摘要翻译：
本文比较了不同的线性和非线性射频能量收集模型，包括有限灵敏度和无限灵敏度，用于同时进行无线信息和功率传输(SWIPT)。在一系列RF采集模型下，成功的SWIPT接收的概率被严格量化，使用了商业RFID背景下最先进的整流器。SWIPT文献的很大一部分使用了过于简化的模型，没有考虑到潜在采集电路的有限灵敏度或非线性。研究表明，通信信号并不总是适合于同时进行能量传递，并得出结论：在实际的SWIPT研究中，应仔细考虑收割机固有的非理想特性；并给出了具体的收割机建模方法。
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英文标题：
《Nonlinear Energy Harvesting Models in Wireless Information and Power
  Transfer》
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作者：
Panos N. Alevizos and Georgios Vougioukas and Aggelos Bletsas
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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：
  This work compares different linear and nonlinear RF energy harvesting models, including limited or unlimited sensitivity, for simultaneous wireless information and power transfer (SWIPT). The probability of successful SWIPT reception under a family of RF harvesting models is rigorously quantified, using state-of-the-art rectifiers in the context of commercial RFIDs. A significant portion of SWIPT literature uses oversimplified models that do not account for limited sensitivity or nonlinearity of the underlying harvesting circuitry. This work demonstrates that communications signals are not always appropriate for simultaneous energy transfer and concludes that for practical SWIPT studies, the inherent non-ideal characteristics of the harvester should be carefully taken into account; specific harvester's modeling methodology is also offered.
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PDF链接：
https://arxiv.org/pdf/1802.09994