Review Article | | Peer-Reviewed

Review of Channel Measurements and Modeling for Successful 5G System Deployments

Received: 5 August 2024     Accepted: 27 August 2024     Published: 20 September 2024
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Abstract

The deployment of 5G wireless networks has enabled the investigation of numerous potential applications across a variety of sectors. To enhance the efficiency of 5G systems, it is imperative to have a thorough understanding of millimeter-wave wireless channels, different multi-access techniques, massive MIMO technologies, beamforming, modulation, and coding. Adjusting the channel modeling approach to accommodate specific characteristics of the deployment site, such as geographical obstructions like hills, tunnels, road infrastructure, and mountains, may prove to be crucial. This review paper delves into the challenges associated with channel modeling, underscoring the importance of multipath components and the diverse measurement techniques required for enhancing 5G communication. Additionally, it delves into the complexities of accurately depicting the behavior of wireless channels in various scenarios and assesses the key factors that could significantly affect the functionality of 5G networks across different environments. For instance, it becomes clear that indoor channels provide a greater impediment than outdoor channels because barriers such as walls, furniture, and human activities can impede signal transmission and interrupt communication. Indoor channels display complex characteristics that include fluctuations in the angles at which signals arrive, transmission of numerous signals over different paths, and a wide range of scattering qualities that are specific to indoor environments. Hence, it is crucial to modify the measurement procedures to correspond to the unique characteristics of indoor channels. Indoor wireless communication relies on channels available both within and outside the structure. Evaluation aspects such as, macroscopic fading, microscopic fading, and shadow fading are critical because these elements have a significant impact on the channel capacity.

Published in International Journal of Wireless Communications and Mobile Computing (Volume 11, Issue 2)
DOI 10.11648/j.wcmc.20241102.12
Page(s) 31-38
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Millimeter Wave, 5G, Massive Multiple Inputs Multiple Outputs, Beamforming, Angle of Arrival, Angle of Departure, High Speed Train, Vehicle-2-Vehicle

