Site-Specific Online Compressive Beam Codebook Learning in mmWave Vehicular Communication
Site-Specific Online Compressive Beam Codebook Learning in mmWave Vehicular Communication
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DOI：
10.1109/twc.2020.3047547
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发表时间：
2020-05
影响因子：
10.4
通讯作者：
Yuyang Wang;Nitin Jonathan Myers;N. González-Prelcic;R. Heath
中科院分区：
文献类型：
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作者：
Yuyang Wang;Nitin Jonathan Myers;N. González-Prelcic;R. Heath
毫米波（mmWave）通信是在车辆网络中支持Gbps传感器数据共享的一种可行的解决方案。在车载通信中使用毫米波和高移动性的大型天线阵列使得设计快速波束对准解决方案具有挑战性。在本文中，我们提出了一种新的框架，学习信道的出发角（AoD）统计在基站（BS），并使用此信息来有效地获取信道测量。我们的框架集成了在线学习压缩感知（CS）码本学习和优化的码本用于基于CS的波束对准。我们制定了一个CS矩阵优化问题的基础上，AoD统计在BS。此外，基于CS信道测量，我们开发了在BS处更新和学习这样的信道AoD统计的技术。我们使用置信上限（UCB）算法来学习AoD统计和CS矩阵。数值结果表明，CS矩阵在所提出的框架提供更快的波束对准比标准CS矩阵设计。仿真结果表明，所提出的波束训练技术可以减少80%的开销相比，穷举波束搜索，和70%相比，不利用任何AoD统计的标准CS解决方案。
Millimeter wave (mmWave) communication is one viable solution to support Gbps sensor data sharing in vehicular networks. The use of large antenna arrays at mmWave and high mobility in vehicular communication make it challenging to design fast beam alignment solutions. In this paper, we propose a novel framework that learns the channel angle-of-departure (AoD) statistics at a base station (BS) and uses this information to efficiently acquire channel measurements. Our framework integrates online learning for compressive sensing (CS) codebook learning and the optimized codebook is used for CS-based beam alignment. We formulate a CS matrix optimization problem based on the AoD statistics available at the BS. Furthermore, based on the CS channel measurements, we develop techniques to update and learn such channel AoD statistics at the BS. We use the upper confidence bound (UCB) algorithm to learn the AoD statistics and the CS matrix. Numerical results show that the CS matrix in the proposed framework provides faster beam alignment than standard CS matrix designs. Simulation results indicate that the proposed beam training technique can reduce overhead by 80% compared to exhaustive beam search, and 70% compared to standard CS solutions that do not exploit any AoD statistics.