5G

1. Investigation and Comparison of 5G Channel
Models: From QuaDRiGa, NYUSIM,
and MG5G Perspectives
PANG Lihua1,2
, ZHANG Jin2
, ZHANG Yang2,3
, HUANG Xinyi2
, CHEN Yijian4
, and LI Jiandong2
(1. School of Communication and Information Engineering, Xi’an University of Science and Technology,
Xi’an 710054, China)
(2. State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an 710071, China)
(3. National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China)
(4. Algorithm Department, ZTE Corporation, Shenzhen 518057, China)
Abstract — This paper investigates and compares
three channel models for the fifth generation (5G) wire-
less communications: the quasi deterministic radio chan-
nel generator (QuaDRiGa), the NYUSIM channel simu-
lator model developed by New York University, and the
more general 5G (MG5G) channel model. First, the char-
acteristics of the modeling processes of the three models
are introduced from the perspective of model framework.
Then, the small-scale parameter modeling strategies of the
three models are compared from space/time/frequency
domains as well as polarization aspect. In particular, the
drifting of small-scale parameters is introduced in detail.
Finally, through the simulation results of angular power
spectrum, Doppler power spectrum density, temporal
autocorrelation function, power delay profile, frequency
correlation function, channel capacity, and eigenvalue dis-
tribution, the three models are comprehensively investig-
ated. According to the simulation results, we clearly ana-
lyze the impact of the modeling strategy on the three
channel models and give certain evaluations and sugges-
tion which lay a solid foundation for link and system-level
simulations for 5G transmission algorithms.
Key words — 5G, Channel model, Small-scale para-
meter, Modeling strategy, Channel simulation.
I. Introduction
5G wireless communications are ready to be used
on a large scale worldwide. Key technologies such as
millimeter wave communications, massive multiple in-
put multiple output (M-MIMO), and heterogeneous
networks are regarded as the main research directions
of 5G[1−3]
. These key technologies impose new require-
ments of the associated wireless channel characteriza-
tion. In order to approximate the actual link-level and
system-level simulation evaluations of the aforemen-
tioned 5G key technologies, it is necessary to establish a
standard channel model that comprehensively charac-
terizes the propagations of 5G wireless communications.
The new features that meet the requirements of 5G
channel modeling are as follows[4−9]
:
1) Wide range of scenarios: In addition to tradi-
tional scenarios (e.g., cities, suburbs, and rural areas),
high-speed trains (HST), device-to-device/vehicle-to-
vehicle communications (D2D/V2V) and other features
should also be supported for 5G application scenarios.
Each scenario has its own corresponding key enabling
technologies, which require the new 5G high-band wire-
less channel model to have a general and flexible model-
ing framework. In this way, the expected channel mod-
el can support joint characterizations of multiple scen-
arios and multiple technologies.
2) Compatibility with frequency dimensions: For 5G
communication systems, penetration, atmospheric ab-
sorption, and blocking models need to be considered be-
cause high-frequency signals are easily affected by vari-
ous factors in the propagation environment, which will
cause large propagation loss, rapid channel changes, and
low diffraction capabilities. Therefore, its characterist-
Manuscript Received Mar. 27, 2021; Accepted May 19, 2021. This work was supported in part by the National Natural Science
Foundation of China (61871300, 61701392, U19B2015), the Key Research and Development Program of Shaanxi (2021GY-050, 2019ZDLSF0
7-06), the Excellent Youth Science Foundation of Xi’an University of Science and Technology (2019YQ3-13), the Fundamental Research
Funds for the Central Universities (JB210112), and the Open Research Fund of the National Mobile Communications Research Laboratory
(2019D12).
© 2022 Chinese Institute of Electronics. DOI:10.1049/cje.2021.00.103
Chinese Journal of Electronics
Vol.31, No.1, Jan. 2022

