摘要翻译：
新兴的健康监测应用，如通过多通道神经植入物进行信息传输、从体内进行图像和视频通信等，要求超低有功功率(<50${\mu}$W)、高数据率、能量可扩展、高能效（PJ/bit）的无线电。以前的文献主要集中在低平均功率、占空比周期的无线电或低功率但低日期的无线电。在本文中，我们研究了传统无线电中每个前端部件的功率性能权衡，包括有源匹配、下变频和RF/IF放大，并根据最高性能/能量度量来确定它们的优先级。分析表明，50${\\omega}$有源匹配和RF增益对于50${\\mu}$W的功率预算是禁止的。采用台积电65nm工艺设计的混频器优先结构，采用N路混频器和自偏置反相器基带低噪声放大器，在OOK调制下，性能可达10Mbps(<5PJ/B)。
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英文标题：
《Design Considerations of a Sub-50 {\mu}W Receiver Front-end for
  Implantable Devices in MedRadio Band》
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作者：
Gregory Chang, Shovan Maity, Baibhab Chatterjee, Shreyas Sen
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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：Networking and Internet Architecture        网络和因特网体系结构
分类描述：Covers all aspects of computer communication networks, including network architecture and design, network protocols, and internetwork standards (like TCP/IP). Also includes topics, such as web caching, that are directly relevant to Internet architecture and performance. Roughly includes all of ACM Subject Class C.2 except C.2.4, which is more likely to have Distributed, Parallel, and Cluster Computing as the primary subject area.
涵盖计算机通信网络的所有方面，包括网络体系结构和设计、网络协议和网络间标准（如TCP/IP）。还包括与Internet体系结构和性能直接相关的主题，如web缓存。大致包括除C.2.4以外的所有ACM主题类C.2，后者更有可能将分布式、并行和集群计算作为主要主题领域。
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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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英文摘要：
  Emerging health-monitor applications, such as information transmission through multi-channel neural implants, image and video communication from inside the body etc., calls for ultra-low active power (<50${\mu}$W) high data-rate, energy-scalable, highly energy-efficient (pJ/bit) radios. Previous literature has strongly focused on low average power duty-cycled radios or low power but low-date radios. In this paper, we investigate power performance trade-off of each front-end component in a conventional radio including active matching, down-conversion and RF/IF amplification and prioritize them based on highest performance/energy metric. The analysis reveals 50${\Omega}$ active matching and RF gain is prohibitive for 50${\mu}$W power-budget. A mixer-first architecture with an N-path mixer and a self-biased inverter based baseband LNA, designed in TSMC 65nm technology show that sub 50${\mu}$W performance can be achieved up to 10Mbps (< 5pJ/b) with OOK modulation.
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PDF链接：
https://arxiv.org/pdf/1710.06541