Enhanced Efficiency BPSK Demodulator Based on One-Dimensional Convolutional Neural Network | Zendy

Min Zhang | Zendy; Zongyan Liu | Zendy; Li Li | Zendy; Hai Wang | Zendy

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Enhanced Efficiency BPSK Demodulator Based on One-Dimensional Convolutional Neural Network

Author(s) -

Min Zhang,

Zongyan Liu,

Li Li,

Hai Wang

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2834144

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

In this paper, a novel binary phase shift keying demodulator based on 1-D convolutional neural network (1-D CNN) is proposed. The utilization of neural networks to detect the locations of phase shifts in the modulated data distinguishes the proposed scheme from other neural network demodulators, which decide the symbols corresponding to the sampled data in a symbol period. Meanwhile, coordinating with the symbol synchronization algorithm, the proposed structure is able to deal with the carrier frequency offsets and sampling frequency errors. Compared to the conventional demodulators, the proposed 1-D CNN demodulator presents better bit error rates performance.

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