用神经网络提升弱信号下多频移键控的解调性能
Software defined demodulation of multiple frequency shift keying with dense neural network for weak signal communications
- 用简单全连接神经网络实现端到端解调
- 在-20dB至0dB信噪比下表现稳定
- 适合低信噪比通信系统设计参考
本文研究了弱信号数字通信系统的符号误码率和比特误码率性能。采用正交多频移键控调制方案，结合监督学习的神经网络解调方法，使用简单的全连接端到端人工神经网络。重点考察在平均信噪比为-20 dB至0 dB的加性高斯白噪声环境下，系统的抗干扰能力。
原文摘要 · Abstract (English)
In this paper we present the symbol and bit error rate performance of the weak signal digital communications system. We investigate orthogonal multiple frequency shift keying modulation scheme with supervised machine learning demodulation approach using simple dense end-to-end artificial neural network. We focus on the interference immunity over an additive white Gaussian noise with average signal-to-noise ratios from -20 dB to 0 dB.
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