Abstract
Automatic modulation recognition (AMR) is one of the essential parts in the intelligent communication system. In the underwater acoustic communication, it is a challenging work that promptly and easily recognizes the signal modulation schemes by conventional methods. The deep neural network method is a good solution to the problem, which creates a better recognition effect. The packets of data that are fed to the familiar neural network is constant. However, the packets of signal data on the communication course consistently change, which seriously reflects on the signal recognition veracity. A novel deep learning network with the sequence convolutional network in this paper is proposed, which is composed of one-dimensional sequence convolution of residual network modules and the variable convolution kernel range. By extracting the time-domain signal characteristics, the affection of various signal packets can be mitigated. In experiments, the employed network not only has more concentrated on the modulation recognition veracity, but also owns a lower parameter quantity and a shorter training time, which indicates ideal recognition results in the underwater communication environment. Moreover, it is more valuable to the real underwater communication system.
Highlights
The task of Automatic modulation recognition (AMR), often considered as the signal recognition, may mainly include classifying individual signal arguments of modulation schemes to identify the communication style, which is imposed between the transceivers on the application scene
As a comparatively new study field, the cognitive radio (CR) has found more real applications in the civilian context, which has been regarded as the concrete form of software defined radio (SDR) [3]
This paper considers the sequence convolutional network (SCNet), an innovative structure of one-dimensional sequence convolution (1DSC), for the underwater acoustic signal modulation recognition
Summary
The task of AMR, often considered as the signal recognition, may mainly include classifying individual signal arguments of modulation schemes to identify the communication style, which is imposed between the transceivers on the application scene. This paper considers the sequence convolutional network (SCNet), an innovative structure of one-dimensional sequence convolution (1DSC), for the underwater acoustic signal modulation recognition. SCNet NETWORK STRUCTURE Due to the special propagation carrier in underwater acoustic communication environment, it is tough to recognize the received signal modulation schemes. When network layers are accumulated continuously, common insurmountable problems make it extremely exhausting to train the deep network, which can be better addressed by the network structure optimization of SCNet. Communication signal dataset is a kind of the time sequence data, and the signal data before and after have the inevitable correlation. Through the multi-layer network structure, a large enough receptive field can be obtained to extract more signal data information to achieve better results.
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