1. Introduction
Experts expect the imminent deployment of 5G technology to enhance inter-device communication across diverse network configurations . Experts project that this technological progress will facilitate faster wireless data sharing, reduced latency, and enhanced transmission speeds . Researchers are currently investigating various emerging technologies within the realm of 5G systems, such as millimeter-wave communication, for their potential to offer extensive bandwidth, despite the possibility of signal degradation at higher frequencies . Presently, researchers are exploring the integration of massive multiple-input, multiple-output (MIMO) communication as a solution to this issue . The inclusion of massive MIMO has the potential to improve the system's overall performance and reliability. The continuous evolution of 5G technologies underscores their adaptable utility in rural, suburban, and urban settings. The efficacy of any wireless communication system is contingent upon a precise characterization of the transmission channel.
The development of transmission channels for 5G technology poses several challenges within the framework of diverse technologies. The challenge is that devices must have the capacity to function well over a broad spectrum of frequencies, ranging from 500 MHz to 100 GHz, and support numerous frequency bands . In addition, the system needs to work well in a lot of different places, including rural, urban, suburban, indoor, high-speed trains (HST), and more . Bidirectional three-dimensional modeling is required for accurate three-dimensional propagation and antenna design . As communication systems progress, they need to integrate beam-tracking and mobility capabilities . Transmitters and receivers must have consistent spatial properties, such as signal weakening at different levels, being able to see without any problems, being able to work indoors or outdoors, choosing the right frequencies, and interacting with frequencies that are close by . An accurate description of large-scale MIMO channels requires a comprehensive understanding of spherical wavefronts and array non-stationarity . For the successful capture of Doppler frequencies and non-stationarity, it is crucial to consider high-mobility situations, namely speeds that surpass 500 km/h in high-speed train (HST) environments . The paper is organized as follows, Section II discusses channel measurement for 5G wireless networks in millimeter wave, massive MIMO, high-speed trains, and vehicle-to-vehicle communications. Section III studies the channel modeling technique for millimeter Wave networks, and section IV gives the conclusion on the review paper.
2. Channel Measurements for 5G Wireless Networks
5G technology performs efficiently in a variety of challenging environments, such as tunnels and underground areas. In these settings, investigating channel modeling presents distinct challenges. These environments often rely on channel measurements to assess their unique conditions and dispersion characteristics. Currently, intensive efforts are underway to address these challenges. This section offers a brief summary of the different methods employed for measuring and characterizing communication channels, with a specific emphasis on advancements in 5G technology.
2.1. Millimeter Wave (mm-Wave) Channel Measurements
Currently, scholars are encountering a significant obstacle in attaining high data rates during mobility. To tackle this issue, a transition towards millimeter-wave frequencies is essential, as they have the capability to facilitate quicker data transfer. Utilizing millimeter-wave technology allows for faster data speeds and simplified air interfaces because of the abundant bandwidth in this frequency spectrum . In recent times, there has been a rising interest in employing millimeter-wave frequencies for the advancement of 5G technologies. The opportunity for high-speed data transmission enabled by millimeter-wave carrier frequencies presents a promising prospect for upcoming wireless networks . Elevated operating frequencies not only increase the bandwidth, but also increase the capacity of radio antennas to achieve greater directivity, thereby improving both the accuracy of the system signal and its spectral efficiency . Operating within the millimeter waveband provides the benefit of mitigating minimal diffraction effects, which are typically a constraint in wireless communication systems. The diminished diffraction resulting from shorter wavelengths within this frequency band enhances the reliability of millimeter-wave communication systems .
Most tests conducted on mmWave channels have focused on significant path loss, which presents a major hurdle to deploying mmWave communication systems. The 38 GHz and 28 GHz measurements were meant to get information on things like RMS delay spread, angle of departure (AOD), path loss, angle of arrival (AOA), and the way potential mmWave channels reflect light . The authors compared urban and light urban areas and discovered that signal propagation was very different. They found that multipath delay spread was higher in large urban areas compared to lighter urban zones. The research conducted in examined an improved version of the well-established close-in (CI) and floating intercept (FI) path loss models. The experimental configuration utilized an enclosed corridor, focusing on both vertical-horizontal (VH) and vertical-vertical (V-V) antenna polarization. A significant outcome of this research indicates that the improved models demonstrate superior performance across different antenna polarizations compared to their traditional versions, thereby validating their elevated accuracy levels. These findings imply that the refined or modified CI and FI models yield more precise path loss predictions in enclosed environments, especially within the framework of 5G networks. In , researchers investigated a variety of models, such as single-frequency, directional, omnidirectional, and multi-frequency to analyze and compare the results.
