用卷积神经网络解调混沌信号，能在噪声中准确识别未训练过的模式。
Demodulation of chaotic signals using convolutional neural network
- 用卷积神经网络学习混沌参数键控信号的解调特征。
- 在-13 dB信噪比下，误码率低至0.0819，性能稳定。
- 无需训练数据包含所有模式，具备强泛化能力，适合通信安全场景。
混沌调制是一种利用确定性混沌生成伪随机信号的有效通信技术。本文提出一种基于深度学习的混沌分岔参数键控信号解调方法，描述了卷积神经网络的架构，并评估了使用混沌逻辑映射生成信号的性能指标。研究测试了二进制信号的比特误码率，在分岔参数偏差1.34%、信噪比为-13 dB（归一化信噪比+20 dB）的加性高斯白噪声环境下，误码率为0.0819。结果表明，该方法即使在训练数据中未包含特定混沌模式的情况下，仍能有效检测混沌特征。
原文摘要 · Abstract (English)
Chaotic modulation is an effective communication technique that exploits deterministic chaos to produce pseudo-random signals. A widely adopted approach involves modulation of the chaotic bifurcation parameter. This paper introduces a deep learning-based demodulation method for keying of the bifurcation parameter. It describes the architecture of the convolutional neural network and evaluates performance metrics for signals generated using the chaotic logistic map. The study assesses the bit error rate for binary signals and reports a bit error rate of 0.0819 for a bifurcation parameter deviation of 1.34% under additive white Gaussian noise at a signal-to-noise ratio of -13 dB (corresponding to a normalized signal-to-noise ratio of +20 dB). The results demonstrate the capability to detect chaotic patterns even when the specific patterns were not included in the training dataset.
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