摘要翻译：
提出了一种高斯噪声(GN)模型，该模型能正确地描述沿链路的任意频率相关信号功率分布。这使得评估通道间受激拉曼散射(ISRS)对光学克尔非线性的影响成为可能。此外，可以考虑频率相关的光纤衰减，并且可以模拟使用混合放大方案的传输系统，其中分布拉曼放大部分应用于光学光谱。为了包括后两种情况，必须数值求解一组耦合的常微分方程，以获得信号功率分布，从而得到一个半解析模型。然而，对于集总放大和光纤衰减变化很小的情况，本文提出了一种不太复杂的完全解析模型，称为ISRS GN模型。对于高斯调制信号，该模型精确到一阶，并通过数值分步仿真得到了广泛的验证。仿真结果与ISRS GN模型的非线性干扰功率最大偏差为0.1$~dB。将该模型应用于一个光带宽为10THz的传输系统，该系统代表整个C+L频段。在最佳发射功率下，ISRS引起的非线性干扰功率变化高达2$~dB。此外，文献中发表的可比模型与ISRS GN模型进行了比较。
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英文标题：
《The Gaussian Noise Model in the Presence of Inter-channel Stimulated
  Raman Scattering》
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作者：
Daniel Semrau and Polina Bayvel
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最新提交年份：
2017
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  A Gaussian noise (GN) model is presented that properly accounts for an arbitrary frequency dependent signal power profile along the link. This enables the evaluation of the impact of inter-channel stimulated Raman scattering (ISRS) on the optical Kerr nonlinearity. Additionally, the frequency dependent fiber attenuation can be taken into account and transmission systems that use hybrid amplification schemes can be modeled, where distributed Raman amplification is partly applied over the optical spectrum. To include the latter two cases, a set of coupled ordinary differential equations must be numerically solved in order to obtain the signal power profile yielding a semi-analytical model. However for lumped amplification and negligible variation in fiber attenuation, a less complex and fully analytical model is presented which is referred to as the ISRS GN model. The derived model is exact to first-order for Gaussian modulated signals and extensively validated by numerical split-step simulations. A maximum deviation of $0.1$~dB in nonlinear interference power between simulations and the ISRS GN model is found. The model is applied to a transmission system that occupies an optical bandwidth of $10$~THz, representing the entire C+L band. At optimum launch power, changes of up to $2$~dB in nonlinear interference power due to ISRS are reported. Furthermore, comparable models published in the literature are benchmarked against the ISRS GN model.
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PDF链接：
https://arxiv.org/pdf/1801.0246