2. ics are significantly different from conventional cellular
mobile communication channels below 6 GHz. Mean-
while, the channel characterization parameters on dif-
ferent frequency bands are obviously different, and the
channel characteristics on different frequency bands (in-
cluding traditional low frequency bands) should be
characterized by the new 5G channel model.
3) Compatibility with antenna dimensions: The M-
MIMO channel modeling also needs to shape the influ-
ence of spherical wavefront arrival and the nonstation-
ary characteristics of the array space because the large
quantities of antennas in the M-MIMO systems cause
the following phenomena. First, the distance between
the receiver and the transmitter now doesn’t exceed the
Rayleigh distance, which makes the wavefront no longer
a plane wave but a spherical wave. Therefore, far-field
propagation assumptions are no longer valid. In addi-
tion, the nonstationarity of the array space also needs
to be able to reflect the birth and death of clusters
across the array.
4) Polarization matching of antenna: The polariza-
tion mismatch between the propagation channel and
the antenna will cause a power loss of 10–20 dB, which
has a great influence on the system capacity. The radi-
ation of an antenna in all directions is different, and its
radiation power can be expressed as a function of its
angle. When the antenna rotates around a fixed point,
the received power will produce additional changes. In
the V2V scenario, the movement of the vehicle will
cause the antenna on the vehicle to rotate. Therefore,
the new 5G high-band channel model should consider
the influence of antenna polarization in different scen-
arios on the channel.
5) Compatibility with spatial-temporal dimensions:
One of the major goals of the 5G communication sys-
tems is to provide users with reliable high-rate data ac-
cess speed in high-speed mobile scenarios or densely
populated scenarios. According to the channel measure-
ment results, similar large-scale parameters (LSPs) and
small-scale parameters (SSPs) should be obtained for
two users with close intervals or two adjacent mobile
terminals. In addition, when users enter different scen-
arios, the channel coefficients should change relatively
smoothly over time. When both transmitter and receiv-
er are moving, it will affect the key characteristic para-
meters of the channel, such as Doppler shift. Therefore,
the new 5G channel model needs to be capable of char-
acterizing the changes in key parameters of the space-
time dimension of the channel when in the mobile scen-
arios or the changing scenarios.
6) Moderate complexity requirements: High-fre-
quency communications usually use a large signal band-
width, resulting in high channel delay resolution. No
matter the transmitter sides or the receiver sides will be
equipped with hundreds or even more antenna array
elements. The dual movement of the receiver and the
transmitter causes the key channel characteristic para-
meters to be updated in units of snapshots. The above
requirements inevitably increase the complexity of mod-
eling. The lower complexity channel model is condu-
cive to the theoretical analysis of 5G-related transmis-
sion algorithms and is more appropriate for system-level
and network-level simulations.
According to 5G channel measurement activities
and statistical analysis results, different research insti-
tutions have proposed some channel models, such as
3GPP TR 38.901[10]
, 3GPP-3D[11]
, QuaDRiGa[12]
,
COST 2100[13]
, mmMAGIC[14]
, 5GCMSIG[15]
,
NYUSIM[16]
, MG5G[17]
, and others[5,18,19]
. There are
overlaps in the directions in which the groups are work-
ing on 5G channel measurement and modeling, but they
also have their own emphases. The channel model pro-
posed by the 3rd generation partnership project (3GPP)
in 3GPP TR 38.901 provides the statistical character-
istics of the path loss model and the large-scale channel
parameters in multiple scenarios. At the same time, the
atmospheric absorption and obstacle blocking effects of
the millimeter wave channel are reflected in the model.
The 3GPP-3D channel model is also proposed by the 3G
PP organization. This model mainly takes the influence
of the elevation angle of the antenna array into ac-
count so the channel model can support the 3D planar
antenna array.
QuaDRiGa is a random channel model based en-
tirely on 3D geometry. It was proposed by the
Fraunhofer HHI laboratory in Germany and expanded
on the basis of the 3GPP-3D and WINNER II models.
QuaDRiGa models the SSPs of the subpath within the
cluster. It has the advantages of supporting the non-
stationary evolution of the antenna array and time di-
mension, switching between line of sight (LOS) and non
line of sight (NLOS) propagation scenarios. The COST
2100 channel model introduces the concept of the visib-
ility region (VR) to simulate the nonstationarity of the
channel. Specifically, the base station (BS) side visibil-
ity region is used to simulate the birth-death process of
clusters along the physical large-array (PLA) axis. Sim-
ultaneously, the multipath component visibility regions
and multipath component (MPC) gain function are
used to model the birth and death of a single MPC on
the user equipment (UE) side.
The key to the mmMAGIC channel model is to use
a larger mobile bandwidth to increase the channel capa-
city and data rate. It incorporates the characteristics of
the 3GPP-3D model and uses a modeling method that
combines measurement, ray tracing, and point cloud
data processing. The application scenarios include out-
door urban blocks, open squares, indoor offices, shop-
2 Chinese Journal of Electronics 2022

3. ping malls, stadiums, airport check-in halls, and out-
door-to-indoor scenarios. While the 5GCMSIG model is
based on the 3GPP-3D model, which combines mult-
iband channel measurements and ray tracing simula-
tion. The birth and death of clusters is described by
Poisson process in 5GCMSIG. When the mobile termin-
al moves to an adjacent position, the weakest cluster
gradually drops, while the new cluster gradually rises.
The team led by Professor Rappaport from New York
University has measured the channels in the 28–73 GHz
frequency band in recent years and extracted a series of
key parameters required for channel modeling. Based on
the analysis of multiband, multiscene, and multilink
type measurement data, the team supplies NYUSIM
millimeter wave channel model. The latest version of
NYUSIM supports channel space consistency and adds
a blocking model. MG5G combines the characteristics
of the WINNER and Saleh-Valenzuela channel models
and is called a more general 5G channel model. The
characteristics of this model include the arrival of the
signal’s spherical wavefront, the nonstationarity of the
scattering clusters on the antenna array and the time
axis, and the spatial consistency. In particular, the
model updates the cluster in units of snapshots. It also
supports HST, V2V, and other scenarios.
Specifically, this paper makes the following contri-
butions. First, the modeling characteristics of three
channel models and the SSPs modeling methods are
studied and compared in this paper. The motivation of
this work is that the current popular channel models
are limited in performance comparison and optimiza-
tion suggestions. This article selects QuaDRiGa,
MG5G, and NYUSIM channel models for comparative
analysis according to 5G wireless communication model-
ing requirements. The above three models belong to the
current mainstream and representative 5G channel
models. They can reflect the characteristics of 5G chan-
nel transmission. Meanwhile, all three channel models
are implemented using open-source programs, which is
convenient for users who wish to further analyze and
develop the model. In addition, the applicability of the
new 5G features of the three models are compared and
analyzed, and three models are simulated comprehens-
ively in this paper. According to the simulation results,
we clearly analyze the impact of the modeling strategy
on the three channel models and give certain evalu-
ations and suggestion which lay a solid foundation for
link- and system-level simulations for 5G transmission
algorithms. Please note that we do not recommend a
certain channel model but instead we explain how dif-
ferent channel modeling strategies affect system per-
formance results. Part of the above works has been
published in our conference paper Ref.[20] and current
journal version shows more systematic comparative case
analysis.
The remainder of this paper is organized as follows.
In Section II, the modeling frameworks of QuaDRiGa,
NYUSIM, and MG5G channel models are briefly intro-
duced. The drifting of SSPs as time evolves as well as
respective model summary are provided in Section III.
Simulation results and associated analysis are presen-
ted in Section IV. Finally, Section V summarizes the
characteristics of the three channel models and figures
out our future research direction.
II. Channel Model Framework
1. QuaDRiGa
QuaDRiGa largely extends WINNER II/WIN-
NER+ and 3GPP-3D models and is the preferred simu-
lation platform for the 5G communication system re-
commended by 3GPP standardization organization.
Specifically, QuaDRiGa is a complete geometry-based 3D
stochastic channel model and embodies many features
included in the SCM and WINNER channel models, as
well as some novel modeling approaches. These ap-
proaches, which provide features that make the mul-
tilink tracking of users accurate and qualitative in this
changing environment. Channel parameters, such as the
delay/angle spread and cross-polarization ratio (XPR),
are randomly determined, based on real-world measure-
ments extracted from statistical distributions. Different
channels are obtained by adding paths with different
channel parameters. It should be noted that different
scenarios use the same approach for modeling, but dif-
ferent parameters. QuaDRiGa supports arbitrary carri-
er frequencies from 0.45 to 100 GHz with up to 1 GHz
radio frequency (RF) bandwidth, and the modeling ap-
proach is also applicable if the parameters of other fre-
quency bands are available. Meanwhile, through the po-
sitions of the scattering clusters and users, the continu-
ous time evolution of channel parameters such as time
delay, power, angle, shadow fading, and the Ricean K-
factor are supported. Moreover, for a longer time/dis-
tance mobility, the user track is separated into mul-
tiple segments, and the length of each segment is linked
to the decorrelation distances of the LSPs. In each seg-
ment, a scenario (e.g., LOS and NLOS) is assigned, the
LSPs vary steadily, and the wide-sense stationary
(WSS) condition is satisfied. The segments are finally
combined into a continuous channel. The spatially cor-
related modeling of LSPs in the LOS and NLOS scen-
arios is determined by the sum-of-sinusoids (SOS) ap-
proach to simulate a Gaussian random process to en-
sure the spatial consistency of SSPs.
The whole process of the modeling procedures for
QuaDRiGa can be drawn as follows. First, the input
variables such as terminal trajectories and propagation
scenario are needed. Then the channel coefficients are
calculated by the following seven steps successively.
Step A calculates the correlated LSPs to ensure the
Investigation and Comparison of 5G Channel Models: From QuaDRiGa, NYUSIM, and MG5G Perspectives 3

