摘要翻译：
无线电能传输(WPT)是一种很有前途的解决方案，可以为电子设备提供方便、持久的能量供应。传统的WPT技术面临着为物联网（IoT）和移动设备（如传感器、控制器、智能手机、笔记本电脑等）提供超过米级距离的瓦级功率的挑战。分布式激光充电(DLC)是一种新的WPT替代方案，它有可能解决这些问题，并使WPT具有与WiFi通信类似的体验。本文提出了一个多模块DLC系统模型，阐述了其物理原理和数学公式。该分析模型考虑到激光波长、传输衰减和光伏电池（PV电池）温度的影响，可以评估每个模块的功率转换或传输。在电-激光和激光-电功率转换线性近似的基础上，通过测量和仿真验证，得到了最大功率传输效率的闭式表达式。因此，我们证明了最大功率传输效率随发射机电源功率、激光波长、传输距离和光伏电池温度的变化。与无线信息传输(WIT)中信息传输容量的最大化一样，功率传输效率的最大化在WPT中也同样重要。因此，本文的工作不仅在理论上为DLC提供了见解，而且在实践中也为DLC系统的设计提供了指导。
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英文标题：
《Distributed Laser Charging: A Wireless Power Transfer Approach》
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作者：
Qingqing Zhang, Wen Fang, Qingwen Liu, Jun Wu, Pengfei Xia, and
  Liuqing Yang
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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：
  Wireless power transfer (WPT) is a promising solution to provide convenient and perpetual energy supplies to electronics. Traditional WPT technologies face the challenge of providing Watt-level power over meter-level distance for Internet of Things (IoT) and mobile devices, such as sensors, controllers, smart-phones, laptops, etc.. Distributed laser charging (DLC), a new WPT alternative, has the potential to solve these problems and enable WPT with the similar experience as WiFi communications. In this paper, we present a multi-module DLC system model, in order to illustrate its physical fundamentals and mathematical formula. This analytical modeling enables the evaluation of power conversion or transmission for each individual module, considering the impacts of laser wavelength, transmission attenuation and photovoltaic-cell (PV-cell) temperature. Based on the linear approximation of electricity-to-laser and laser-to-electricity power conversion validated by measurement and simulation, we derive the maximum power transmission efficiency in closed-form. Thus, we demonstrate the variation of the maximum power transmission efficiency depending on the supply power at the transmitter, laser wavelength, transmission distance, and PV-cell temperature. Similar to the maximization of information transmission capacity in wireless information transfer (WIT), the maximization of the power transmission efficiency is equally important in WPT. Therefore, this work not only provides the insight of DLC in theory, but also offers the guideline of DLC system design in practice.
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PDF链接：
https://arxiv.org/pdf/1801.03835