摘要翻译：
毫米波（mmWave）被认为是唯一可行的高带宽车载通信解决方案。然而，频繁的信道估计和波束形成要求提供满意的服务质量限制了mmWave用于车载通信。在本文中，我们提出了一个新的信道估计和波束跟踪框架的mmWave通信环境下的车载网络。对于信道估计，我们提出了一种称为鲁棒自适应多反馈(RAF)的算法，该算法以较少的反馈比特数获得了与现有信道估计算法相当的估计性能。在给定信道估计数的情况下，给出了RAF算法估计误差概率的上下界，并通过蒙特卡罗仿真验证了算法的准确性。对于波束跟踪，我们提出了一种新的实用的mmWave车载通信模型。与已有的研究相比，该模型基于位置、速度和信道系数，使得跟踪性能有了明显的改善。针对新的波束跟踪模型，我们重新推导了雅可比矩阵方程，降低了车载通信的复杂度。通过大量的仿真，验证了本文提出的信道估计和波束跟踪算法与现有算法和模型相比的优越性。仿真结果表明，RAF算法可以达到预期的PEE，同时平均减少反馈开销75.5%，减少信道估计时间14%。波束跟踪算法也被证明显著提高了波束跟踪性能，为数据传输提供了更大的空间。
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英文标题：
《Fast Channel Estimation and Beam Tracking for Millimeter Wave Vehicular
  Communications》
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作者：
Sina Shaham, Ming Ding, Matthew Kokshoorn, Zihuai Lin, Shuping Dang,
  Rana Abbas
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最新提交年份：
2019
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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：
  Millimeter wave (mmWave) has been claimed to be the only viable solution for high-bandwidth vehicular communications. However, frequent channel estimation and beamforming required to provide a satisfactory quality of service limits mmWave for vehicular communications. In this paper, we propose a novel channel estimation and beam tracking framework for mmWave communications in a vehicular network setting. For channel estimation, we propose an algorithm termed robust adaptive multi-feedback (RAF) that achieves comparable estimation performance as existing channel estimation algorithms, with a significantly smaller number of feedback bits. We derive upper and lower bounds on the probability of estimation error (PEE) of the RAF algorithm, given a number of channel estimations, whose accuracy is verified through Monte Carlo simulations. For beam tracking, we propose a new practical model for mmWave vehicular communications. In contrast to the prior works, the model is based on position, velocity, and channel coefficient, which allows a significant improvement of the tracking performance. Focused on the new beam tracking model, we re-derive the equations for Jacobian matrices, reducing the complexity for vehicular communications. An extensive number of simulations is conducted to show the superiority of our proposed channel estimation method and beam tracking algorithm in comparison with the existing algorithms and models. Our simulations suggest that the RAF algorithm can achieve the desired PEE, while on average, reducing the feedback overhead by 75.5% and the total channel estimation time by 14%. The beam tracking algorithm is also shown to significantly improve beam tracking performance, allowing more room for data transmission.
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PDF链接：
https://arxiv.org/pdf/1806.00161