基于扩展卡尔曼滤波器的车辆跟踪V2I通信自适应波束设计
Adaptive Beam Design for V2I Communications using Vehicle Tracking with Extended Kalman Filter
摘要：
车联网通信系统是通过将车辆连接到无线网络来改善驾驶体验和汽车安全的强有力的候选者。为了充分利用车辆连接的优势，必须确保路侧单元（RSU）和快速移动车辆之间的稳定网络连接。基于扩展的卡尔曼滤波器（EKF），我们开发了一种车辆跟踪算法，使可靠的无线电连接。对于车辆跟踪算法，我们专注于估计高移动性车辆的波束方向的快速变化，同时减少反馈开销。此外，我们设计了一个波束形成码本，考虑道路布局和RSU。通过利用所提出的波束成形码本，道路上的车辆可以期望类似于传统蜂窝服务的服务质量。最后，波束形成器选择算法的开发，以确保足够的增益系统的链路预算。数值结果表明，基于EKF的车辆跟踪算法和所提出的波束形成结构比现有方案更适用于车辆-基础设施网络。
英文摘要：
Vehicle-to-everything communication system is a strong candidate for improving the driving experience and automotive safety by linking vehicles to wireless networks. To take advantage of the full benefits of vehicle connectivity, it is essential to ensure a stable network connection between roadside unit (RSU) and fast-moving vehicles. Based on the extended Kalman filter (EKF), we develop a vehicle tracking algorithm to enable reliable radio connections. For the vehicle tracking algorithm, we focus on estimating the rapid changes in the beam direction of a high-mobility vehicle while reducing the feedback overhead. Furthermore, we design a beamforming codebook that considers the road layout and RSU. By leveraging the proposed beamforming codebook, vehicles on the road can expect a service quality similar to that of conventional cellular services. Finally, a beamformer selection algorithm is developed to secure sufficient gain for the system's link budget. Numerical results verify that the EKF-based vehicle tracking algorithm and the proposed beamforming structure are more suitable for vehicle-to-infrastructure networks compared to existing schemes.
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