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篇名
Beam Tracking Based on a New State Model for mmWave V2I Communication on 3D Roads
並列篇名
Beam Tracking Based on a New State Model for mmWave V2I Communication on 3D Roads
作者 Yu Sun (Yu Sun)Chen-Wei Feng (Chen-Wei Feng)Xian-Ling Wang (Xian-Ling Wang)Jiang-Nan Yuan (Jiang-Nan Yuan)Lin Zhang (Lin Zhang)
英文摘要

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.

 

起訖頁 033-050
關鍵詞 multiple-input multiple-outputbeam trackingextended Kalman filterinternet of vehiclesmillimeter wave
刊名 電腦學刊  
期數 202402 (35:1期)
該期刊-上一篇 ACANet: A Fine-grained Image Classification Optimization Method Based on Convolution and Attention Fusion
該期刊-下一篇 MR-SFAMA-Q: A MAC Protocol based on Q-Learning for Underwater Acoustic Sensor Networks
 

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