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

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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