A Weighted Random Forest Based PositioningAlgorithm for 6G Indoor Communications

TL;DR

This paper proposes a weighted random forest algorithm utilizing CSI-based channel fingerprints and ray-tracing to achieve high-precision indoor positioning within 6 cm accuracy for 6G communications.

Contribution

It introduces a novel weighted random forest method combined with ray-tracing for efficient, high-accuracy indoor positioning in 6G networks.

Findings

Achieves stable 6 cm positioning accuracy across various indoor scenarios.

Utilizes CSI-based channel fingerprints for high-resolution positioning.

Ray-tracing improves database establishment efficiency.

Abstract

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.