# Efficient Open-Set Recognition for Interference Signals Based on Convolutional Prototype Learning
_Article_  
1 School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China;  
1 cow_1 [EMAIL_ADDRESS] (X.C. ); [EMAIL_ADDRESS] (X.Y.) [PERSON]; [PERSON]; [PERSON]; Efficient Open-Set Recognition for Interference Signals Based on Convolutional Prototype Learning. _Article_  
1 School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China;  
1 cow_1 [EMAIL_ADDRESS] (X.C. ); [EMAIL_ADDRESS] (X.Y.) [PERSON]; [PERSON]; [PERSON]; Efficient Open-Set Recognition for Interference Signals Based on Convolutional Prototype Learning. _Article_  
1 School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China;  
1 [EMAIL_ADDRESS] (X.C. ); [EMAIL_ADDRESS] (X.Y.) [PERSON]; [PERSON]. intelligent anti-interference communication in the future and has attracted the attention of many researchers. Most of the traditional interference recognition methods are composed of feature extraction and pattern recognition. The feature extraction module uses a signal high-order cumulant [2], signal space [3,4] and transform domain [5, 6, 7] to complete the signal features extraction. The pattern recognition module uses a decision tree, SVM, BP neural network and other methods in traditional machine learning to complete the interference class decisions. However, the performance of such methods depends on the rationality of the feature selection, which is related to the cognition of professionals toward interference signals, so human factors have great influence. In addition, the traditional interference recognition methods also have problems such as low recognition accuracy. With the development of deep learning, more and more interference recognition algorithms based on deep learning and convolutional neural networks (CNN) have been proposed [8, 9, 10, 11, 12, 13, 14]. Datasets produced by simple signal preprocessing can be used to obtain a very high recognition accuracy [15], which solves the problem of feature parameters selected artificially in traditional methods. However, the above recognition methods based on deep learning have a closed hypothesis, which is called closed-set recognition (CSR), i.e., the types of interference signals in the test phase appeared in the training phase. When a new type of interference appears, the existing methods will identify it incorrectly as one of the known types. Since the communication interference may be the malicious electromagnetic signals transmitted by non-partners, with the rapid development of science and technology, new interference will emerge continually and cannot be known in advance [1]. Therefore, the open-set recognition (OSR) of interference signals needs to be solved urgently. There have been few OSR works for wireless signals [1, 16, 17, 18, 19]. Refs. [1, 16] decoupled the known classes classification and the unknown classes rejection, which have good OSR performance under the background of low noise power, but their OSR performance decreases significantly under the background of high noise power. References [17, 18, 19] employed different methods to generate \"fake\" unknown class signals, which are used to train a network with known class signals together. However, their performance strongly depends on the \"fake\" unknown class signals. Recently, prototype learning methods have been used for OSR because of their high intraclass compactness and interclass separability of sample features and achieved good performance [20, 21, 22]. However, these methods cannot be directly applied to the OSR of noisy interference signals. Therefore, this paper proposes an OSR classifier for interference signals based on convolution prototype learning. The contributions of this paper are mainly as follows:  
* We propose a new hollow convolution prototype learning (HCPL).