论文标题
Wiener-Hammerstein模型及其针对光学发射器非线性数字前启动的学习
Wiener-Hammerstein model and its learning for nonlinear digital pre-distortion of optical transmitters
论文作者
论文摘要
我们提出了光学发射器组件的简单非线性数字前启动（DPD），它由有限脉冲响应（FIR）过滤器的串联块，无内存的非线性函数和另一个FIR滤波器组成。该模型是Wiener-Hammerstein（WH）模型，其结构与神经网络或多层感知基本相同。由于模型感知的结构并利用了机器学习领域中发达的优化方案，因此这种意识使人们能够实现复杂性有效的DPD。该方法的有效性通过电气和光学背靠背（B2B）实验进行评估，结果表明，WH DPD在信噪比（SNR）中提供了0.52 dB的增益（SNR），而光学调制器输出功率在固定SNR的固定SNR上，在固定的lin-On-On-On-On-On-On-On-On-On-On-On-On-On-On-On-On-On-On-On-Onal-nylly DPD中提供了0.52 dB的增益。
We present a simple nonlinear digital pre-distortion (DPD) of optical transmitter components, which consists of concatenated blocks of a finite impulse response (FIR) filter, a memoryless nonlinear function and another FIR filter. The model is a Wiener-Hammerstein (WH) model and has essentially the same structure as neural networks or multilayer perceptions. This awareness enables one to achieve complexity-efficient DPD owing to the model-aware structure and exploit the well-developed optimization scheme in the machine learning field. The effectiveness of the method is assessed by electrical and optical back-to-back (B2B) experiments, and the results show that the WH DPD offers a 0.52-dB gain in signal-to-noise ratio (SNR) and 6.0-dB gain in optical modulator output power at a fixed SNR over linear-only DPD.