4. consistency of the LSPs. After setting the LSPs, a series
of channel MPCs with specific initial delay, phase,
angle, and power appear at the transmitter and receiv-
er in Step B. In Step C, we can calculate accurate K-
factors, delay spreads, and angle spreads based on
power, delay, and so on. Then, the channel coefficients
can be calculated by updating the initial characteriza-
tion parameters. In Step D, we can get the accurate loc-
ation of the first-bounce scatterer (FBS) and the last-
bounce scatterer (LBS), based on the delay and angle
information obtained from the previous step. The delay
and phase are updated when the user is moving. Step E
uses the Jones matrix to model the effects of the geo-
metric polarization. Step F recalculates the path gain
(PG) and K-factor and applys the LSP to the channel
coefficient because the LSPs will change with the move-
ment of the user. Finally, QuaDRiGa combines each
segment of the user’s trajectory into a continuous chan-
nel. A smooth transition between segments is made
through the birth-death procedure of clusters. More de-
tails of QuaDRiGa modeling process can be seen in
Ref.[12].
2. MG5G
MG5G is a more general 3D geometry-based
stochastic 5G channel model developed in 2018. MG5G
is more general, indicating that it can support a vari-
ety of 5G characteristics, including the spherical wave-
front, the array-time evolution, and the high delay res-
olution of mmWave channels, and it also supports most
5G application scenarios, such as M-MIMO, HST, V2V,
and mmWave communications. Meanwhile, by chan-
ging the value of parameters of MG5G channel model
properly, the model can be simplified into a channel
model for specific purposes. It needs to be figured out
that MG5G channel model mainly focuses on the mod-
eling of SSPs, without considering the modeling of
LSPs. The current frequency and scenario parameters
supported by MG5G are derived from the COST 2100
channel model, and the channel parameters under more
frequency points need to be obtained from future chan-
nel measurements.
The modeling process of MG5G has the following
steps. Similar to QuaDRiGa, MG5G channel model also
requires the basic information of transmitter and receiv-
er as input variables. First, clusters are allocated to re-
ceiver and transmitter antenna elements, which is to
say each element has its own observable cluster set.
Second, given the initial angle information of clusters
drawn from predefined stochastic distributions, the dis-
tance vectors can be determined. Then, the associated
path delay, power, and drifting angle can be calculated.
It should be noted that in the mobile scenario, MG5G
divides the clusters in the channel into three states ac-
cording to birth-death probability: newborn, disappear-
ance, and survival. The state of each cluster can only be
one of the above. For a newly generated cluster, para-
meters are generated according to predefined distribu-
tions. While for a survived cluster, properties are gener-
ated based on the geometry. Consequently, the cluster
set that each antenna element is able to “see” is chan-
ging. Finally, MG5G model will update the parameters
according to the geometric position changes, then calcu-
lating the channel coefficients for each movement. The
details of modeling process of MG5G can be referred to
Ref.[17].
3. NYUSIM
NYUSIM supports channel carrier frequencies of
0.5 to 100 GHz and up to 800 MHz RF bandwidth. In
MIMO channel modeling, the maximum number of an-
tennas supported by the transmitter is 128, and the
number is 64 for receiver, respectively. The model is
based on the channel measurements from 28 to 73
GHz[21−25]
, supporting the modeling of urban macrocell
(UMa), rural macrocell (RMa), and urban microcell
(UMi) scenarios as well as the linear and hexagonal
single-mobility case in the channel. It is worth noting
that NYUSIM considers the atmospheric attenuation,
outdoor-to-indoor penetration, and human blockage
shadowing effects when modeling the path loss in the
channel. NYUSIM proposes the time cluster-spatial lobe
(TCSL) approach to model the spatial-temporal charac-
teristics of the channel. Specifically, the time cluster is
composed of a set of MPCs with similar propagation
delays and different angle parameters. The spatial lobe
is described as the main direction in which the signal or
energy concentrates to leave or arrive. Therefore, the
MPCs belonging to the same time cluster may belong to
different spatial lobes, and the MPCs belonging to the
same spatial lobe may also be scattered in hundreds or
thousands of nanoseconds and belong to different time
clusters. Thus, the method of the time cluster and the
space lobe can model the channel from both the time
domain and the space domain. At the same time, NY-
USIM model proposes using multiple reflection surfaces
to update the channel SSPs in the mobile scenarios. A
cluster birth-death procedure is used to smoothly con-
nect the consecutive channel segments.
The modeling process of NYUSIM can be de-
scribed as follows. First we enter the channel carrier fre-
quency, the antenna settings, and the user’s motion tra-
jectory according to the simulation requirements. The
whole model will obtain the relevant parameters of the
time cluster and space lobe, respectively. In the process
of generating time clusters, the number of subpaths be-
longing to the same time cluster will be calculated first,
and then the initial delay, power, and phase of the sub-
paths will be generated. The steps of generating spatial
lobe parameters are consistent with the time cluster.
After obtaining the number of spatial lobes, the model
will assign subpaths to different spatial lobes, and cal-
4 Chinese Journal of Electronics 2022

