Beam Tracking Based on a New State Model for mmWave V2I Communication on 3D Roads

Yu Sun Yu Sun, Chen-Wei Feng Yu Sun, Xian-Ling Wang Chen-Wei Feng, Jiang-Nan Yuan Xian-Ling Wang, Lin Zhang Jiang-Nan Yuan
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Abstract

The expansion of 5G and Internet of Things has laid a good foundation for the in-depth research of Internet of vehicles. Low frequency resources are scarce, and Internet of vehicles communication requires extremely high communication rate. Millimeter wave can meet the above two requirements, but its characteristics and the complicated communication conditions of Internet of vehicles make it difficult to combine the two. Overcoming these problems and making beam tracking accurate and steady is a major challenge at present. In this paper, a new extended Kalman filter tracking algorithm is proposed for mmWave V2I scenarios. On the basis of the original algorithm, a threshold prediction update mechanism is added. A new scheme is adopted, which takes position and velocity as tracking variables, and the tracking model is derived for the first time in MIMO 3D scenarios based on this scheme. The model considers the three-dimensional road conditions, including the vehicle deflection motion and the millimeter wave link blocked by large vehicles, which is more suitable for practical application scenarios. The simulation results reveal that the position and velocity tracking scheme is superior to the angle and gain tracking scheme, and the tracking error of the proposed algorithm is lower than that of the algorithms using similar state models. Based on the three-dimensional scene, it considers more realistic situations, and is more consistent with the kinematic characteristics of the vehicle and has more practical significance.  
基于新状态模型的波束跟踪,用于三维道路上的毫米波 V2I 通信
5G 和物联网的发展为车联网的深入研究奠定了良好的基础。低频资源稀缺,车联网通信要求极高的通信速率。毫米波可以满足以上两个要求,但其特性和车联网复杂的通信条件使得两者难以结合。克服这些问题,实现精确稳定的波束跟踪是当前面临的一大挑战。本文针对毫米波 V2I 场景提出了一种新的扩展卡尔曼滤波跟踪算法。在原有算法的基础上,增加了阈值预测更新机制。该算法采用了一种以位置和速度为跟踪变量的新方案,并首次基于该方案推导出了 MIMO 3D 场景下的跟踪模型。该模型考虑了三维路况,包括车辆的偏转运动和毫米波链路被大型车辆阻挡的情况,更适合实际应用场景。仿真结果表明,位置和速度跟踪方案优于角度和增益跟踪方案,且所提算法的跟踪误差低于采用类似状态模型的算法。基于三维场景,考虑的情况更真实,更符合车辆的运动学特性,更具有实用意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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