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Communication modulation recognition algorithm based on STFT mechanism in combination with unsupervised feature-learning network

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Abstract

Aiming at the limitations of traditional communication modulation recognition algorithms, a novel recognition algorithm based on deep learning network far communication signal features is proposed in this paper. By introducing the two different computation mechanisms of STFT, two unsupervised feature-learning networks based on Restrict Boltzmann Machine (RBM) are respectively adopted for communication signal, where the network based on RBM is greatly improved in computing performance compared to the network based on convolutional Restrict Boltzmann Machine (CRBM), and greatly reduces the requirement for high-performance hardware in deep learning networks. In addition, as for signal modulation recognition and signal detection problems in communications, two modulation recognition networks are constructed by using the learning-to-STFT networks and the Back Propagation Neural Network (BPNN) classifier. Compared with the traditional modulation algorithms, our proposed algorithm in the paper can obtain better performance in the recognition accuracy, especially under the condition of low SNR.

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  1. School of Information Engineering, Yancheng Institute of Technology, Yancheng, 224000, Jiangsu, China

    Suqin Wu

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  1. Suqin Wu
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This article is part of the Topical Collection: Special Issue on Fog/Edge Networking for Multimedia Applications

Guest Editors: Yong Jin, Hang Shen, Daniele D'Agostino, Nadjib Achir, and James Nightingale

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Wu, S. Communication modulation recognition algorithm based on STFT mechanism in combination with unsupervised feature-learning network. Peer-to-Peer Netw. Appl. 12, 1615–1623 (2019). https://doi.org/10.1007/s12083-019-00807-2

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