Deep Learning-Based Link Configuration for Radar-Aided Multiuser mmWave Vehicle-to-Infrastructure Communication
Deep Learning-Based Link Configuration for Radar-Aided Multiuser mmWave Vehicle-to-Infrastructure Communication
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DOI：
10.1109/tvt.2023.3239227
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发表时间：
2022-01
影响因子：
6.8
通讯作者：
Andrew M. Graff;Yun Chen;Nuria Gonz'alez-Prelcic;Takayuki Shimizu
中科院分区：
文献类型：
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作者：
Andrew M. Graff;Yun Chen;Nuria Gonz'alez-Prelcic;Takayuki Shimizu
按照传统的波束训练协议配置毫米波链路，如在当前蜂窝标准中提出的，引入了大的通信开销，特别是在信道高度动态的车辆系统中。在本文中，我们提出了使用无源雷达阵列来感测来自道路上的多个车辆的汽车雷达传输，以及一个雷达处理链，该雷达处理链提供有关道路基础设施和每个车辆之间的链路的候选波束的缩减集的信息。该先验信息可以稍后由波束训练协议利用以显著减少开销。雷达处理链估计雷达信号的定时和啁啾速率，通过滤除干扰雷达啁啾来隔离各个信号，并估计每个单独雷达传输的空间协方差。然后，使用深度网络将这些雷达空间协方差的特征转换为通信空间协方差的特征，通过学习雷达和通信信道之间的复杂映射，在视线和非视线设置中。将这种方法的通信速率和中断概率与穷举搜索和纯雷达辅助波束训练方法（没有基于深度学习的映射）进行比较，并在光线跟踪模拟的多用户信道上进行评估。结果表明：（i）所提出的处理链可以可靠地隔离各个雷达的空间协方差，以及（ii）基于深度学习的雷达到通信转换策略在LOS和NLOS信道中提供了比纯雷达辅助方法的显着改进。
Configuring millimeter wave links following a conventional beam training protocol, as the one proposed in the current cellular standard, introduces a large communication overhead, especially relevant in vehicular systems, where the channels are highly dynamic. In this paper, we propose the use of a passive radar array to sense automotive radar transmissions coming from multiple vehicles on the road, and a radar processing chain that provides information about a reduced set of candidate beams for the links between the road-infrastructure and each one of the vehicles. This prior information can be later leveraged by the beam training protocol to significantly reduce overhead. The radar processing chain estimates both the timing and chirp rates of the radar signals, isolates the individual signals by filtering out interfering radar chirps, and estimates the spatial covariance of each individual radar transmission. Then, a deep network is used to translate features of these radar spatial covariances into features of the communication spatial covariances, by learning the intricate mapping between radar and communication channels, in both line-of-sight and non-line-of-sight settings. The communication rates and outage probabilities of this approach are compared against exhaustive search and pure radar-aided beam training methods (without deep learning-based mapping), and evaluated on multi-user channels simulated by ray tracing. Results show that: (i) the proposed processing chain can reliably isolate the spatial covariances for individual radars, and (ii) the radar-to-communications translation strategy based on deep learning provides a significant improvement over pure radar-aided methods in both LOS and NLOS channels.