Cho Hyung Joon, Varughese Siddharth, Lippiatt Daniel, Desalvo Richard, Tibuleac Sorin, Ralph Stephen E
Opt Express. 2020 Oct 12;28(21):32087-32104. doi: 10.1364/OE.406294.
We experimentally demonstrate accurate modulation format identification, optical signal to noise ratio (OSNR) estimation, and bit error ratio (BER) estimation of optical signals for wavelength division multiplexed optical communication systems using convolutional neural networks (CNN). We assess the benefits and challenges of extracting information at two distinct points within the demodulation process: immediately after timing recovery and immediately prior to symbol unmapping. For the former, we use 3D Stokes-space based signal representations. For the latter, we use conventional I-Q constellation images created using demodulated symbols. We demonstrate these methods on simulated and experimental dual-polarized waveforms for 32-GBaud QPSK, 8QAM, 16QAM, and 32QAM. Our results show that CNN extracts distinct and learnable features at both the early stage of demodulation where the information can be used to optimize subsequent stages and near the end of demodulation where the constellation images are readily available. Modulation format identification is demonstrated with >99.8% accuracy, OSNR estimation with <0.5 dB average discrepancy and BER estimation with percentage error of <25%.
我们通过实验证明，利用卷积神经网络（CNN）可对波分复用光通信系统的光信号进行准确的调制格式识别、光信噪比（OSNR）估计和误码率（BER）估计。我们评估了在解调过程中两个不同点提取信息的益处和挑战：定时恢复后立即提取以及符号解映射前立即提取。对于前者，我们使用基于三维斯托克斯空间的信号表示。对于后者，我们使用由解调符号创建的传统I-Q星座图。我们在32-Gbaud QPSK、8QAM、16QAM和32QAM的模拟和实验双偏振波形上演示了这些方法。我们的结果表明，CNN在解调早期（此时信息可用于优化后续阶段）和解调接近尾声（此时星座图已可得）都能提取出独特且可学习的特征。调制格式识别的准确率超过99.8%，OSNR估计的平均差异小于0.5 dB，BER估计的百分比误差小于25%。
