论文标题
多索子光谱振幅估计的连续特征值去除
Successive Eigenvalue Removal for Multi-Soliton Spectral Amplitude Estimation
论文作者
论文摘要
基于光学的非线性傅里叶转换系统需要对信号的非线性光谱进行准确估算，通常通过信号样本上的分段近似方法计算得出。我们提出了一种算法，称为连续的特征值去除，以改善多索氏脉冲的频谱估计。它利用了darboux变换的属性，该属性允许从非线性频谱中删除特征值。这会导致脉冲持续时间较小和带宽较小。删除信号的特征值后，频谱系数依次估计。作为一种有益的应用，我们表明该算法通过迭代降低脉冲持续时间来降低计算复杂性。
Optical nonlinear Fourier transform-based communication systems require an accurate estimation of a signal's nonlinear spectrum, computed usually by piecewise approximation methods on the signal samples. We propose an algorithm, named successive eigenvalue removal, to improve the spectrum estimation of a multi-soliton pulse. It exploits a property of the Darboux transform that allows removing eigenvalues from the nonlinear spectrum. This results in a smaller pulse duration and smaller bandwidth. The spectral coefficients are estimated successively after removing the eigenvalues of a signal. As a beneficial application, we show that the algorithm decreases the computational complexity by iteratively reducing the pulse duration.
