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摘要翻译：
分析了在放大自发辐射噪声(ASE)和非理想收发子系统产生噪声的情况下，数字非线性补偿(NLC)的效率。研究了它对信噪比(SNR)和reach提高的影响，重点研究了分体式NLC，即在发射机和接收机之间分配数字反向传播算法。提出了一种计算任意分裂NLC结构的非理想传输系统信噪比的解析模型。当信号-信号非线性被补偿时，剩余的信号-噪声相互作用引起了性能限制。这些相互作用包括信号与共传播ASE和收发信机噪声之间的非线性拍打。虽然收发器噪声信号拍打通常在短距离传输中占主导地位，但ASE噪声信号拍打在较大传输距离中占主导地位。结果表明，两种方案在最优NLC分流比和各自的到达增益方面表现不同。此外，本文还给出了预测这两种方案中最优NLC分流比和最大可达量的简单公式。研究发现，当发射机和接收机注入相同的噪声时，对于小于1000 km的距离，采用标准单模光纤，收发信噪比（背靠背）为26 dB的情况下，分裂NLC提供的增益与传统数字反向传播(DBP)相比可以忽略不计。然而，当发射端和接收端注入不等量的噪声时，通过对分裂NLC算法进行适当的裁剪，可以在DBP的基础上达到56%的增益。数值模拟证实了理论结果。
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英文标题：
《The Impact of Transceiver Noise on Digital Nonlinearity Compensation》
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作者：
Daniel Semrau, Domanic Lavery, Lidia Galdino, Robert I. Killey, 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：
The efficiency of digital nonlinearity compensation (NLC) is analyzed in the presence of noise arising from amplified spontaneous emission noise (ASE) as well as from a non-ideal transceiver subsystem. Its impact on signal-to-noise ratio (SNR) and reach increase is studied with particular emphasis on split NLC, where the digital back-propagation algorithm is divided between transmitter and receiver. An analytical model is presented to compute the SNR's for non-ideal transmission systems with arbitrary split NLC configurations. When signal-signal nonlinearities are compensated, the performance limitation arises from residual signal-noise interactions. These interactions consist of nonlinear beating between the signal and co-propagating ASE and transceiver noise. While transceiver noise-signal beating is usually dominant for short transmission distances, ASE noise-signal beating is dominant for larger transmission distances. It is shown that both regimes behave differently with respect to the optimal NLC split ratio and their respective reach gains. Additionally, simple formulas for the prediction of the optimal NLC split ratio and the reach increase in those two regimes are reported. It is found that split NLC offers negligible gain with respect to conventional digital back-propagation (DBP) for distances less than 1000 km using standard single-mode fibers and a transceiver (back-to-back) SNR of 26 dB, when transmitter and receiver inject the same amount of noise. However, when transmitter and receiver inject an unequal amount of noise, reach gains of 56% on top of DBP are achievable by properly tailoring the split NLC algorithm. The theoretical findings are confirmed by numerical simulations.
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PDF链接：
https://arxiv.org/pdf/1710.00782
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