2.2. Massive MIMO Channel Measurement
MIMO approaches have attracted significant interest in wireless communication because they can provide several benefits, including fast data transfer and dependable connections, without requiring improvements in the signal strength. Fourth-generation LTE mobile networks are currently deploying MIMO, a technology that effectively integrates with OFDM.
Using a 50 MHz bandwidth, researchers evaluated frequencies at 2.6 GHz. The base station (BS) conducted the evaluations using a 128-element linear virtual array . The analysis revealed significant disparities in the angular power spectrum (APS), K-factor, and channel gain within the linear array. In massive MIMO systems, the research shows that the assumptions of plane waves or far-fields have not been tested. In addition, a MIMO arrangement using a linear array incorporates several important antennas. The experiment in used a frequency spectrum spanning 15 gigahertz, with a bandwidth of 5 gigahertz. The virtual planar antenna array consisted of 1600 elements, which were separated into various subarrays to study the dynamic properties of the massive MIMO channels for each antenna element. This study investigates many critical aspects, including delay spread, azimuth angular spread of arrival (AASA), elevation angular spread of arrival (EASA), and the K-factor. The array plane clearly illustrates the significant variations in these characteristics among individual sites. In , the papers evaluated the effectiveness of channels utilizing Multiple-Input Multiple-Output (MIMO) technology. The paper examined frequencies ranging from 16, 11, 38, and 28 GHz for indoor channel environments.
2.3. High-Speed Train (HST) Channel Measurement
The propagation channel has a significant impact on the study and implementation of a remote control system for railway routes, especially in the context of high-speed trains. People view high-speed trains as a cost-efficient and environmentally friendly option for long-distance mass transit. The GSM-R system was designed as a vital component for integrating traffic management and security systems on railroads using wireless communication. Significant disparities arise in the transmission routes between the cellular system and high-speed trains, mainly because of variations in antenna placement and surrounding transmission conditions . Directional transmitting antennas are necessary for enabling communication in high-speed train systems.
Precisely delineating signal transmission routes is a significant challenge in the realm of scientific investigation. Prior research has built specialized radio propagation models to tackle this difficulty, specifically in the context of high-speed train networks. The main goal of these models, as explained in , is to accurately guess where trains are by looking at things like the K-factor, the Doppler frequency, and the time delay spread, all of which change when the signal strength does. The studies are performed using data acquired at a frequency of 2.35 GHz. Also, researchers have created position-based channel models that accurately show channel properties. This makes it easier to test and verify wireless communication systems in similar situations. The study described in conducted a measuring campaign to investigate the communication channels used by high-speed train networks that employ Long-Term Evolution Advanced (LTE-A) technology. Cell-specific reference signals (CRSs) facilitated the process of obtaining channel impulse responses (CIRs). The findings suggest that most channels primarily use non-line-of-sight (NLOS) passageways instead of line-of-sight (LOS) routes. The analysis of delay patterns yielded valuable insights on geometric properties, such as train velocity and the most efficient route between the transmitter and receiver. In addition, a thorough analysis was performed to examine and evaluate the properties of signal attenuation in different orientations, specifically focusing on the Universal Mobile Telecommunications System (UMTS). In , a ray-tracing technique was used to construct models of environmental structures at various elevations, both above and below the surface. Certain geographic regions limit these models, and they do not account for random occurrences .
2.4. Vehicle-to-Vehicle (V2V) Channel Measurement