5. culate the subpath angles based on the spatial lobe
angles. Later, according to the geometric relationship of
the transceiver, the delay, power, angle, and phase will
be updated with the user’s movement. The transition
between segments is carried out by the birth-death pro-
cess of clusters. After completing the above modeling
steps, NYUSIM will generate channel parameters based
on the input parameters. The angle parameters in a seg-
ment are updated according to the geometric relation-
ship between different snapshots. Parameters will be re-
distribute at the beginning of different segments. More
details can be obtained from Ref.[16].
III. Small-Scale Parameters Comparsion
The previous section described the modeling frame-
work of the three models. The channel modeling meth-
ods of the three models have similarities and their own
characteristics. The three models have certain differ-
ences in the modeling characteristics of the parameters,
such as how to update parameters as space and time
change, how to distribute parameters, and how to
define clusters. The parameter modeling method affects
the characteristics of the model. In the next part, the
three models will be described from the perspective of
parametric modeling. Since the modeling of LSPs is rel-
atively mature, the modeling of SSPs will be emphas-
ized. Specifically, the drifting SSPs comparison of
QuaDRiGa, MG5G, and NYUSIM channel models has
been detailed in our conference paper Ref.[20]. Here, in
order to maintain logical completeness, we rephrase rel-
evant information in the following paragraphs.
1. QuaDRiGa channel model
The user track is divided into multiple segments in
the process of the user movement. A segment consists
of a certain number of time snapshots, and QuaDRiGa
updates the channel parameters of the mobile termin-
als with the time snapshot as the basic unit. We will in-
troduce the drifting process of QuaDRiGa in detail.
m
s p
q
As shown in Fig.1, the FBS and LBS represent the
first and last reflection of a subpath, respectively. For
the NLOS components, the -th subpath length at
snapshot between the -th transmitter antenna ele-
ment and the -th receiver antenna element is calcu-
lated as
dq,p,n,m,s = |bp,n,m,s| + |cn,m| + |aq,n,m,s| (1)
bp,n,m,s m
n s
p cn,m
aq,n,m,s q
bp,n,m,s aq,n,m,s
where represents the vector of the -th sub-
path within the -th cluster at snapshot points from
the -th transmitter antenna element to the FBS,
represents the vector from the FBS to the LBS, and
represents the vector from the -th receiver an-
tenna to the LBS. The and can be gen-
erated as
bp,n,m,s = r + an,m − ep,s − cn,m (2)
aq,n,m,s = an,m − eq,s (3)
r
an,m
ep,s
p
eq,s
q
m
where points from the initial transmitter location to
the initial receiver location, is the vector from the
initial receiver to the scatterer, represents the vec-
tor from the initial transmitter position to the -th
transmitter antenna element, and represents the
vector from the initial receiver position to the -th re-
ceiver antenna element. Then angles of the -th sub-
path can be obtained by the inverse trigonometric func-
tion of the distance vector above.
ϕa
q,n,m,s = arctan2{aq,n,m,s,y, aq,n,m,s,x} (4)
θa
q,n,m,s = arcsin
{
aq,n,m,s,z
|aq,n,m,s|
}
(5)
ϕb
p,n,m,s = arctan2{bp,n,m,s,y, bp,n,m,s,x} (6)
θb
p,n,m,s = arcsin
{
bp,n,m,s,z
|bp,n,m,s|
}
(7)
arctan2
ϕa
q,n,m,s ϕb
p,n,m,s
θa
q,n,m,s θb
p,n,m,s
aq,n,m,s,x aq,n,m,s,y
aq,n,m,s,z
aq,n,m,s x y z
bp,n,m,s,x bp,n,m,s,y bp,n,m,s,z
m
where is the operator of multi-valued inverse
tangent. and are the azimuth angle of
arrival (AOA) and the azimuth angle of departure
(AOD), respectively. and are the zenith
angle of arrival (ZOA) and the zenith angle of depar-
ture (ZOD), respectively. , , and
are the modulus of projection components of
on the -, -, and -axis, respectively. The
same goes for , , and . The
phase of the -th subpath is also calculated according
to the path length
ψq,p,n,m,s =
2π
λ
· (dq,p,n,m,s mod λ) (8)
λ n
s
where is the wavelength. Next, the -th cluster delay
at snapshot is calculated according to the subpath
lengths within the cluster
τq,p,n,s =
1
20 · c
20
∑
m=1
dq,p,n,m,s (9)
c
where is the speed of light.
TX
track
R
X
t
r
a
c
k
Initial TX location
Initial RX location
TX location at snapshot s
RX location at snapshot s
ep, s
eq, s
bn, m
bp, n, m, s
aq, n, m, s
an, m
rq, p, s
cn, m
LBS
FBS
r
Fig. 1. Multibounce model of QuaDRiGa
Investigation and Comparison of 5G Channel Models: From QuaDRiGa, NYUSIM, and MG5G Perspectives 5

