Abstract
In this paper, a new Separable Attention Capsule Network (SACN) is proposed for signal classification. SACN is a light-weight network composed of multi-channel separable convolution layer, attention module and classification layer. First, depth-wise convolution is employed to extract features of signals in a low-complexity manner, and the multi-channel network structure is designed to increase the network width to improve the diversity of features of signals. Then a channel attention module is followed by a capsule network whose element contains a group of neurons. This attention module can explore the interdependence among channels to use global information to selectively strengthen some important channels, thus achieving the improvement of generalization ability of SACN. Some experiments are taken on several datasets with communication and radar signals, and the comparison results prove the efficiency of SACN and the superiority to its counterparts.
Highlights
With the rapid development of radio technology, there are increasing types of radio signals and their classification has been an important topic in the field of signal processing [1], [2]
Early radar devices are relatively simple, and the signal classification mainly relies on some empirical parameters, such as Time of Arrival (TOA) and Pulse Repetition Frequency (PRF)
Some experiments are taken on several datasets with communication and radar signals, to validate our proposed Separable Attention Capsule Network (SACN), and the results are compared with its counterparts
Summary
With the rapid development of radio technology, there are increasing types of radio signals and their classification has been an important topic in the field of signal processing [1], [2]. Some time-frequency features are proposed for classification [3], [4], [6]–[8]. With the development of deep learning technology, Deep Neural Networks (DNNs) have been proposed to classify the time-frequency maps of signals [15]–[17]. Deep learning has shown its effectiveness in signal classification, most of the available methods have the following limitations: 1) They first exract the time-frequency features and input into DNNs, which can not well explore the capability of deep learning; 2) They often exhibit low classification accuracy when the Signal-Noice Ratio (SNR) is low, especially for negative SNR; 3) They can only classify signals with large differences, such as signals with different modulation types. A new Separable Attention Capsule Network (SACN) is proposed for signal classification.
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