Selvi Lukman, Yul Yunazwin Nazaruddin, Bo Ai, Endra Joelianto

Corresponding email: lukmansylvia@gmail.com

Corresponding email: lukmansylvia@gmail.com

**Published at : ** 07 Oct 2022

**Volume :** **IJtech**
Vol 13, No 4 (2022)

**DOI :** https://doi.org/10.14716/ijtech.v13i4.5058

Lukman, S., Nazaruddin, Y.Y., Ai, B., Joelianto, E., 2022. Path Loss Modelling for High Speed Rail in 5G Communication System.

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Selvi Lukman | Doctoral Program of Engineering Physics, Faculty of Industrial Technology, Institut Teknologi Bandung, Bandung, Indonesia |

Yul Yunazwin Nazaruddin | Instrumentation and Control Research Group, Institut Teknologi Bandung, Bandung, Indonesia |

Bo Ai | State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China |

Endra Joelianto | 1. Instrumentation and Control Research Group, Institut Teknologi Bandung, Bandung, Indonesia 2. University Center of Excellence on Artificial Intelligence for Vision, NLP and Big Data Analytics, Ins |

Abstract

A new path loss model for
high-speed rail (HSR) in the 5G communication system is constructed in this
paper. The model is identified to obtain an accurate mathematical
representation of path loss multipath propagation in line of sight of HSR
scenarios. The grey box modelling utilization of Generalized Reduced Gradient
(GRG) and Genetic algorithm (GA) is applied to find the unknown parameters of
the constructed path loss model since some uncertainties in obtaining the
corresponded parameters are unavoidable to be collected in the field. Both
algorithms achieve excellent results in finding the unknown parameter values
with RMSE and MAPE evaluation which are converging finally to 2.779 and 1.701
%. The visualization of fitting plots is also presented, and GA provides a
better-adjusted agreement with the measurement dataset of HSR. Accordingly, the
constructed path loss model is successfully validated since it is capable of
following the dynamic characteristic of the original HSR path loss
measurement. The path loss model can then be utilized for the future dense
deployment of HSR infrastructures for the 5G communication network.

Generalized reduced gradient algorithm; Genetic algorithm; Grey-box modelling; Parameter estimation; Path loss model

Introduction

High-speed rail (HSR) refers to passenger rail systems utilizing a specialized rolling stock integrated system in the dedicated tracks. The deployment of FRCMS (Future Railway Communication System) is expected to fulfill the significantly increased demand for railway signaling systems. Some major communication functions in FRMCS are strictly related to railway operations with safety implications for the critical applications of the similar 5G technologies as radio communication cellular systems (Monserrat et al., 2020). Over the last decades, many researchers have been focusing on wireless communication technology that will be applied to HSR to ensure data transmission in the 5G framework (Suryanegara, 2018). A satisfying investigation of millimetre-wave propagation characteristics for HSR on field measurement in viaduct and tunnel scenarios has yielded the reliability of wireless transmission (Park et al., 2020).

The train backbone wireless
networking is implemented based on point-to-point links devices, and the study of path loss in multipath
propagation of HSR is stepping into new challenges when dealing with large-scale fading and shadowing. This types
of propagation are the most likely to
occur in railway scenarios. Since practically, HSR runs over 300 km/hr, it
suffers from severe fading, vehicle penetration losses (VPL) and unavoidable
Doppler effect. Accordingly, it is important to understand a chosen path loss
model that can be utilized in line of sight of HSR propagation for a 5G
communication system. At the same time, still
revealing the stability of parameter, accuracy, and functionality of the
limited measurement dataset.

It
brings out some new challenges in obtaining some path loss parameters value in
the field because of the combination of high velocity and spectrum allocation,
particularly for the future 5G-HSR wireless system level. Those uncertainties
parameters issues are almost existed not only in 5G cellular systems but also
in the 5G-HSR scenario therefore a path loss parameterization scheme related to
HSR environment scenarios from surrounding physical factors to the model
variables must accommodate causal functions of associated 5G-HSR particularly,
in line-of-sight variables which in this study, a new path loss model for
5G-HSR is constructed. The accuracy is validated by using a different approach
of grey box modelling to yield a comprehensive knowledge of path loss for
5G-HSR that allows network designers to plan the most optimal infrastructures
for HSR.