6. p q
In QuaDRiGa, the single channel coefficient
between the -th transmitter antenna element and the -
th receiver antenna element include the effects of polar-
ization and antenna patterns.
h
′
q,p,n,m,s =Fq
(
Θa
q,n,m,s, Φa
q,n,m,s
)T
· Mq,p,n,m,s · Fp
(
Θd
p,n,m,s, Φd
p,n,m,s
)
(10)
Fq Fp
(Θa
, Φa
)
(
Θd
, Φd
)
q p
M
where and represent the polarimetric antenna re-
sponses on the receiver and transmitter side, respect-
ively. and involve the orientation of
the -th receiver antenna and the -th transmitter an-
tenna. is the polarization transfer matrix and incor-
porates the additional changes of the polarization
caused by scattering for the NLOS components. It is
noteworthy that the geometric polarization approach is
used in QuaDRiGa and that the propagation of signals
can be separated from the antenna effects. Channel po-
larization modeling is influenced by the angle of an-
tenna rotation and the real signal propagation scenario.
Jones matrices are taken to simulate the linear vari-
ation of LOS/NLOS path polarization[26]
. Because the
channel coefficient of the path is calculated from the
channel coefficient of the subpath in the path, the chan-
nel coefficient of the subpath with a phase update is
calculated first. The channel coefficient of the subpath
after updating can be represented as
ψ
′
q,p,n,m,s = e(−jψ0
n,m−jψq,p,n,m,s)
(11)
h
′′
q,p,n,m,s = h
′
q,p,n,m,s · ψ
′
q,p,n,m,s (12)
ψ0
n,m
n
s
where is the initialized random phase of the sub-
path. QuaDRiGa uses average power to make the power
of the path fluctuate around this value without causing
greater randomness in the path power in the channel.
So the channel coefficient of the -th cluster at snap-
shot can be written as
h
′′′
q,p,n,s =
20
∑
m=1
h
′′
q,p,n,m,s (13)
hq,p,n,s =
v
u
u
u
u
u
u
u
u
t
P
20
·
S
∑
s=1
20
∑
m=1

9. h
′′
q,p,n,m,s

12. 2
S
∑
s=1

15. h
′′′
q,p,n,s

18. 2
· h
′′′
q,p,n,s (14)
P n
S
where denotes the initial path power of the -th
cluster and denotes the snapshot number in the seg-
ment.
n
Next, QuaDRiGa adds the influence of large-scale
fading to (14) to obtain the final channel coefficient of
the -th path. This article mainly studies the similarit-
ies and differences of the three models in small-scale
parameter modeling, therefore this paper will not re-
peat them here. The above steps are used to generate
channel coefficients within each time snapshot, and
then the cluster birth-death process is used to support a
smooth transition between different scenarios. The
birth-death process of clusters occurs in the overlap-
ping portion between two adjacent segments. Specific-
ally, the overlapping part is further divided into subin-
tervals, during which the power of the clusters in the
old segments ramp down and new clusters in the next
segments ramp up. Power ramps follow a sine square
function which has a constant slope at the beginning
and the end in order to guarantee consistency between
each subinterval
w = sin2
(π
2
· w[lin]
)
(15)
w[lin]
where means the linear ramp, which is ranging
from 0 to 1.
2. MG5G channel model
∆t
In MG5G model, the scattering environment can
be considered to be the reflections of clusters, and the
clusters birth-death process on the array-time axis is
used to characterize the nonstationarity feature in MG5G.
The birth-death process of clusters in MG5G is de-
scribed by the survival probability of the old cluster
and the birth process of the new cluster. The survival
probability of a cluster during time interval is com-
puted as
PT (∆t) = e
−λR
pF (∆vR+∆vT
)∆t
Ds
c (16)
λR pF
∆vT
∆vR
Ds
c
where is the death rate of a cluster and is the
rate of mobile clusters. and are mean velocit-
ies of the transmitter and receiver, respectively. is
the space correlation distance, which depends on scen-
arios. On the other hand, the average number of new-
born clusters is determined by a Poisson process
E [Nnew (t + ∆t)] =
λG
λR
(1 − PT (∆t)) (17)
t
∆t p
In addition, MG5G uses a simple linear power scal-
ing method to control the change of cluster power dur-
ing the birth-death process. That is to say, the power of
a new cluster will linearly increase from 0 to its prede-
termined power within 1 ms and the power of a disap-
pearing cluster will decrease to 0 within 1 ms. After the
status of each cluster is determined, its time delay,
power, angle, and other parameters are calculated by
updating the distance vector. The specific evolution
process is as follows. When the user moves at time
after , the vectors of positions for the -th transmit-
6 Chinese Journal of Electronics 2022