Vehicle-to-vehicle communication is essential for improving road safety and effectively managing traffic congestion . The propagation channel's attributes have a significant impact on wireless systems' efficiency. The study described in aimed to assess the V2V channel in both urban and highway settings, with a particular emphasis on the 5 GHz frequency range. Throughout the investigation, scientists performed tests with a 50 MHz test signal and omnidirectional antennas positioned both within and outside the automobiles. The channels were modeled using frequency correlation functions and the root mean square (RMS) delay spread. In highly crowded metropolitan regions, the estimated root mean square (RMS) delay spread is approximately one microsecond, leading to coherence bandwidths ranging from 1 to 2 megahertz (MHz) . Currently, there is ongoing research on the practicality of using the 2.4 GHz radio band. Changes in Doppler spread and signal weakening have been studied before in close-range indoor machine-to-machine (M2M) communication situations, as shown in . Researchers in recorded the results of analytical tests conducted under comparable channel conditions.
The study in conducted calculations on the unique characteristics of narrowband communications in the 5 GHz frequency region. In , the authors investigated the phenomenon of fading in vehicle channels by evaluating the radio channel data obtained from various traffic scenarios. The authors analyzed samples of the time-varying frequency response to identify large-scale fading (LSF), and they created statistical models for the second-order Doppler spread and time-frequency-dependent RMS delay spread. The distributions of Doppler spread and RMS delay spread under different traffic conditions.
3. Channel Modeling in 5G Wireless Networks
Having a thorough grasp of the millimeter-wave spectrum is crucial for optimal communication networks. The visualization in Figure 1 illustrates the attributes of the millimeter-wave spectrum. Accurate channel modeling plays a critical role in the efficient functioning of millimeter-wave communication systems. Parameters such as Root Mean Square (RMS), Angle of Arrival (AoA), Angle of Departure (AoD), signal attenuation, and reflections from surfaces are significant factors influencing the propagation of millimeter waves over different types of surfaces.
Figure 1. Millimeter Wave Spectrum .
Recently, academic research in the field of millimeter-wave (mm-wave) technology has focused on finding ways to test indoor channels without using blind methods, by using pilot/data support systems. Each of these scholarly inquiries has focused on specific facets of this subject matter. For example, highlighted a particular study that involved setting up a measurement setup in an indoor office setting using a 60 GHz frequency band and a Vector Network Analyzer (VNA). The principal objective of this investigation was to replicate a wide signal by transmitting it through a specific channel for a defined duration, followed by utilizing the VNA output to scrutinize the amplitude and phase attributes.
Figure 2. Vector Network Analyzer or Channel Sounder .
Figure 2 shows how the Vector Network Analyzer (VNA) scans a single carrier across different frequencies, looking at how the signal's amplitude and phase change at each frequency point. The VNA is capable of discerning a channel's intricate frequency traits and impulse response by applying an inverse Fourier transform to the collected data . Engineers frequently use these instruments to analyze channel responses in close indoor environments or restricted spaces, but their slow scanning speeds lead to stagnant or slow channel changes over extended periods . Furthermore, their cumbersome design poses challenges in terms of mobility, especially when endeavoring to synchronize the transmitter and receiver over distances exceeding a few hundred meters.
Figure 3. Rotated Directional Antenna Measurement Method .
In general, there are two primary methodologies for evaluating the angular properties of indoor millimeter wave (mmWave) channels. One method involves the utilization of a Rotating Directional Antenna (RDA), while the other utilizes a standardized uniform virtual array (UVA) . These methodologies are predominantly effective in quasi-static scenarios due to the time-intensive nature of scanning the antenna in three-dimensional space. There is an RDA method for getting data that uses a much focused antenna along with either a vector network analyzer (VNA) or a channel sounder . Figure 3 illustrates how a mechanism affixes the antenna, enabling precise scanning at designated angles in both horizontal and vertical planes while gathering pertinent data .
The precise positioning of the antenna is crucial for determining the Angle of Arrival (AoA) or Angle of Departure (AoD) of the Multipath Components (MPC), as dictated by its narrow signal beam. The accurate placement of the antenna plays a critical role in correctly measuring the angles at which signals enter or leave a multipath setting.
The UVA technique involves replacing the RDA with a 3-D omni-directional antenna that moves in small steps within a space smaller than half a wavelength , as shown in figure 4.
Figure 4. Uniform Virtual Array configuration and direction characterization .