Furthermore,
the major limitation of the existing research is based on particular scenarios,
whether empirical or deterministic models. (He et al., 2018) was
motivated to observe the path loss model using key parameters such as coherence
time, polarization ratios, and Doppler shifts. A simulation was demonstrated
based on channel measurement for HSR communications in a 5G Millimeter-Wave
Band. As the future 5G technology requires many supporting technologies such as
base station infrastructures and fiber optics to be laid on the tracks (Suryanegara, 2016), a
local standard emerges as another solution to mainstream technologies. It led
to another challenge for a requirement of an accurate path loss that can be utilized
world-widely.

The early studies of the path loss model
are majorly conducted in cellular networks. The models are derived from
electromagnetic propagation theory (MacCartney et al., 2013), which
are not very accurate but easy to implement. Therefore, some correction mechanisms
must be constructed in a definite environment to achieve desired accuracy
results. (Phillips
et al., 2013) investigated additional parameters such as carrier
frequency, distance, transmitter, receiver heights and carrier frequency for
these cellular path loss models. The research yielded a more accurate path loss
model for a 5G cellular network. Accordingly, a prior knowledge or an explicit
measurement must be combined for a special path loss model for 5G-HSR (Zhong et al., 2021).

Several studies concerning 5G coverage
path loss prediction were evaluated in recent years with the development of
promising stochastic path loss models with the combination of antenna
configuration and beamforming in cellular networks (Sousa et al., 2021). Other
researchers introduced some key parameters of a line-of-sight characteristics (Sun et al., 2015). This
work provided important key parameters of large-scale path loss scenarios and
shadow fading for the future 5G communication system in urban macro cellular.
The comparison was presented as well at the frequency of 2, 10, 18, and 28 Ghz
in Aalborg, Denmark. Other works in
realizing 5G stochastic path loss models were studied by (Rappaport et al., 2017) by investigating
large-scale path loss models in wireless communication channels such as
mm-MAGIC, NYUSIM, and 3GPP TR38,901. The study concluded that additional random
variables in a path loss model must account for supplementary fading due to
scattering and multipath effects, which are dominant but difficult to obtain in
most stochastic path loss models.

One of the revolutionary technologies to
develop a critical signaling 5G-HSR is millimeter wave technology. It is
accessible to a massive capacity and bandwidth in frequency bands above 24 GHz.
Since millimeter-wave suffers from higher propagation loss, MIMO directional
antenna is widely accepted for designing a wireless communication system.
However, 5G-HSR employs different characteristics from traditional cellular
scenarios. Some specific characteristics in the 5G-HSR propagation environment,
such as line of sight dominance, Doppler shift, high velocity, and multiple
scenarios, must be carefully considered to obtain the optimal design and
performance (Zhang
et al., 2018). These considerations were related to diversity
effects and the Doppler shift. Tuned free space path loss model was analyzed,
and in this term a path loss model for HSR was estimated theoretically by
gaining its diversity effect and Doppler shift performed by Maximal Ratio
Combining (MRC) scheme (Roy & Fortier, 2004).

The capability of machine learning in
analyzing the existing system performance to be more accurate is undoubtedly
more resourceful for performing prediction tasks. It has been conducted several times by
performing various training and testing on path loss datasets where an
estimated model contains necessary predictions to be compared with the actual
dataset. It produced the best prediction model for path loss, majorly in the
cellular environment. In a single scenario, a requirement of evenly distributed
data must be sufficient to be fed to the model for given prediction accuracy.
However, the process becomes more complicated when incremental learning
algorithms are involved because of gradual model constructions are performed
without retraining accomplishment (Zhang et al., 2019).

