摘要翻译：
目前大多数电力线通信系统和标准，包括窄带和宽带，都是基于正交频分复用(OFDM)的。然而，这种多路复用方案受到高峰均功率比(PAPR)的影响，这会极大地影响PLC调制解调器的能量效率、尺寸和成本，并引起电磁兼容(EMC)问题。本文研究了具有较好峰均比特性的矢量OFDM(VOFDM)在非高斯宽带PLC信道上的性能。此外，利用VOFDM系统的低峰均比特性，进一步提高了非线性预处理器的效率。从PAPR的互补累积分布函数、噪声检测错误概率和非线性预处理器输出端的信噪比等方面研究了可实现的增益。为了比较，本文还对传统OFDM系统的性能进行了分析。结果表明，在相同的系统条件下，与传统的OFDM相比，该系统可以节省高达2 dB的发射功率，这最终也转化为一个对EMC限制更有弹性的系统，降低了成本和PLC调制解调器的尺寸。研究还表明，随着VOFDM系统矢量块(VB)大小的增加，可实现的增益变得更加显著。
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英文标题：
《Vector OFDM Transmission over Non-Gaussian Power Line Communication
  Channels》
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作者：
Bamidele Adebisi, Khaled M. Rabie, Augustine Ikpehai, Cinna Soltanpur,
  and Andrew Wells
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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：
  Most of the recent power line communication (PLC) systems and standards, both narrow-band and broadband, are based on orthogonal frequency-division multiplexing (OFDM). This multiplexing scheme however suffers from the high peak-to-average power ratio (PAPR) which can considerably impact the energy efficiency, size and cost of PLC modems as well as cause electromagnetic compatibility (EMC) issues. This paper investigates the performance of vector OFDM (VOFDM), which has inherently better PAPR properties, over non-Gaussian broadband PLC channels equipped with two nonlinear preprocessors at the receiver. In addition, the low PAPR property of the VOFDM system is exploited to further enhance the efficiency of the nonlinear preprocessors. The achievable gains are studied in terms of the complementary cumulative distribution function of the PAPR, probability of noise detection error and the signal-to-noise ratio at the output of the nonlinear preprocessors. For comparison's sake, the performance of conventional OFDM systems is also presented throughout the paper. Results reveal that the proposed system is able to provide up to 2 dB saving in the transmit power relative to the conventional OFDM under same system conditions, which eventually also translates into a system that is more resilient to EMC limits, reduced cost and size of PLC modems. It is also shown that the achievable gains become more significant as the vector block (VB) size of the VOFDM system is increased.
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PDF链接：
https://arxiv.org/pdf/1806.1024