Communication methods at UVA frequently incorporate mathematical techniques, including subspace methods and rotational symmetry. Beyond simple methods, UVA also uses more complex ones, like space-alternating generalized expectation maximization (SAGE) and Richter's ML parameter estimation model, to guess what multipath components (MPCs) are like. The primary objective of the IEEE 802.15.3c and 802.11ad standards was to facilitate wireless communication within the 60-GHz frequency band. The IEEE 802.15.3c standard stresses channel design, with a particular emphasis on the analysis of the Angle of Arrival (AoA) in the horizontal plane. On the other hand, the IEEE 802.11ad standard caters to a specific channel type, providing accurate data on angle parameters in two distinct orientations. Both standards employ a finely tuned Saleh (S-V) model to meet the distinct structural demands of the channel. The IEEE 802.11ad channel model enhances the Saleh-Valenzuela model by integrating cluster characteristics in both temporal and spatial dimensions, particularly in indoor settings. This study utilizes an improved S-V model to evaluate the characteristics of a 60-GHz indoor channel, with the objective of examining the impact of environmental elements and their combined properties in terms of time and space. In addition, the channel model incorporates sophisticated 3D S-V-based millimeter-wave channel models specifically designed for indoor settings. The papers examined distinct features inside clusters and investigate shared qualities among different clusters.
Indoor channels provide a greater impediment than outdoor channels because barriers such as walls, furniture, and human activities can impede signal transmission and interrupt communication. Indoor channels display complex characteristics, including fluctuations in the angles at which signals arrive, the transmission of numerous signals over different paths, and a wide range of scattering qualities that are specific to indoor environments. Hence, it is crucial to modify measuring procedures to correspond with the unique characteristics of indoor channels. Indoor wireless communication relies on channels that are available both within and outside of structures. Evaluating aspects such as macroscopic fading, microscopic fading, and shadow fading is critical because these elements have a significant impact on channel capacity.
Research at the 60 GHz frequency, with a special focus on generating channel models suited for WLAN networks led to the development of the IEEE 802.11ad standard. The main goal of this research is to improve the efficiency and performance of wireless local area networks by examining signal transmission characteristics in the 60 GHz frequency band. The model examines the channel's response to impulses, considering the polarization of antennas and their capacity to focus beams in certain directions. This model, as described in the equations below, incorporates the polarization features described in .
ht, φtx, θtx, φrx, θrx= iHiCi (t- Ti, φtx- φtxi, θtx- Ξtxi,φrx- φrxi, θrx- Ξrxi)(1)
Where,
H () = Channel impulse response consists of inputs from various channel segments. This happens when we merge the feedback from different channel groups.
t, φtx, θtx, φrx, θrx = Measurements of the time, azimuth, and elevation were taken at both the sender and receiver positions;
Ti, φtxi, Ξtxi, φrxi, Ξrxi = Time angular coordinates for each cluster denote the unique spatial and temporal locations of the cluster within a particular timeframe;
H(i)= 2 x 2 Matrix gain of a specific cluster refers to the measurement that characterizes the polarization aspects of a cluster;
C(i)= The mathematical equation that describes the channel impulse response for a particular group, known as the ith cluster, can be expressed in as:
C(i) (t,φtx, θtx,φrx,θrx) =kαi,kδ(t-τ(i,k))δφtx- φtxi,kδ(θtx- θtxi,k)δ(φrx- φrxi,k)δ(θrx- θrxi,k)(2)
Here,
δ (0) = Dirac delta function
α(i,k)= amplitude of the k-th ray of the i-th cluster
τ(i,k), φtxi,k, θtxi,k, φrxi,k, θrxi,k = Using its angular coordinates in relation to time and direction, one can determine the exact position of a ray in a cluster, providing accurate positional information within a specific category.
At 60 GHz for IEEE 802.11ad, the research results look at different parts of all rays, such as their strength, phase, and polarization, as well as their spatial and temporal properties . The azimuth and elevation angles at both the sender and receiver positions influenced the beam's properties. The IEEE 802.11ad model in allows for the analysis of three propagation scenarios: conference room, cubicle, and living room setup.
4. Conclusions
This review paper delves into the intricate and complex aspects of channel modeling, channel measurements, and the numerous multipath components (MPC) that play a crucial role in shaping the complexity of building 5G networks. Understanding the characteristics of wireless channels is critical for channel modeling, particularly in scenarios such as underground tunnels, mountainous regions, and construction sites along roadsides. Precise channel measurements play a key role in improving the efficiency and reliability of channel models. Employing specialized measurement techniques tailored to address the unique characteristics of the subject under study is crucial to achieving accurate outcomes. To sum it up, the precise measurement and characterization of 5G channels hold immense importance, as they will have a profound impact on the future progression and advancement of wireless communication technologies in the coming years.
Abbreviations

