摘要翻译：
利用数字信号处理来抑制非线性已被证明可以提高光纤传输链路的可实现数据速率。一种特别有效的技术是数字反向传播(DBP)，一种能够同时补偿线性和非线性信道失真的算法。然而，实现这一技术的最大障碍是它的高计算复杂度。近年来，已经有几种计算复杂度降低的DBP替代方案被提出，尽管这些技术没有显示出与实现复杂度相称的性能优势。为了充分表征DBP的计算要求，需要对数字相干接收机中所使用的逻辑约束下的算法行为进行建模。这样的模型允许根据真正的硬件复杂度来分析任何信号恢复算法，其中最重要的是包括乘法操作的位深度。在比特深度有限的情况下，每次运算都会引入量化噪声，不能再假设传统的DBP算法会优于其低复杂度的替代算法。本文利用固定点硬件的通用软件模型，比较了DBP和一种单步非线性DBP实现方法&增强分裂步长傅里叶法(ESSFM)与线性均衡方法。讨论了位深度和快速傅立叶变换(FFT)大小的要求，以检验这两种数字非线性补偿方案的最佳工作状态。对于1000 km的传输系统，我们发现（假设FFT大小是优化的），从信噪比来看，ESSFM算法在13比特的所有硬件分辨率上都优于传统的DBP算法。
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英文标题：
《On the Impact of Fixed Point Hardware for Optical Fiber Nonlinearity
  Compensation Algorithms》
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作者：
Tom Sherborne, Benjamin Banks, Daniel Semrau, Robert I. Killey, Polina
  Bayvel and Domani\c{c} Lavery
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最新提交年份：
2018
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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：
  Nonlinearity mitigation using digital signal processing has been shown to increase the achievable data rates of optical fiber transmission links. One especially effective technique is digital back propagation (DBP), an algorithm capable of simultaneously compensating for linear and nonlinear channel distortions. The most significant barrier to implementing this technique, however, is its high computational complexity. In recent years, there have been several proposed alternatives to DBP with reduced computational complexity, although such techniques have not demonstrated performance benefits commensurate with the complexity of implementation. In order to fully characterize the computational requirements of DBP, there is a need to model the algorithm behavior when constrained to the logic used in a digital coherent receiver. Such a model allows for the analysis of any signal recovery algorithm in terms of true hardware complexity which, crucially, includes the bit-depth of the multiplication operation. With a limited bit depth, there is quantization noise, introduced with each arithmetic operation, and it can no longer be assumed that the conventional DBP algorithm will outperform its low complexity alternatives. In this work, DBP and a single nonlinear step DBP implementation, the \textit{Enhanced Split Step Fourier} method (ESSFM), were compared with linear equalization using a generic software model of fixed point hardware. The requirements of bit depth and fast Fourier transform (FFT) size are discussed to examine the optimal operating regimes for these two schemes of digital nonlinearity compensation. For a 1000 km transmission system, it was found that (assuming an optimized FFT size), in terms of SNR, the ESSFM algorithm outperformed the conventional DBP for all hardware resolutions up to 13 bits.
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PDF链接：
https://arxiv.org/pdf/1804.08545