摘要翻译：
针对快于奈奎斯特(FTN)的正交双二进制(QDB)调制偏振分复用(FTN)相干光学系统，提出了一种基于训练符号的偏振解复用均衡算法。该算法在最小均方算法的基础上，考虑了一个符号的多个位置候选，以利用QDB调制的训练符号。结果表明，在不同的极化对准场景下，该算法具有良好的收敛性能。与采用4进制正交幅度调制(4-QAM)差分编码的恒模算法和采用逐码检测的16-QAM系统相比，该算法的误码率为2×10-2所需的光信噪比分别降低了1.7和1.8dB。此外，与基于Tomlinson-Harashima预编码的FTN系统的比较表明，当采用4-QAM时，QDB是更好的。
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英文标题：
《Training Symbol-Based Equalization for Quadrature Duobinary PDM-FTN
  Systems》
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作者：
S. Zhang, D. Chang, O. A. Dobre, O. Omomukuyo, X. Lin, and R.
  Venkatesan
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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：
  A training symbol-based equalization algorithm is proposed for polarization de-multiplexing in quadrature duobinary (QDB) modulated polarization division multiplexedfaster-than-Nyquist (FTN) coherent optical systems. The proposed algorithm is based on the least mean square algorithm, and multiple location candidates of a symbol are considered in order to make use of the training symbols with QDB modulation.Results show that an excellent convergence performance is obtained using the proposed algorithm under different polarization alignment scenarios. The optical signal-to-noise ratio required to attain a bit error rate of 2*10-2 is reduced by 1.7 and 1.8 dB using the proposed algorithm, compared to systems using the constant modulus algorithm with differential coding for 4-ary quadrature amplitude modulation(4-QAM) and 16-QAM systems with symbol-by-symbol detection, respectively.Furthermore, comparisons with the Tomlinson-Harashima precoding-based FTN systems illustrate that QDB is preferable when 4-QAM is utilized.
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PDF链接：
https://arxiv.org/pdf/1801.0115