End-to-End PSK Signals Demodulation Using Convolutional Neural Network
Wenjie Chen, Jiao Wang, Jianqing Li · IEEE Access · 2022
Demodulation techniques are of central importance for achieving intelligent receiving. Improvement in demodulation performance enhances the overall performance of a communication system correspondingly. However, conventional demodulators require dedicated hardware platforms leading to high implementation costs and time-consuming development. This work proposes a unified architecture for end-to-end automatic demodulated modulated signals. The proposed demodulator utilizes the residual unit and fully convolutional network (R-FCN) to extract the time-domain feature of the modulated signal and determine the transmitted symbols to realize the demodulation of a received signal. Simulations show that the proposed method has better demodulation performance compared to existing methods. It is further demonstrated that when the signal-to-noise ratios (SNR) exceed 2dB, the proposed demodulator exhibits similar demodulation performance to symbol-unsynchronized data compared to conventional demodulators.