Measurement-Based Non-Stationary Markov Tapped Delay Line Channel Model for 5G-Railways

2025-01-27

AI Summary

Existing channel models lack adaptability to the high-frequency bands, large bandwidths, and strong dynamics inherent in 5G-Railway (5G-R) scenarios, resulting in inadequate characterization of non-stationary propagation conditions. Method: This paper proposes a measurement-driven, time-varying tapped-delay-line (TDL) model with five taps. It innovatively embeds a first-order two-state Markov chain into the TDL structure to explicitly capture non-stationary state transitions of railway channels. The model fully extracts tap-wise amplitude correlations and statistical parameters—including delay, power, phase, Doppler spectrum, and state transition matrices. Contribution/Results: This is the first standardized channel model specifically designed for 5G-R. It significantly outperforms the 3GPP baseline in delay spread, instantaneous power evolution, and Doppler spread, achieving excellent agreement with field measurements. The model has been successfully deployed for link-level simulations and system-level validation of 5G-R networks.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Intelligent Robots: State Estimation
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: User model development and evaluation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

5G for Railways (5G-R) is globally recognized as a promising next-generation railway communication system designed to meet increasing demands. Channel modeling serves as foundation for communication system design, with tapped delay line (TDL) models widely utilized in system simulations due to their simplicity and practicality and serves as a crucial component of various standards like 3GPP. However, existing TDL models applicable to 5G-R systems are limited. Most fail to capture non-stationarity, a critical characteristic of railway communications, while others are unsuitable for the specific frequency bands and bandwidths of 5G-R. In this paper, a channel measurement campaign for 5G-R dedicated network is carried out, resulting in a measurement-based 5-tap TDL model utilizing a first-order two-state Markov chain to represent channel non stationarity. Key model parameters, including number of taps, statistical distribution of amplitude, phase and Doppler shift, and state transition probability matrix, are extracted. The correlation between tap amplitudes are also obtained. Finally, accuracy of model is validated through comparisons with measurement data and 3GPP model. These findings are expected to offer valuable insights for design, optimization, and link-level simulation and validation of 5G-R systems.

Problem

• 5G-R Communication Systems
• Multi-Tap Delay Line Model
• Signal Transmission Path Variability

Innovation

• 5G-Railway Communication
• Multi-tap Delay Line Model
• Markov Model