MIMO

Multiple Inputs Multiple Outputs

mMIMO

Massive Multiple Inputs Multiple Outputs

5G

Fifth Generation communication

MPC

Multi-path Component

AoA

Angle of Arrival

APS

Angular Power Spectrum

BS

Base Station

RMS

Root Mean Square

AASA

Azimuth Angular Spread of Arrival

EASA

Elevation Angular Spread of Arrival

AoD

Angle of Deviation

mmWave

Millimeter-Wave

HST

High Speed Train

V2V

Vehicle2vehicle

UMTS

Universal Mobile Telecommunications System

S-V

Saleh-Valenzuela.

M2M

Machine2Machine

LSF

Large Scale Fading

OFDM

Orthogonal Frequency Division Multiplexing

LTE-A

Long-Term Evolution Advanced (LTE-A)

C-S-R-S

Cell-Specific Reference Signals

CIR

Channel Impulse Response

NLOS

Non-line of Sight

LOS

Linear of Sight

VNA

Vector Network Analyzer

SAGE

Space-Alternating Generalized Expectation Maximization

UVA

Uniform Virtual Array

RDA

Rotated Directional Antenna

Author Contributions
Akorede Kola-Jnr: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Software, Writing – original draft
Francis Ibikunle: Conceptualization, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – review & editing
Ariba Folashade Olamide: Conceptualization, Investigation, Methodology, Supervision, Validation, Writing – review & editing
Bright Unaegbu: Conceptualization, Formal Analysis, Funding acquisition, Resources, Validation, Visualization
Olubiyi Joshua: Conceptualization, Data curation, Resources, Visualization
Adedire Collins: Conceptualization, Investigation, Methodology, Resources, Validation
Conflicts of Interest
The authors declare no conflicts of interest.
References
[1] S. Visvesvaraya, "Study on four disruptive technologies for 5G and beyond wireless communication," CSI Transactions on ICT, vol. 8, p. 171–180, June 2, 2020.
[2] Bartelt, "5G transport network requirements for the next generation fronthaul interface," EURASIP Journal on Wireless Communications and Networking, vol. 89, 2017.
[3] Mehrdad Shariat, "A Flexible Network Architecture for 5G Systems," Wireless Communication and Mobile Computing, August 2, 2019.
[4] E. E. Salah, F. Mikael, S. Panagiotis, Z. Gerd, and M.-S. David, "5G service requirements and operational use cases: analysis and METIS II vision," 2016 European Conference on Networks and Communications (EuCNC), 27–30 June 2016.
[5] Yong Niu, "A survey of millimeter wave communications (mmWave) for 5G: opportunities and challenges," The Journal of Mobile Communication, Computation, and Information, p. 2657–2676, 9 April 2015.
[6] S. D. J. Mercy Sheeba, "Beamforming Techniques for Millimeter Wave Communications: A Survey," Emerging Trends in Computing and Expert Technology, p. 1563–1573, November 7, 2019.
[7] Said El-Khamy, "A smart multi-user massive MIMO system for next-generation wireless communications using evolutionary optimized antenna selection," Telecommunication Systems Modelling, Analysis, Design, and Management, p. 309–317, 2017.
[8] C. Yi and H. Chong, "Channel Modeling and Characterization for Wireless Networks-on-Chip Communications in the Millimeter Wave and Terahertz Bands," IEEE Transactions on Molecular, Biological, and Multi-Scale Communications, vol. 5, no. 1, pp. 30–43, October 2019.
[9] C. S. a. M. T. Guneser, "Review of 5G Channel Models and Modeling of Indoor Path Loss at 32 GHz," American Journal of Computer Science and Engineering Survey Op, vol. 9, no. 1, 2021.
[10] Muhammad-Yasir, "G2A Communication Channel Modeling and Characterization Using Confocal Prolates," Wireless Personal Communications, vol. 115, p. 745–787, 21 June 2020.
[11] Pavel Kukolev, "In-vehicle channel sounding in the 5.8-GHz band," EURASIP Journal on Wireless Communications and Networking, 2015.
[12] J. W. Xueru Li, "Massive MIMO with multi-cell MMSE processing: exploiting all pilots for interference suppression," EURASIP Journal on Wireless Communications and Networking, 26 June 2017.
[13] O. Renaudin, V.-M. Kolmonen, P. Vainikainen, and C. Oestges, "Wideband measurement-based modeling of inter-vehicle channels in the 5 GHz band," Proceedings of the 5th European Conference on Antennas and Propagation (EUCAP), 11–15 April 2013.
[14] Xiaoling Xu, "Key technology and application of millimeter wave communications for 5G: a survey," Cluster Computing, vol. 22, p. 12997–13009, 2019.
[15] O. H. Koymen, P. Andrzej, S. Sundar, and L. Junyi, "Indoor mm-wave channel measurements: a comparative study of 2.9 GHz and 29 GHz," IEEE Global Communications Conference (GLOBECOM), 6–10 December 2015.
[16] Theodore S. Rappaport, "Overview of Millimeter Wave Communications for Fifth-Generation (5G) Wireless Networks—with a Focus on Propagation Models," IEEE Transactions on Antennas and Propagation, August 2017.
[17] L. Zhijian, D. Xiaojiang, C. Hsiao-Hwa, A. Bo, C. Zhifeng, and W. Dapeng, "Millimeter-Wave Propagation Modeling and Measurements for 5G Mobile Networks," IEEE Wireless Communications, vol. 26, no. 1, pp. 72–77, February 2019.
[18] T. T. Oladimeji, P. Kumar, and M. K. Elmezughi, “Performance analysis of improved path loss models for millimeter-wave wireless network channels at 28 GHz and 38 GHz,” PLoS ONE, vol. 18, no. 3, p. e0283005, Mar. 2023,
[19] I. S. P. P. Aruna Kumari, "Signal propagation analysis at 28GHz and 73GHz millimeter wave bands for next generation networks," International Journal of Image, Graphics, and Signal Processing, vol. 11, no. 8, 2019.
[20] Weiqiang Tan, "Multiuser precoding scheme and achievable rate analysis for massive MIMO systems," EURASIP Journal on Wireless Communications and Networking, volume 22, August 2018.
