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摘要翻译：
5G毫米波（mmWave）技术以其高带宽、宽视场感知和精确定位能力，有望成为下一代V2X网络和自动驾驶汽车的重要组成部分。由于移动信道的波束对准困难以及由于mmWave发射机和接收机之间的阻塞效应，mmWave链路的可靠性可能受到损害。为了应对这些挑战，可以利用来自亚6 GHz信道的带外信息来预测mmWave频段中的时间和角度信道特性，这就需要很好地理解传播特性如何在不同频段之间耦合。在本文中，我们使用光线跟踪模拟来描述在城市环境中，在900 MHz到73 GHz的V2X信道中，车辆保持视线(LOS)和非视线(NLOS)光束的角和时间相关性。我们的结果揭示了随着频率的增加，传播信道的稀疏性增加，并突出了5.9GHz和28GHz频段之间强烈的时间/角相关性，尤其是对LOS信道。
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英文标题：
《Angular and Temporal Correlation of V2X Channels Across Sub-6 GHz and
mmWave Bands》
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作者：
Chethan Kumar Anjinappa and Ismail Guvenc
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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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一级分类：Computer Science 计算机科学
二级分类：Other Computer Science 其他计算机科学
分类描述：This is the classification to use for documents that do not fit anywhere else.
这是用于不适合其他任何地方的文档的分类。
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英文摘要：
5G millimeter wave (mmWave) technology is envisioned to be an integral part of next-generation vehicle-to-everything (V2X) networks and autonomous vehicles due to its broad bandwidth, wide field of view sensing, and precise localization capabilities. The reliability of mmWave links may be compromised due to difficulties in beam alignment for mobile channels and due to blocking effects between a mmWave transmitter and a receiver. To address such challenges, out-of-band information from sub-6 GHz channels can be utilized for predicting the temporal and angular channel characteristics in mmWave bands, which necessitates a good understanding of how propagation characteristics are coupled across different bands. In this paper, we use ray tracing simulations to characterize the angular and temporal correlation across a wide range of propagation frequencies for V2X channels ranging from 900 MHz up to 73 GHz, for a vehicle maintaining line-of-sight (LOS) and non-LOS (NLOS) beams with a transmitter in an urban environment. Our results shed light on increasing sparsity behavior of propagation channels with increasing frequency and highlight the strong temporal/angular correlation among 5.9 GHz and 28 GHz bands especially for LOS channels.
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PDF链接：
https://arxiv.org/pdf/1804.03505
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