Cluster-Based Time-Variant Channel Characterization and Modeling for 5G-Railways

With the rapid development of high-speed railways, 5G for Railways (5G-R) is gradually replacing the legacy Global System for the Mobile Communications for Railway (GSM-R) system to meet growing communication demands. However, the large bandwidth, use of array antennas, and pronounced non-stationarity caused by high mobility make 5G-R channel modeling significantly more complex. Accurate and realistic channel models are therefore essential to support reliable system design. Despite this need, there is a scarcity of time-variant channel models specifically tailored to 5G-R frequency bands and scenarios, particularly those that adopt a cluster-based framework. This paper proposes a cluster-based time-variant channel model for 5G-R, extending the 3GPP framework by incorporating temporal evolution and dynamic multipath behavior. The model is constructed from comprehensive real-world measurements on a 5G-R private network. Key propagation features including path loss, Rice K-factor, delay and angular spreads are statistically characterized, with the Kolmogorov–Smirnov test validating their goodness of fit. Clustering analysis shows 1 to 6 multipath clusters per stationarity region, and their birth-death dynamics are effectively modeled using a first-order four-state Markov chain. Compared to the standard 3GPP CDL model, the proposed 5G-R CDL model exhibits longer delays, more balanced cluster power distribution, and wider angular spreads, better reflecting the unique features of the railway propagation environment. Moreover, validation with cluster dynamic parameters confirms that the birth–death model accurately captures cluster evolution, supporting the Markov process assumption. Link-level simulations further demonstrate that the proposed model more accurately captures real-world variations in bit error rate and signal quality than existing 3GPP models. The proposed model enhances realism and adaptability for link-level simulations, network planning, and physical layer algorithm design in high-speed railway scenarios, providing a solid foundation for future 5G-R channel standardization.