摘要翻译：
纳米级无线传感器网络（NWSNs）可能很快就可以使用石墨烯基天线，这种天线在0.1-10太赫兹波段共振。为了节省有限的纳米级能量，人们期望NWSNs将使用飞秒量级的极短脉冲进行通信。对太赫兹脉冲波达方向(DOA)的准确估计将有助于NWSNS实现许多有用的应用。本文利用著名的多信号分类(MUSIC)算法，研究了NWSNs在不同能量水平、距离、脉冲形状和频率下的DOA估计。我们的分析表明，在发射峰值能量为6 THz的一阶高斯脉冲下，可以获得最佳的DOA估计。基于Monte Carlo仿真，我们证明了MUSIC算法能够在脉冲能量小于1阿托焦耳的情况下，从6m左右的距离估计出均方根误差小于1度的DOA。
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英文标题：
《Direction of Arrival Estimation for Nanoscale Sensor Networks》
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作者：
Shree M. Prasad, Trilochan Panigrahi, Mahbub Hassan
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最新提交年份：
2018
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Emerging Technologies        新兴技术
分类描述：Covers approaches to information processing (computing, communication, sensing) and bio-chemical analysis based on alternatives to silicon CMOS-based technologies, such as nanoscale electronic, photonic, spin-based, superconducting, mechanical, bio-chemical and quantum technologies (this list is not exclusive). Topics of interest include (1) building blocks for emerging technologies, their scalability and adoption in larger systems, including integration with traditional technologies, (2) modeling, design and optimization of novel devices and systems, (3) models of computation, algorithm design and programming for emerging technologies.
涵盖基于硅CMOS技术替代品的信息处理（计算、通信、传感）和生物化学分析方法，如纳米级电子、光子、自旋、超导、机械、生物化学和量子技术（此列表不是唯一的）。感兴趣的主题包括：（1）新兴技术的构建块、其可伸缩性和在大型系统中的采用，包括与传统技术的集成；（2）新型设备和系统的建模、设计和优化；（3）新兴技术的计算模型、算法设计和编程。
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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：
  Nanoscale wireless sensor networks (NWSNs) could be within reach soon using graphene-based antennas, which resonate in 0.1-10 terahertz band. To conserve the limited energy available at nanoscale, it is expected that NWSNs will communicate using extremely short pulses on the order of femtoseconds. Accurate estimation of direction of arrival (DOA) for such terahertz pulses will help realize many useful applications for NWSNs. In this paper, using the well-known MUltiple SIgnal Classification (MUSIC) algorithm, we study DOA estimation for NWSNs for different energy levels, distances, pulse shapes, and frequencies. Our analyses reveal that the best DOA estimation is achieved with the first order Gaussian pulses, which emit their peak energy at 6 THz. Based on Monte Carlo simulations, we demonstrate that MUSIC algorithm is capable of estimating DOA with root mean square error less than one degree from a distance of around 6 meter for pulse energy as little as 1 atto Joule.
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PDF链接：
https://arxiv.org/pdf/1807.04435