发帖
雷达卡
摘要翻译：
短接触时间的移动场景对自动车辆和路边基站(BS)之间的高带宽数据传输提出了挑战。毫米波频段是一个可行的解决方案，因为它们在60GHz频段提供了巨大的带宽，数据传输速率为几个Gbps。然而，波束形成作为该频段的默认模式，要求在相对运动下精确连续对准。利用802.11ad前导码的特殊结构，提出了一种配置IEEE 802.11ad WiFi路由器作为基站和雷达的方法。我们将雷达功能嵌入到符合标准的操作中，这些操作不会修改超出802.11ad协议定义的帧的核心结构。这不仅减少了波束训练时间，而且还确保了车辆流量增加时的可伸缩性，因为雷达可以在200米的距离上精确测距0.1米。我们进一步分析了分配给所提出的带内雷达的时间和通信模式之间的成本效益权衡。我们的结果显示，在一个特定的模拟车辆场景中，与经典的802.11ad操作相比，在波束训练期间产生的开销减少了83%。
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英文标题：
《Beam Alignment and Tracking for Autonomous Vehicular Communication using
IEEE 802.11ad-based Radar》
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作者：
Guillem Reus Muns, Kumar Vijay Mishra, Carlos Bocanegra Guerra,
Yonnina C. Eldar and Kaushik R. Chowdhury
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最新提交年份：
2017
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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：
Mobility scenarios involving short contact times pose a challenge for high bandwidth data transfer between autonomous vehicles and roadside base stations (BS). Millimeter wave bands are a viable solution as they offer enormous bandwidth in the 60GHz band with several Gbps data transfer rates. However, beamforming is used as a default mode in this band, which requires accurate and continuous alignment under relative motion. We propose a method in which an off-the-shelf IEEE 802.11ad WiFi router is configured to serve as the BS as well as a radar exploiting special structure of 802.11ad preamble. We embed the radar functionality within standards-compliant operations that do not modify the core structure of the frames beyond what is defined by the 802.11ad protocol. This not only reduces the beam training time, but also ensures scalability with increasing vehicular traffic because radar allows accurate ranging of up to 0.1m at distances up to 200m. We further analyze the ensuing cost-benefit trade-off between the time allotted to the proposed in-band radar and communication modes. Our results reveal 83% reduction on the overhead incurred during the beam training achieved for a specific simulated vehicular scenario over the classical 802.11ad operation.
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PDF链接：
https://arxiv.org/pdf/1712.02453
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