摘要翻译：
本文回顾了一些最近有希望的方法，使移动电源更接近现实。与通常由微波社区和通信/信号处理社区发表的文章不同，这些文章一方面分别强调用于WPT的射频、电路和天线解决方案，另一方面强调用于WPT的通信、信号和系统设计，这篇评论文章独特地将射频、信号和系统设计连接起来，以便使这些社区彼此更接近，并更好地理解高效WPT网络架构的基本构建块。我们首先回顾使移动电源成为现实的工程要求和设计挑战。然后，我们回顾了传感器和设备、无线电源和无线通信的射频设计等广泛领域的最新进展。在为WPT的信号和系统设计提供一些新的新面貌和有希望的途径之前，我们确定了它们的局限性并进行了批判性的观察。
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英文标题：
《Towards the 1G of Mobile Power Network: RF, Signal and System Designs to
  Make Smart Objects Autonomous》
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作者：
Bruno Clerckx, Alessandra Costanzo, Apostolos Georgiadis, and Nuno
  Borges Carvalho
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最新提交年份：
2017
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Information Theory        信息论
分类描述：Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
涵盖信息论和编码的理论和实验方面。包括ACM学科类E.4中的材料，并与H.1.1有交集。
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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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一级分类：Mathematics        数学
二级分类：Information Theory        信息论
分类描述：math.IT is an alias for cs.IT. Covers theoretical and experimental aspects of information theory and coding.
它是cs.it的别名。涵盖信息论和编码的理论和实验方面。
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英文摘要：
  This article reviews some recent promising approaches to make mobile power closer to reality. In contrast with articles commonly published by the microwave community and the communication/signal processing community that separately emphasize RF, circuit and antenna solutions for WPT on one hand and communications, signal and system designs for WPT on the other hand, this review article uniquely bridges RF, signal and system designs in order to bring those communities closer to each other and get a better understanding of the fundamental building blocks of an efficient WPT network architecture. We start by reviewing the engineering requirements and design challenges of making mobile power a reality. We then review the state-of-the-art in a wide range of areas spanning sensors and devices, RF design for wireless power and wireless communications. We identify their limitations and make critical observations before providing some fresh new look and promising avenues on signal and system designs for WPT.
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PDF链接：
https://arxiv.org/pdf/1712.06088