The path loss models utilized in most existing
research do not contemplate physical factors. In accordance with it, this work
investigates an alternative approach to achieving an accurate path loss model
for 5G-HSR. A grey box modelling is initiated as the mathematical
representation of the path loss model for HSR in 5G communication system. The
provision of grey box modelling implementation with appropriate algorithms will
allow an optimal or almost optimal model that adjusts to the given path loss
measurement. The idea is to find the global minimum cost function in a search
space direction. The objective is to minimize the mean square error between the
prediction dataset from the optimized model with the real measurement established
in the field of study (?eho? & Havlena, 2011).

The utilization of grey box modelling to
obtain mathematical representation had been investigated to find the unknown
parameters for AMPS (Automated People Mover System) train by using the
Generalized Reduced Gradient Method (Suryana et al., 2020). A
near-optimal solution was also achieved by maximizing the sum-rate capacity of
a dynamic beam strategy to fulfill the critical quality of high-speed rail
requirements through a problem decomposition using GA (Gao et al., 2018). Garah et al. (2016) investigated
the Genetic Algorithm (GA) to produce a near-optimal solution for the GSM path
loss model. The comparison has yielded a good agreement with the measurement
result of the SUI model, COST-231 empirical path loss model, and COST-231 Hata.

Numerous path loss predictions in 5G
scopes with different methods have been analyzed mostly for the case of
cellular networks with recent contributions of machine learning implementation (Wu et al., 2010).
However, to the best of author’s knowledge, a grey box modelling approach to
validate path loss models for 5G-HSR has not been found in any literatures
because not only unavoidable measurement difficulties to be taken in the field
but also a comprehensive knowledge about causative relationship between path
loss parameters must be well constructed.

For a typical deployment of HSR wireless
communication infrastructures, the line of Sight (LOS) scenario is usually
referred to minimize radio waves reflection after traveling over a large area (Kanhere & Rapapport,
2021). The terminal equipment in HSR can add signal
interferences either constructively or destructively. Random and rapid
fluctuations in the received amplitude on a running HSR will cause a situation
where signals spread in the frequency domain. It leads to one of the
measurement difficulties, which copes with the path loss value. This parameter denotes a close-in measurement or a
free space assumption from the transmitter where the signal starts to attenuate
(Vahidi.,
2021). The values of fading under HSR scenario is perhaps the most
difficult parameter to achieve because of multipath propagation in a high
mobility environment; consequently, an alternative solution must be considered
for this parameter. The performance of wireless system transmission under
increased mobility of high-speed rail is dependent on sub-carrier signal
frequency shift and Orthogonal frequency division multiplexing [OFDM] due to
Doppler Effect. This parameter explains a functional relationship with
distance, and when the millimeter -Wave is taken into account for data
transmission, a higher Doppler effect will be emerged (Xiong et al., 2021).

In this study, the investigation of GRG
and GA for a grey box model identification is utilized to find some missing
parameters value of the constructed path loss model for HSR in a 5G
communication network. In this regard, the error between the output of the
optimized path loss model and path loss original measurement data will be
considered as objective functions with the visualization of fitting plots. The
rest of the paper is organized as follows. The grey box model is introduced in
Section 2. The optimization method and the constructed path loss model for
5G-HSR are investigated in Section 3. Section 4 displayed the simulation
results, and finally, the paper is concluded in Section 5.

Conclusion

As the significance of 5G wireless network planning
continues to grow, so will the requirements for better methods of measuring
wireless signal propagation and modelling a path
loss prediction for high speed rail.
This paper gives a broad overview of approaches given in a grey box modelling
to validate a newly constructed path loss model in the 5G communication system
for HSR. The grey box modelling with the application of GRG and GA has shown
excellent results in finding the unknown parameters value of the newly
constructed path loss model with satisfying results of RMSE convergence
approximately to 2.779 and MAPE value 1.701 %, respectively. The results
revealed that the new path loss model is successfully validated. The framework
in this study had shown that the created path loss model had a good adjusted
agreement with the dynamic characteristic of the original path loss measurement
which GA ultimately achieves. In future works, many possible directions in this
area with promising great impacts in high-speed rail crucial applications are
widely open for investigation. Comparative validation techniques and
measurement-based approaches are required, so the validated path loss model can
be utilized to design the future dense wireless communication infrastructures
for high-speed rail in a 5G communication network.