19. q
ter antenna and -th receiver antenna are computed as
AR
q (t + ∆t) = AR
q (t) + vR
∆t (18)
AT
p (t + ∆t) = AT
p (t) + vT
∆t (19)
vT
vR
AR
q (t) AT
p (t)
q p
t
n
As is shown in Fig.2, where and are velo-
city vectors of the transmitter and receiver arrays,
and indicate the vectors of positions for
the -th receiver antenna and -th transmitter antenna
at time , respectively. Meanwhile, the distance vectors
from the receiver and the transmitter to the -th
cluster are adjusted as
DR
n (t + ∆t) = DR
n (t) + vR
n ∆t (20)
DT
n (t + ∆t) = DT
n (t) + vT
n ∆t (21)
vT
n vR
n
n
n
DT
n (t) DR
n (t) t
n t + ∆t
where and are the vectors of velocity for the first
and last bounce of the -th cluster. The vectors for the
-th cluster at the transmitter and receiver are ex-
pressed as and at time . Thus the delay
of the -th NLOS component at is calculated as
τn (t + ∆t) =
DR
n (t + ∆t)
+
DT
n (t + ∆t)
c
+ e
τn (t + ∆t) (22)
˜
τn (t + ∆t)
n
where the virtual delay between the first and
last bounces of the -th cluster follows an exponential
distribution.
Clustern
Clustern+2
Clustern+1
ZG ZG
yG
yG
Observable link Non-observable link
Fig. 2. Geometry-based model of MG5G
n
ϕA
n
The initial angular parameters of the -th cluster
are extracted by wrapped Gaussian distributions. Here,
we take the AOA as an example, and it can be com-
puted as
ϕA
n (t + ∆t) = arctan2{DR
n,y (t + ∆t) , DR
n,x (t + ∆t)}
(23)
DR
n,y (t + ∆t) DR
n,x (t + ∆t)
y
x
m
n
where and are the modulus
of projection components of the distance vector on the -
axis and -axis, respectively. In MG5G, the cluster
mean power is assumed to satisfy the inverse square law
to describe how the channel changes over time. After
mathematical derivation, the mean power of the -th
ray within the -th cluster is generated as
P′
n,m (t + ∆t) = P′
n,m (t)
3τn (t) − 2τn (t + ∆t) + τn,m
τn (t) + τn,m
(24)
τn,m m
P′
n,m
where is the relative delay of the -th ray, which
is exponentially distributed[5]
. in (24) are not nor-
malized. They can be normalized by summing over the
mean power of rays which are scaled by the power of
cluster. We can see from (24) that the update of the
subpath power is related to the delay. Rather than
obeying the exponential distribution of the delay, it has
a linear relationship with the power at the previous mo-
ment. Finally, the complex channel gain consisting of
the LOS/NLOS components is calculated as follows:
• n
q p
When and only when the -th path is available
to the -th receiver element and -th transmitter ele-
ment,
hq,p (t, τ)
=
√
K
K + 1
hLOS
q,p (t)δ
(
τ − τLOS
(t)
)
| {z }
LOS
+
√
1
K + 1
N(t)
∑
n=1
M(t)
∑
m=1
hq,p,n,m (t)δ
(
τ − τNLOS
(t)
)
| {z }
NLOS
(25)
• Otherwise
hq,p (t, τ) = 0 (26)
hLOS
q,p (t) =
[
FT
p,V (DLOS
q,p (t), AT
p (t))
FT
p,H(DLOS
q,p (t), AT
p (t))
]T [
ejΦLOS
0
0 −ejΦLOS
] [
FR
q,V (DLOS
q,p (t), AR
q (t))
FR
q,H(DLOS
q,p (t), AR
q (t))
]
ej2πfLOS
q,p (t)t
(27)
hq,p,n,m(t) =
[
FT
p,V (DT
n,m(t), AT
p (t))
FT
p,H(DT
n,m(t), AT
p (t))
]T [
ejΦV V
n,m
√
κejΦV H
n,m
√
κejΦHV
n,m ejΦHH
n,m
] [
FR
q,V (DR
n,m(t), AR
q (t))
FR
q,H(DR
n,m(t), AR
q (t))
]
×
√
Pn,m(t)ej2πfR
q,n,m(t)t
ej2πfT
p,n,m(t)t
(28)
Investigation and Comparison of 5G Channel Models: From QuaDRiGa, NYUSIM, and MG5G Perspectives 7

20. K N(t)
M(t)
τLOS
(t)
τNLOS
(t)
hLOS
q,p (t) hq,p,n,m (t)
ΦLOS
(0, 2π] ΦV V
n,m ΦV H
n,m ΦHV
n,m ΦHH
n,m
(0, 2π] V
H
FT
FR
fLOS
q,p (t)
fR
q,n,m(t) fT
p,n,m(t)
κ
Pn,m(t) m
n
2 × 2
where indicates the Ricean factor, is the num-
ber of clusters, is the number of subpaths within
the clusters, is the delay of LOS component,
and is the delay of NLOS component. Specific-
ally, and are shown in (27) and (28),
repectively. obeys the uniform distribution in
. The , , , and represent the
polarization components, which obey the uniform distri-
bution in . Among them, represents the vertic-
al polarization and represents the horizontal polariz-
ation. Functions and are antenna patterns. The
is the Doppler frequency between the transmit-
ter and the receiver of the LOS component. For the
NLOS components, Doppler frequencies at the receiver
and transmitter are expressed as and .
The variable is the cross polarization power ratio, and
is the mean power of the -th ray within
cluster after normalized. As can be seen in the equa-
tions, MG5G uses a polarization coupling matrix
with random coefficients is used to describe the changes
of polarization from the transmitter to the receiver.
However, the elliptical and circular polarization are not
covered.
3. NYUSIM channel model
NYUSIM model defines that the user trajectory is
divided into several segments according to the relevant
distance, and a segment is composed of multiple snap-
shots. The AOA of the LOS path at snapshot is up-
dated as
ϕAOA (t + ∆t) = ϕAOA (t) + SAOA · ∆t (29)
SAOA ∆t
where is the linear changing rate of AOA and
is the update time, respectively. The calculation pro-
cesses of AOD, ZOA, and ZOD are the same as AOA.
The expression for linear changing rates are obtained by
SAOA =
vy cos (ϕAOA) − vx sin (ϕAOA)
r sin (θZOA)
(30)
SAOD =
vy cos (ϕAOD) − vx sin (ϕAOD)
r sin (θZOD)
(31)
SZOA =
vx cos (ϕAOA) cos (θZOA)
r
+
vy cos (θZOA) sin (ϕAOA) − vz sin (θZOA)
r
(32)
SZOD =
vx cos (ϕAOD) cos (θZOD)
r
+
vy cos (θZOD) sin (ϕAOD) − vz sin (θZOD)
r
(33)
ϕAOA ϕAOD θZOA θZOD
t r
where , , , and are the angles of the
LOS path at the last time instance and is the separ-
ation distance from the transmitter to the receiver.
vx vy vz
v x y
z
UT
′
UT
′′
ϕr
v ϕr
′
v
ϕRS1 ϕRS2
x
Moreover, , , and are the value of projections of
the velocity vector of the user terminal in the -, -,
-axis, respectively. The NLOS component is trans-
formed into a virtual LOS component by means of the
relationship between the actual receiver location and its
mirror image. Then the angles of NLOS components
can be updated using the update equations for the LOS
component. As illustrated in Fig.3, an NLOS MPC is
reflected twice before it reaches the user terminal (UT).
and are the mirror images of the user posi-
tion and and are the mirror images of the user
velocity direction. and are the angles of the
first and second reflection surfaces with respect to the
axis.
y
x
vr′
vr
v
BS
ϕAOD
ϕAOA
ϕRS2
ϕRS1
ϕr
v
′
ϕr
v
ϕv
UT
UT″
UT′
Reflection
surface 2
Reflection
surface1
Fig. 3. Multibounce model of NYUSIM
ϕAOA ϕr
v
According to the geometric relationship in Fig.3,
and can be given by
ϕAOA = 2ϕRS2 − 2ϕRS1 + ϕAOD (34)
ϕr
v = 2ϕRS2 − 2ϕRS1 + ϕv (35)
Further, the angle relations in the M reflections
case can be derived by iteration as follows
ϕAOA = BM
ϕAOD + 2
M
∑
i=1
(−1)
i
ϕRSi (36)
ϕr
v = BM
ϕv + 2
M
∑
i=1
(−1)
i
ϕRSi (37)
ϕRSi i
x B
50% 50%
where is the angle of the -th reflection surface
with respect to the -axis. A random binary number
can be 1 (even reflections) or –1 (odd reflections) with
and probability. It indicates that the probabil-
ities that an NLOS MPC experiences an odd or an even
number of reflection surfaces are assumed to be equal.
The delay of each NLOS component is then calculated
according to the path length and can be represented as
τ (t + ∆t) = τ (t) + ∆l/c (38)
8 Chinese Journal of Electronics 2022

