用卷积神经网络实现混沌通信中的参数调制解调，准确率达88%
Supervised machine learning based signal demodulation in chaotic communications
- 构建卷积神经网络模型解调混沌逻辑映射信号
- 在20 dB信噪比下，误差点率1.34%时准确率达88%
- 适合对混沌通信系统性能优化的研究者参考
混沌调制是一种高效的宽带通信方法，利用确定性混沌生成伪随机载波。混沌分岔参数调制是其中一种成熟且广泛应用的技术。本文提出基于机器学习的分岔参数键控解调方法，设计了卷积神经网络结构，并针对混沌逻辑映射生成的信号给出了性能指标。研究评估了二进制信号的整体解调准确率，在信噪比为20 dB、加性高斯白噪声环境下，分岔参数偏差1.34%时，平衡数据集上的准确率达到0.88。
原文摘要 · Abstract (English)
A chaotic modulation scheme is an efficient wideband communication method. It utilizes the deterministic chaos to generate pseudo-random carriers. Chaotic bifurcation parameter modulation is one of the well-known and widely-used techniques. This paper presents the machine learning based demodulation approach for the bifurcation parameter keying. It presents the structure of a convolutional neural network as well as performance metrics values for signals generated with the chaotic logistic map. The paper provides an assessment of the overall accuracy for binary signals. It reports the accuracy value of 0.88 for the bifurcation parameter deviation of 1.34% in the presence of additive white Gaussian noise at the normalized signal-to-noise ratio value of 20 dB for balanced dataset.
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