Acknowledgement

The Indonesian Ministry of Research
partially funds this research, Technology and Higher Education under WCU
Program managed by Institute Technology Bandung and Institute Technology
Bandung Research Program 2022. The authors would also like to acknowledge BJTU,
Beijing, China, for providing dataset measurement of a running bullet train.

Supplementary Material

Filename | Description |
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R1-EECE-5058-20220207224029.pdf | We have tried to attach our cover letter in the previous step, but unfortunately it fails all the time. So we attach our cover letter in this page. Thank you |

References

Bohlin, T.P., 2006. *Practical
Grey Box Identification: Theory and Applications. *London: Springer-Verlag

D’Angelo, G., Palmieri, F., 2021.
A Modified Genetic Algorithm with Gradient Based Local Search for Solving
Constrained Optimization Problems. *Information Sciences,* Volume 547, pp.
136–162

Gao, M., Ai, B., Zhong, Z., Liu,
Y., Ma, G., Zhang, Z., Li, D., 2018. Dynamic mmWave Beam Tracking for High
Speed Rail Communications. *In*; IEEE Wireless Communication and
Networking Conference Workshop (WCNCW), pp. 278–283

Garah, M., Djouane, L., Oudira, H.,
Hamdiken, N., 2016. Path Loss Models Optimization for Mobile Communication in
Different Areas. *Indonesian Journal of Electrical Engineering and Computer
Science*, Volume 3(1), pp. 126–135

Hauth, J., 2008. *Grey Box
Modelling for Non Linear Systems.* Dissertation, Fachbereich Mathematik der
Technischen Universitat Kaiserslautern

He, D., Ai, B., Guan, K., Zhong,
Z., Hui, B., Kim, J., Chung, H., Kim, I., 2018. Channel Measurement Simulation
and Analysis for High Speed Railway. Communications in 5G Millimetre-Wave Band.
*IEEE Transactions on Intelligent Transportation Systems,* Volume 19, pp.
3144–3158

He, D., Wang, Y., Chen, Y., Yuan,
Y., Zhen, T., Liu, J., 2020. Topology Optimization of Train Communication
Network Based on Improved Adaptive Genetic Algorithm. *In*: 2020
International Conference in Communication Technology (ICCT), pp. 182–189

Kanhere, O., Rappaport, T.S.,
2021. Position and Location of Futuristic Cellular Communications: 5G and
Beyond. *IEEE Communication Magazine*, Volume 59(1), pp. 70–75

Koshikawa, R., Terui, A., Mikawa,
M., 2007. *Solving Systems of Non Linear Equations with Genetic Algorithm and
Newton’s Method.* University of
Tsubuka, Tsubuka, Japan, https://doi.org/10.48550/arXiv.2007.05159

Laiyemo, A.O., 2018. *High
Speed Moving Networks in Future Wireless System*. Doctoral Dissertation, Universitatis
Ouluensis, Oulu, Finlad

MacCartney., J, Zhang., S, Nie.,
Rappaport, T.S., 2013. Path Loss Models for 5G Millimeter Wave Propagation
Channels in Urban Microcells’. *In*: 2013 IEEE Global Communications
Conference (GLOBECOM), pp. 3948–3953

Monserrat, J.F., Adam, D., Lamas,
C.B., Sultan, S., 2020. Envisioning 5G Enabled Transport. Report for World Bank
Group, Washington

Naqvi, S.I., Naqvi, A.H., Arshad,
F., 2019. An Integrated Antenna System for 4G and Millimetre-Wave 5G for Future
Handheld Devices. *IEEE Access, *Volume 7, pp. 116555-116566

