论文标题
6G室内通信的加权随机森林基于森林的定位
A Weighted Random Forest Based PositioningAlgorithm for 6G Indoor Communications
论文作者
论文摘要
由于室内无视线（NLOS）传播和多访问干扰（MAI），在室内场景中实现厘米级的定位精度是一个巨大的挑战。但是，第六代（6G）无线通信为厘米级定位提供了一个很好的机会。在6G中，毫米波（MMWave）和Terahertz（THZ）通信具有超大的带宽，因此通道状态信息（CSI）将具有很高的分辨率。在本文中，提出了基于加权的随机森林（WRF）室内定位算法，使用基于CSI的通道指纹特征，以实现6G室内通信的高精度定位。此外，射线追踪（RT）用于提高建立通道指纹数据库的效率。仿真结果证明了所提出算法的准确性和鲁棒性。结果表明，在不同的室内场景中，算法的定位精度在6 cm之内稳定，而通道指纹数据库以0.2 m的间隔建立。
Due to the indoor none-line-of-sight (NLoS) propagation and multi-access interference (MAI), it is a great challenge to achieve centimeter-level positioning accuracy in indoor scenarios. However, the sixth generation (6G) wireless communications provide a good opportunity for the centimeter-level positioning. In 6G, the millimeter wave (mmWave) and terahertz (THz) communications have ultra-broad bandwidth so that the channel state information (CSI) will have a high resolution. In this paper, a weighted random forest (WRF) based indoor positioning algorithm using CSI based channel fingerprint feature is proposed to achieve high-precision positioning for 6G indoor communications. In addition, ray-tracing (RT) is used to improve the efficiency of establishing channel fingerprint database. The simulation results demonstrate the accuracy and robustness of the proposed algorithm. It is shown that the positioning accuracy of the algorithm is stable within 6 cm in different indoor scenarios with the channel fingerprint database established at 0.2 m intervals.