21. ∆l
where is the change of the path length. The update
of the phase of each multipath is also obtained by the
change of the path length and is calculated as
ψ (t + ∆t) = ψ(t) + 2π∆l/λ (39)
n m
The power of the -th cluster and the -th MPC
are generated as
P′
n = P0e− τn
Γ 10
Zn
10 (40)
P′
n,m = Pn,me−
ρn,m
γ 10
Un,m
10 (41)
P0
Γ
Zn
Pn,m
ρn,m γ
Un,m
where is the mean power of the cluster which is ar-
riving first, represents the cluster decay time con-
stant, and is a log-normal distributed random vari-
able. Moreover, is the mean power of the first ar-
riving MPC, is the subpath excess delay, repres-
ents the subpath delay time constant, and is a
log-normal random variable.
p
q
f
The MIMO channel coefficient between the -th
transmitter antenna and -th receiver antenna for the
sub-carrier is generated as
hq,p (f) =
∑
m
αq,p,mejψq,p,m
e−j2πfτq,p,m
× e−j2πdT p sin(ϕAOA)
e−j2πdRq sin(ϕAOD)
(42)
m m αq,p,m
ψq,p,m
τq,p,m dT dR
where represents the -th MPC, is the amp-
litude of the channel gain, is the phase of the
MPC, is the time delay, and and are the
antenna element spacings at the transmitter and receiv-
er, respectively. Eq.(42) is adapted from Eq.(2) in
Ref.[27]. Additionally, NYUSIM uses the power loss to
model the effects of the co-polarization and cross-polar-
ization propagation of paths. Specifically, the cross-po-
larization discrimination (XPD) varies with its fre-
quency and environment, and its value range is 5–27
dB[28]
. According to the measurement results, due to
polarization mismatch, cross-polarization will increase
the path loss by an additional 25 dB, while the co-po-
larization does not have this loss. The birth-death pro-
cedure of a cluster is used to connect the consecutive
channel segments smoothly. One weakest cluster in the
new segment is defined to replace the weakest cluster in
the old segment at a snapshot.
4. Summary
For a more intuitive and clear understanding,
QuaDRiGa, MG5G, and NYUSIM channel models are
summarized and compared according to different chan-
nel modeling aspects in Table 1. Table 1 shows that
QuaDRiGa and MG5G are lacking in the modeling of
obstruction and atmospheric environment. In addition,
MG5G still lacks the transition between different
scenes, and NYUSIM lacks in dual mobility features.
Moreover, neither QuaDRiGa nor NYUSIM considers
the movement characteristics of clusters. The initial and
updating approaches of the SSPs are listed in Table 2.
In Table 2, it’s noteworthy that the initial angle of NY-
USIM is related to the lobe angle. The azimuth angle of
the lobe obeys a uniform distribution, and the zenith
angle obeys a normal distribution. In addition, QuaD-
RiGa assumes that the subpaths cannot be resolved in
the delay domain, but can be resolved in the angle do-
main. Table 3 summarizes the distribution of the num-
ber of clusters and subpaths in the above three channel
models. It is noteworthy that the number of clusters of
QuaDRiGa and MG5G is greater than that of NY-
Table 1. Modeling summary of QuaDRiGa, MG5G, and NYUSIM channel models
Characteristics QuaDRiGa MG5G NYUSIM
Frequency range 0.45−100 GHz Not mentioned 6−100 GHz
Propagation scenario Indoor/Outdoor/Satellite Indoor/Outdoor Indoor/Outdoor
Modeling method GBSM GBSM GBSM and SSCM combined
3D modeling at both sides Yes Yes Yes
M-MIMO modeling Yes Yes Limited
Antenna pattern Omnidirectional/Directional Omnidirectional/Directional Omnidirectional
Polarization modeling Linear/Elliptical/Circular Linear Linear
High-resolution subpath Angle domain only Yes Yes
Spherical wavefront modeling Yes Yes Yes
Array nonstationary modeling Yes Yes Yes
Scattering environment modeling FBS-LBS FBS-LBS Random distribution of clusters
Time nonstationary modeling Yes Yes Yes
Spatial consistency modeling Yes Yes Yes
Transition between different scenes Yes No Yes
Blocking modeling No No Yes
Atmospheric absorption No No Yes
Single-mobility Yes Yes Yes
V2V Yes Yes No
Cluster mobility No Yes No
Investigation and Comparison of 5G Channel Models: From QuaDRiGa, NYUSIM, and MG5G Perspectives 9

