3D massive MIMO channel model for high-speed railway wireless communication

3D massive MIMO channel model for high-speed railway wireless communication

Citation Author(s):
Chengjian
Liao
Submitted by:
Chengjian Liao
Last updated:
Thu, 01/16/2020 - 09:50
DOI:
10.21227/j6f9-7233
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Abstract: 

In the fifth generation (5G) wireless communication, high-speed railway (HSR) communication is one of the most challenging scenarios. By adopting massive multi-input multi-output (mMIMO) technology in HSR communication, the design of the underlying communication system becomes more challenging. Some new channel characteristics must be studied, such as non-stationarity in space, time and frequency domains. In this paper, two models are established for the two states of HSR at rest or in motion. When the train is stationary, a three-dimensional (3D) stationary channel model based on the single-ring distribution of the scatterers is established for a uniform planar array in a stable propagation scene. When the train is moving, in order to describe the non-stationary characteristics of the channel, a geometric random non-stationary channel model based on a cylinder scattering model is established by introducing the birth and death process of the propagation path and time-varying characteristics of the channel parameters. Moreover, a time evolution algorithm for time-varying channel parameters is proposed, and a modularization study of key parameter update algorithms is conducted, including the geographical location parameters of the transceiver and the scatterer, the number of effective propagation paths, and the delay. Finally, the antenna array response and spatial correlation matrix are derived for the proposed 3D channel model. The influence of the number of antennas on beam directivity is mainly studied, also the influences of parameters such as the scatterer distribution radius, antenna height, azimuth, and antenna element spacing on the eigenvalues of the correlation matrix are analyzed.

Instructions: 

With the rapid development of information globalization, wireless communication has penetrated into all aspects of people's lives. It is expected that by 2020, there will be a fifth-generation (5G) wireless communication system with ultra-high user experience data rate, ultra-high connection density and ultra-high power [1]. As a typical application scenario of 5G communication, in recent years, high-speed railway (HSR) communication scenario have received widespread attention. A large number of HSR users require a huge amount of communication data, which far exceeds the capacity of the current HSR communication system. HSR wireless communication systems also face various challenges, such as frequent handovers, large doppler frequency shifts, and rapidly experiencing different scenarios [2-5]. 

In order to solve the above problems, massive multi-input multi-output (mMIMO) technology, which can obtain diversity gain, multiplexing gain, and power gain by deeply digging wireless space dimensional resources, is introduced into HSR system [6],[7]. In HSR system with mMIMO, the transmission capacity and communication quality of wireless communication links can be greatly improved, and some challenges brought by HSR with traditional network architectures can also be overcome [8]. Since, the speed of the train in HSR is very fast, and the antenna dimension increases significantly after the configuration of mMIMO. As a result, the wireless channel propagation environment becomes extremely complicated. For the design, performance evaluation, and testing of future HSR communication systems with mMIMO, accurate and effective channel model is essential [9],[10]. 

In open literature, various HSR channel models have been proposed and analyzed to describe various HSR communication scenarios. In [11], a deterministic ray-tracing HSR channel model is provided to simulate HSR tunnel channels, and several channel characteristics such as frequency selectivity and Doppler spreading are studied. In [12], a three-dimensional (3D) deterministic ray-tracing HSR tunnel channel model is given. In constructing the channel model, the Doppler frequency shift and delay when two trains meet are considered. The ray tracing channel model has high accuracy by merging a large amount of channel information, but it also leads to high computational complexity. In [13], a two-dimensional (2D) non-stationary geometry-based stochastic model (GBSM) was proposed for HSR MIMO channels. The statistical characteristics of time-varying small-scale fading channels, such as autocorrelation function (ACF), spatial interaction correlation function (CCF) and local scattering function (LSF), are analyzed. In [14] and [15], the HSR channel model of the tunnel scenario is studied. In [16], a novel geometry-based random cluster model of HSR channels was proposed based on the measured data. Random geometric parameters are used to describe the time evolution of clustering. All the above models focus on HSR channels without considering mMIMO technology.

In order to meet the increasing demand for HSR communication, HSR communication systems introduce some emerging 5G technologies, such as mMIMO technology, to improve network capacity and reliability of communication links. In [17], the performance analysis of mMIMO in HSR communication was studied. The effects of train speed, K-factor, and signal-to-noise ratio (SNR) are discussed. It shows that mMIMO technology can be applied to HSR communication to improve communication link reliability. In order to accurately characterize the HSR mMIMO channel, we assume that the scatterers are distributed on a cylinder, and establish a geometrically random non-stationary channel model that reflects the time-varying characteristics of the communication process. We propose a method for calculating the time evolution of channel parameters, and study the spatial correlation characteristics of a 3D stationary channel model, which provides a theoretical basis for the research and performance verification of HSR wireless communication systems.

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[1] Chengjian Liao, "3D massive MIMO channel model for high-speed railway wireless communication", IEEE Dataport, 2020. [Online]. Available: http://dx.doi.org/10.21227/j6f9-7233. Accessed: Feb. 27, 2020.
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doi = {10.21227/j6f9-7233},
url = {http://dx.doi.org/10.21227/j6f9-7233},
author = {Chengjian Liao },
publisher = {IEEE Dataport},
title = {3D massive MIMO channel model for high-speed railway wireless communication},
year = {2020} }
TY - DATA
T1 - 3D massive MIMO channel model for high-speed railway wireless communication
AU - Chengjian Liao
PY - 2020
PB - IEEE Dataport
UR - 10.21227/j6f9-7233
ER -
Chengjian Liao. (2020). 3D massive MIMO channel model for high-speed railway wireless communication. IEEE Dataport. http://dx.doi.org/10.21227/j6f9-7233
Chengjian Liao, 2020. 3D massive MIMO channel model for high-speed railway wireless communication. Available at: http://dx.doi.org/10.21227/j6f9-7233.
Chengjian Liao. (2020). "3D massive MIMO channel model for high-speed railway wireless communication." Web.
1. Chengjian Liao. 3D massive MIMO channel model for high-speed railway wireless communication [Internet]. IEEE Dataport; 2020. Available from : http://dx.doi.org/10.21227/j6f9-7233
Chengjian Liao. "3D massive MIMO channel model for high-speed railway wireless communication." doi: 10.21227/j6f9-7233