Technology research and standardization work of sixth generation (6G) has been carried out worldwide. Channel research is the prerequisite of 6G technology evaluation and optimization. This paper presents a survey and tutorial on channel measurement, modeling, and simulation for 6G. We first highlight the challenges of channel for 6G systems, including higher frequency band, extremely large antenna array, new technology combinations, and diverse application scenarios. A review of channel measurement and modeling for four possible 6G enabling technologies is then presented, i.e., terahertz communication, massive multiple-input multiple-output communication, joint communication and sensing, and reconfigurable intelligent surface. Finally, we introduce a 6G channel simulation platform and provide examples of its implementation. The goal of this paper is to help both professionals and non-professionals know the progress of 6G channel research, understand the 6G channel model, and use it for 6G simulation.
Terahertz (THz) channel propagation characteristics are vital for the design, evaluation, and optimization for THz communication systems. Moreover, reflection plays a significant role in channel propagation. In this letter, the reflection coefficient of the THz channel is researched based on extensive measurement campaigns. Firstly, we set up the THz channel sounder from 220 to 320 GHz with the incident angle ranging from 10{\deg} to 80{\deg}. Based on the measured propagation loss, the reflection coefficients of five building materials, i.e., glass, tile, aluminium alloy, board, and plasterboard, are calculated separately for frequencies and incident angles. It is found that the lack of THz relative parameters leads to the Fresnel model of non-metallic materials can not fit the measured data well. Thus, we propose a frequency-angle two-dimensional reflection coefficient model by modifying the Fresnel model with the Lorenz and Drude model. The proposed model characterizes the frequency and incident angle for reflection coefficients and shows low root-mean-square error with the measured data. Generally, these results are useful for modeling THz channels.