[21] J. Chen, X. Yin, X. Cai, and W. Stephen, "Measurement-Based Massive MIMO Channel Modeling for Outdoor LoS and NLoS Environments," IEEE Access, vol. 5, pp. 2126–2140, 16 January 2017.
[22] J. Huang, W. Cheng-Xiang, F. Rui, S. Jian, and Z. Wensheng, "Multi-Frequency mmWave Massive MIMO Channel Measurements and Characterization for 5G Wireless Communication Systems," IEEE Journal on Selected Areas in Communications, vol. 35, no. 7, July 2017.
[23] F. Rui, W. Cheng-Xiang, H. Jie, G. Xiqi, S. Sana, and H. Harald, "Classification and Comparison of Massive MIMO Propagation Channel Models," IEEE Internet of Things Journal, vol. 9, no. 23, pp. 23452–23471, December 2022.
[24] Cheng-Xiang Wang, "Recent advances and future challenges for massive MIMO channel measurements and models," Special Focus on 5G Wireless Communication Networks, vol. 59, p. 1–16, January 7, 2016.
[25] L. Jian-zhi Li, "Indoor massive multiple-input multiple-output channel characterization and performance evaluation," vol. 18, p. 773–787, June 30, 2017.
[26] S. Joseph Isabona, "Downlink Massive MIMO Systems: Achievable Sum Rates and Energy Efficiency Perspective for Future 5G Systems," Wireless Personal Communications, vol. 96, p. 2779–2796, 16 May 2017.
[27] C. Rui, L. Wen-Xuan, M. Guoqiang, and L. Changle, "Development Trends of Mobile Communication Systems for Railways," IEEE Communications Surveys & Tutorials, vol. 20, no. 4, pp. 3131–3141, July 24, 2018.
[28] S. Paul Unterhuber, "Path loss models for train-to-train communications in typical high-speed railway environments," 11th European Conference on Antennas and Propagation (EuCAP 2017), 27 February 2018.
[29] H. Lei Zhang, "Broadband Wireless Channel in Composite High-Speed Railway Scenario: Measurements, Simulation, and Analysis," Wireless Communications in Transportation Systems, May 30, 2017.
[30] J. Rodriguez-Pineiro, P. Suarez-Casal, M. Lerch, S. Caban, and J. A. Garcia-Naya, "LTE Downlink Performance in High Speed Trains," 2015 IEEE 81st Vehicular Technology Conference (VTC Spring), 11–14 May 2015.
[31] Z. W. Miao Hu, "Theoretical Analysis of Obstruction’s Influence on Data Dissemination in Vehicular Networks," Lecture Notes of the Institute for Computer Sciences, Social Informatics, and Telecommunications Engineering (LNICST), vol. 184, p. 105–116, 17 December 2016.
[32] R.-S. H. Zhang-Dui Zhong, "Radio Propagation and Wireless Channel for Railway Communications," Dedicated Mobile Communications for High-speed Railway, pp. 57–123, August 15, 2017.
[33] Azpilicueta Leyre, "Wireless channel properties for vehicular environments," Radio Wave Propagation in Vehicular Environments, December 2020.
[34] X. Zhao, S. Li, X. Liang, Q. Wang, L. Hentilä, and J. Meinilä, "Measurements and modeling for D2D indoor wideband MIMO radio channels at 5 GHz," The Institution of Engineering and Technology, vol. 10, no. 14, p. 1839–1845, 20 September 2016.
[35] V. M. Derek Doran, "Propagation studies for mobile-to-mobile communications,” Propagation Phenomena in Real World Networks, January 1, 2015.
[36] S. M. I. A. a. A. U., "Modelling and simulation of a generalized vehicle-to-vehicle fading channel," The Institution of Engineering and Technology, vol. 7, no. 9, p. 818–827, 2013.
[37] Y. I. Yanwu Ding, "Simulation models for mobile-to-mobile channels with isotropic and nonisotropic scattering," Peer-to-Peer Networking and Applications, vol. 14, p. 507–527, 2021.
[38] Y. Xichen Liu, "Empirical study on directional millimeter-wave propagation in vehicle-to-infrastructure communications between road and roadside," Frontiers of Information Technology & Electronic Engineering, vol. 22, p. 503–516, May 2, 2021.
[39] K. Y. Changzhen Li, "Measurements and analysis of vehicular radio channels in the inland lake bridge area," The Institution of Engineering and Technology, 12 April 2019.
[40] X. Wu, C.-X. Wang, J. Sun, J. Huang, R. Feng, Y. Yang, and G. Xiaohu, "60-GHz Millimeter-Wave Channel Measurements and Modeling for Indoor Office Environments," IEEE Transactions on Antennas and Propagation, pp. 1912–1924, 15 February 2017.
[41] L. Wang, J. Chen, X. Wei, J. Cui, and B. Zheng, "First-order reflection MIMO channel model for 60 GHz NLOS indoor WLAN systems," IEEE International Conference on Communication Systems, November 19–21, 2014.
[42] Swetank Kumar Saha, "60 GHz indoor WLANs: insights into performance and power consumption," Wireless Networks, vol. 24, p. 2427–2450, March 4, 2018.
[43] S. M. B. Shaela Sharmin, "Characterization of WLAN System for 60 GHz Residential Indoor Environment Based on Statistical Channel Modeling," International Journal of Wireless and Microwave Technologies, vol. 10, no. 2, 2020.
[44] C. A. Ali Kabalan, "Millimeter-wave home area network prospect with cost-effective RoF links," Optical and Quantum Electronics, January 3, 2019.
[45] M. A. F. Junghoon Ko, "Millimeter-Wave Channel Measurements and Analysis for Statistical Spatial Channel Model in In-Building and Urban Environments at 28 GHz," IEEE Transactions on Wireless Communications, vol. 16, no. 9, pp. 5853–5868, September 2017.
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    Kola-Jnr, A., Ibikunle, F., Olamide, A. F., Unaegbu, B., Joshua, O., et al. (2024). Review of Channel Measurements and Modeling for Successful 5G System Deployments. International Journal of Wireless Communications and Mobile Computing, 11(2), 31-38. https://doi.org/10.11648/j.wcmc.20241102.12

