The use of fifth-generation (5G) communication technology by high-speed trains (HSTs) has a lot of potential to satisfy current needs for high data rates. Therefore, accurate modeling of the HST wireless channels is crucial for the design and performance assessment of the 5G systems. This paper proposes a general three-dimensional (3D) non-stationary small-scale fading model for 5G HST millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) channels, which captures the wireless channel characteristics in different HST operating scenarios. The proposed channel model has two characteristics. Firstly, it incorporates the distribution of overhead line poles along the railway to characterize the periodic scattering components from the poles. Secondly, it represents complex HST operating scenarios as combinations of five types of scattering clusters, namely hills, trees, lakes, buildings, and concrete. Furthermore, the impact of environmental complexity (EC) on channel statistical properties in the 5G HST massive MIMO scenarios is investigated. Afterwards, based on the birth-death process of scattering clusters, the proposed channel model can characterize the channel non-stationarity in the space-time-frequency domain. Simulation results demonstrate that the proposed model effectively captures the channel non-stationarity, the scattering characteristics of overhead line poles, and the impact of different ECs on system performance. The accuracy and practicality of the proposed model are validated through a comparison with measurement results.

Aiming to address the problem of limited transmission distance in applying reconfigurable intelligent surface (RIS) technology, this study leverages a tunnel simulation platform to investigate RIS-assisted wireless communication systems. Through theoretical derivation, we propose a path loss model formula specifically applicable to tunnel scenarios. Simulation results demonstrate that the proposed model accurately reflects the communication performance characteristics of RIS in tunnel scenarios, verifying the capability of RIS technology to enhance signal transmission distance within tunnels in rail transit engineering applications. This finding highlights the significant engineering potential and value of RIS technology.

Most people primarily rely on subjective opinions and action assessment from others in the process of learning badminton, which leads to biases and unreliable assessment of player performance. This paper presents a method of deep learning-based recognition and quality assessment of badminton actions to accurately identify player actions and assess player performance. In this research, we construct a video dataset of standard badminton actions for training networks and design a human pose estimation and tracking network to detect keypoints of individual players in the video dataset and track their trajectories. Furthermore, badminton action recognition is carried out based on the SlowFast network framework, and a Siamese network with it as the backbone network is proposed for automating the quality assessment of badminton actions. Experimental results demonstrate that the mean average precision (mAP) of human body pose estimation reaches 83.2%, the Multiple Object Tracking Accuracy (MOTA) of pose detection and recognition in badminton games reaches 81.4%, and the Multiple Object Tracking Precision (MOTP) reaches 90.7%. The accuracy of professional players in identifying badminton strokes is 83.08% for Top-1 and 96.89% for Top-3. Therefore, the proposed method can be effectively applied to badminton action recognition and quality assessment.

To effectively deploy 5G network base stations (BSs) in railway communication systems and ensure reliable communication between train terminals and BSs, it is essential to study the outdoor-to-indoor (O2I) wireless channel characteristics in high-speed trains (HSTs). This letter utilizes data from the 2.6 GHz HST measurement campaign to calibrate and validate the ray-tracing simulation model for the specified scenario by identifying key parameters, such as the relative permittivity (6.27) and conductivity (0.54) of the HST glass windows. Using this calibrated model, we analyze the channel fading characteristics under various polarization modes. The results indicate that, under the horizontal co-polarized (H-H) mode, the path loss (PL), root-mean-square delay spread (RMS-DS), and angular spread (AS) are all lower compared to other polarization modes. These findings are intended to provide a reference for the future deployment of 5G base stations in HST environments.