, as well as the phase and the amplitude. The work in [10] designed two one-dimensional convolutional neural networks, and took the collected time-domain I/Q data as the input of the two neural networks. The authors fused the output of the two networks.