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

TL;DR

This paper develops a measurement-based non-stationary 5G railway channel model using a Markov TDL approach, capturing key characteristics and validated against real data and standards.

Contribution

It introduces a novel 5-tap TDL model with non-stationarity for 5G-R, incorporating Markov chains and detailed parameter extraction from measurements.

Findings

Model accurately reflects non-stationary channel behavior.

Parameters align with measurement data and 3GPP standards.

Provides a practical tool for 5G-R system design and simulation.

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.