Park, J.J., Lee, J., Kim K.W.,
Kim, M.D., 2020. 28 Ghz High Speed Train Measurements and Propagation
Characteristic Analysis. *In*: 14 th European Conference on Antennas and
Propagation (EuCAP). pp. 1–5

Phillips, C., Sicker, D.,
Grunwald, D., 2013. A Survey of Wireless Path Loss Prediction and Coverage
Mapping Methods. *IEEE Communications Surveys & Tutorials,* Volume
15(1), pp. 255–270

Rappaport, T.S., Sun, S., Shafi,
M., 2017. Investigation and Comparison of 3GPP and NYUSIM Channel Models for 5G
Wireless Communications. *In*: 2017 IEEE 86th Vehicular Technology
Conference (VTC-Fall), pp. 1–5

?eho?, J., Havlena, V., 2011. A
Practical Approach to Grey-box Model Identification. *In*: IFAC
Proceedings, Volume 44(1), pp. 10776–10781

Roy, S., Fortier, P., 2004.
Maximal-Ratio Combining Architectures and Performance with Channel Estimation
Based on a Training Sequence. *In*: IEEE Transactions on Wireless
Communications, Volume 3(4), pp. 1154–1164

Suryana, L.E, Joelianto, E.,
Hidayat, Y.A., 2020. Grey Box Modelling and Validation of AMPS Train. *International
Journal on Electrical Engineering and Informatics,* Volume 12(4), pp. 956–965

Suryanegara, M., 2016. 5G as
Disruptive Innovation: Standard and Regulatory Challenges at a Country Level. *International
Journal of Technology*, Volume 7(4), pp. 635–642

Suryanegara, M., Asvial, M.,
2018. The Patterns of Innovation Agendas on 5G Mobile Technology. *International
Journal of Technology*, Volume 9(5), pp. 876–887

Sousa, M., Alves, A., Vieira, P.,
Queluz, M.P., Rodrigues, A., 2021. Analysis and Optimization of 5G Coverage
Predictions Using a Beamforming Antenna Model and Real Drive Test Measurements.*
IEEE Access, *Volume 9, pp. 101787–101808

Sun, S., Thomas, T.A., Rappaport,
T.S., Nguyen, H.C., Kovacs, I.Z., Rodrigues, I., 2015. Path Loss, Shadow
Fading, and Line-of-Sight Probability Models for 5G Urban Macro-Cellular
Scenarios. *In*: 2015 IEEE Globecom Workshops (GC Wkshps), pp. 1–7

Vahidi, V., 2021. High Speed
Trains Communication Systems in 5G Cellular Network. *Digital Signal
Processing*, Volume 115, p. 103075

Wu, D., Zhu, G., A, Bo.,
2010. Application of Artificial Neural
Networks for Path Loss Prediction in Railway Environments. *In*: 2010 5th
International ICST Conference on Communications and Networking in China, pp.
1–5

Xiong, L., Zhang, Z., Yao, D.,
2021. Dynamic Doppler Prediction in High
Speed Rail Using Long Short Term Memory Neural Network. *Transactions on
Emerging Telecommunication Technologies,* Volume 32(9), p. e4269

Zhang, C., Wang, G.P., Jia, M.Z.,
He, R., Zhou, Li., Ai, B., 2018. Doppler Shift Estimator for Millimeter-Wave
Communication Systems on High-Speed Railways. *In*: 2018 IEEE/CIC
International Conference on Communications in China (ICCC), pp. 227–231

Zhang, Y., Wen, J.X., Yang, G.S.,
He, Z.W., Wang, J., 2019. Path Loss Prediction Based on Machine Learning:
Principle, Method, and Data Expansion. *Applied Sciences*, Volume 9(9), p.
1908

Zhong, G., Xiong, K., Zhong, Z., Ai,
B., 2021. Internet of Things for High-speed Railways. *Intelligent and
Converged Networks,* Volume 2(2), pp. 115–132