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    Kola-Jnr A, Ibikunle F, Olamide AF, Unaegbu B, Joshua O, et al. Review of Channel Measurements and Modeling for Successful 5G System Deployments. Int J Wirel Commun Mobile Comput. 2024;11(2):31-38. doi: 10.11648/j.wcmc.20241102.12

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  • @article{10.11648/j.wcmc.20241102.12,
      author = {Akorede Kola-Jnr and Francis Ibikunle and Ariba Folashade Olamide and Bright Unaegbu and Olubiyi Joshua and Adedire Collins},
      title = {Review of Channel Measurements and Modeling for Successful 5G System Deployments
    },
      journal = {International Journal of Wireless Communications and Mobile Computing},
      volume = {11},
      number = {2},
      pages = {31-38},
      doi = {10.11648/j.wcmc.20241102.12},
      url = {https://doi.org/10.11648/j.wcmc.20241102.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wcmc.20241102.12},
      abstract = {The deployment of 5G wireless networks has enabled the investigation of numerous potential applications across a variety of sectors. To enhance the efficiency of 5G systems, it is imperative to have a thorough understanding of millimeter-wave wireless channels, different multi-access techniques, massive MIMO technologies, beamforming, modulation, and coding. Adjusting the channel modeling approach to accommodate specific characteristics of the deployment site, such as geographical obstructions like hills, tunnels, road infrastructure, and mountains, may prove to be crucial. This review paper delves into the challenges associated with channel modeling, underscoring the importance of multipath components and the diverse measurement techniques required for enhancing 5G communication. Additionally, it delves into the complexities of accurately depicting the behavior of wireless channels in various scenarios and assesses the key factors that could significantly affect the functionality of 5G networks across different environments. For instance, it becomes clear that indoor channels provide a greater impediment than outdoor channels because barriers such as walls, furniture, and human activities can impede signal transmission and interrupt communication. Indoor channels display complex characteristics that include fluctuations in the angles at which signals arrive, transmission of numerous signals over different paths, and a wide range of scattering qualities that are specific to indoor environments. Hence, it is crucial to modify the measurement procedures to correspond to the unique characteristics of indoor channels. Indoor wireless communication relies on channels available both within and outside the structure. Evaluation aspects such as, macroscopic fading, microscopic fading, and shadow fading are critical because these elements have a significant impact on the channel capacity.
    },
     year = {2024}
    }
    

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  • TY  - JOUR
    T1  - Review of Channel Measurements and Modeling for Successful 5G System Deployments
    
    AU  - Akorede Kola-Jnr
    AU  - Francis Ibikunle
    AU  - Ariba Folashade Olamide
    AU  - Bright Unaegbu
    AU  - Olubiyi Joshua
    AU  - Adedire Collins
    Y1  - 2024/09/20
    PY  - 2024
    N1  - https://doi.org/10.11648/j.wcmc.20241102.12
    DO  - 10.11648/j.wcmc.20241102.12
    T2  - International Journal of Wireless Communications and Mobile Computing
    JF  - International Journal of Wireless Communications and Mobile Computing
    JO  - International Journal of Wireless Communications and Mobile Computing
    SP  - 31
    EP  - 38
    PB  - Science Publishing Group
    SN  - 2330-1015
    UR  - https://doi.org/10.11648/j.wcmc.20241102.12
    AB  - The deployment of 5G wireless networks has enabled the investigation of numerous potential applications across a variety of sectors. To enhance the efficiency of 5G systems, it is imperative to have a thorough understanding of millimeter-wave wireless channels, different multi-access techniques, massive MIMO technologies, beamforming, modulation, and coding. Adjusting the channel modeling approach to accommodate specific characteristics of the deployment site, such as geographical obstructions like hills, tunnels, road infrastructure, and mountains, may prove to be crucial. This review paper delves into the challenges associated with channel modeling, underscoring the importance of multipath components and the diverse measurement techniques required for enhancing 5G communication. Additionally, it delves into the complexities of accurately depicting the behavior of wireless channels in various scenarios and assesses the key factors that could significantly affect the functionality of 5G networks across different environments. For instance, it becomes clear that indoor channels provide a greater impediment than outdoor channels because barriers such as walls, furniture, and human activities can impede signal transmission and interrupt communication. Indoor channels display complex characteristics that include fluctuations in the angles at which signals arrive, transmission of numerous signals over different paths, and a wide range of scattering qualities that are specific to indoor environments. Hence, it is crucial to modify the measurement procedures to correspond to the unique characteristics of indoor channels. Indoor wireless communication relies on channels available both within and outside the structure. Evaluation aspects such as, macroscopic fading, microscopic fading, and shadow fading are critical because these elements have a significant impact on the channel capacity.
    
    VL  - 11
    IS  - 2
    ER  - 

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