用深度卷积网络实现弱信号软件解调，提升电力系统远程监测抗干扰能力。
Software demodulation of weak radio signals using convolutional neural network
- 基于卷积神经网络实现JT65A协议的弱信号软件解调。
- 在-30 dB至0 dB信噪比下，首次获得符号与比特误码率性能。
- 揭示协议抗干扰能力比理论极限低约1.5 dB，适合电力通信研究者参考。
本文提出将JT65A无线通信协议用于电力系统广域监测系统中的数据传输。研究采用深度卷积神经网络对以JT65A协议传输的多频移键控弱信号进行软件解调，并以符号误码率和比特误码率形式呈现解调性能。重点关注在加性高斯白噪声环境下，信噪比范围为-30 dB至0 dB时的抗干扰能力，该性能为首次获得。结果表明，该协议的抗干扰能力比非相干正交MFSK信号的理论极限低约1.5 dB。
原文摘要 · Abstract (English)
In this paper we proposed the use of JT65A radio communication protocol for data exchange in wide-area monitoring systems in electric power systems. We investigated the software demodulation of the multiple frequency shift keying weak signals transmitted with JT65A communication protocol using deep convolutional neural network. We presented the demodulation performance in form of symbol and bit error rates. We focused on the interference immunity of the protocol over an additive white Gaussian noise with average signal-to-noise ratios in the range from -30 dB to 0 dB, which was obtained for the first time. We proved that the interference immunity is about 1.5 dB less than the theoretical limit of non-coherent demodulation of orthogonal MFSK signals.
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