22. USIM. Meanwhile, the number of subpaths within each
cluster have fixed values in QuaDRiGa. NYUSIM uses
a modeling method that combines the geometrically
based stochastic model (GBSM) and the statistical spa-
tial channel model (SSCM), which is implemented by
the TCSL approach. It should be concerned that the
cluster definitions in QuaDRiGa, MG5G, and NYUSIM
channel models are different from each other. A more
detailed introduction is as follows:
1) Clustering definition in QuaDRiGa: Clusters
represent areas where scattered events occur simultan-
eously. Meanwhile, all subpaths within a cluster have
the same path length and the same delay, but different
arrival angles.
2) Clustering definition in MG5G: Clusters are
made up of MPCs that are close in time. But these
MPCs have different angles and power. Moreover, the
clusters can have fixed velocity during the movement of
the transmitter and receiver.
3) Clustering definition in NYUSIM: The concept
of time cluster and spatial lobe proposed by NYUSIM is
describing multipath behavior in omnidirectional chan-
nel impulse responses (CIRs). The multipaths in a time
cluster have similar delay but may have a great differ-
ence in angle. In the same case, a spatial lobe contains
multipaths with similar angle but may have different
delay. The TCSL modeling approach can achieve the ef-
fect of decoupling time and space.
Table 2. Summary of SSPs modeling for QuaDRiGa, MG5G, and NYUSIM channel models
Parameter QuaDRiGa MG5G NYUSIM
Initial delay Exponential distribution Exponential distribution Exponential distribution (sorted)
Initial angle Complementary error function Wrapped gaussian distribution Related to the spatial lobe angle
Initial power Exponential distribution Exponential distribution Exponential distribution
Initial phase (−π, π)
Randomly distributed in (−π, π)
Randomly distributed in (0, 2π)
Uniformly distributed in
Evolution of delay Geometric relationship
Depends on the distance vector
and exponential distribution
Geometric relationship
Evolution of angles Geometric relationship Geometric relationship Reflection surfaces
Evolution of power Geometric relationship Depends on the delay Exponential distribution
Evolution of phase Geometric relationship Geometric relationship Geometric relationship
Table 3. The number distribution of clusters and subpaths in QuaDRiGa, MG5G,
and NYUSIM channel models in UMa scenario
Parameter name LOS NLOS
NYUSIM
Number of time clusters Discrete uniform [1,6]
Number of subpaths per time cluster Discrete uniform [1,29]
Number of spatial lobes (departure) min{5,max{1,Poisson(1.9)}} min{5,max{1,Poisson(1.6)}}
Number of spatial lobes (arrival) min{5,max{1,Poisson(1.8)}} min{5,max{1,Poisson(1.6)}}
QuaDRiGa
Number of clusters 6–25
Number of subpaths per cluster 20
MG5G
Number of clusters 8 20
Number of subpaths per cluster max{Poisson(15),1}
IV. Results and Analysis
fc = 28
Nt
Nr
D = 200
vR = 20
Unless otherwise mentioned, the following simula-
tions are all performed with the parameters of NLOS
scenario, where GHz and both the transmitter
antenna and the receiver antenna are omnidirectional
antennas and have vertical polarization. In addition,
the numbers of transmitter antennas and receiver
antennas are set to be 1. Moreover, the initial dis-
tance from the transmitter to the receiver m,
the speed of the receiver m/s, and the correla-
tion distance is 45 m, respectively. The special paramet-
ers and the changed parameters will be explained in the
corresponding simulation.
1. Space domain
Figs.4–6 show the Angular power spectrum (APS)
Nt = 1 Nr = 32
for QuaDRiGa, MG5G, and NYUSIM channel models.
In the simulations, we have and . The
user starts a linear movement away from the transmit-
ter toward the east at 200 m from the transmitter and
the movement distance is 210 m. We employ a sliding
window with 3 antennas over the entire array and the
channels within this specified range can be deemed as
WSS according to the channel correlation analysis.
From this perspective, we can get 30 windows from 32
antennas. It can be clearly seen from these three fig-
ures that the AOAs of the clusters vary on each of the
antenna elements, and we can see that the clusters have
a birth-death process along the antenna axis due to
spherical wave modeling characteristics. The simula-
tion results of the three models are somewhat different.
The main reason is the different calculation methods of
10 Chinese Journal of Electronics 2022

23. the angle parameters. The distributions of angles in
NYUSIM are affected by the spatial lobes, and the path
angles will be concentrated near the angles of the spa-
tial lobes. The angle of QuaDRiGa obeys the comple-
mentary error function, and the angle distribution of
MG5G obeys the Gaussian distribution. Therefore, the
angle distributions of these two models are not as con-
centrated as NYUSIM. At the same time, the underly-
ing geometry of these models also limits the values of
AOAs, which is also the reason for the difference in
angle simulation.
2. Temporal domain
In Fig.7, the Doppler power spectrum densities
(DPSDs) of QuaDRiGa, MG5G, and NYUSIM chan-
nels at two different time instants in UMa scenario are
illustrated. First, we can see from the figure that the
movement of users has caused DPSDs to drift over
time. As we know, the Doppler shift is mainly related
to the carrier frequency, the arrival angle, the velocity
of the receiver, and its moving direction. In this simula-
tion, all the conditions are the same except the arrival
angle distribution of the path. As shown in Fig.7, NY-
USIM has multiple peaks. The main reason can be in-
terpreted as the approach of time clusters and spatial
lobes makes the time domain and spatial domain char-
acteristics of channels decoupled from each other. The
cluster is affected by multiple lobes at the same time.
Specifically, the multipath from the same time cluster
may come from different spatial angle lobes. Similarly,
the multipath from the same spatial lobes may come
from different time clusters. The channel coefficients of
the cluster are calculated from the subpaths in the
cluster, so the Doppler shift of NYUSIM is mainly af-
fected by its TCSL modeling method.
−2000−1500−1000 −500 0 500 1000 1500 2000
Doppler frequency (Hz)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Normalized
doppler
PSD
MG5G t=0 s
MG5G t=1 s
NYUSIM t=0 s
NYUSIM t=1 s
QuaDRiGa t=0 s
QuaDRiGa t=1 s
Fig. 7. The DPSDs of the QuaDRiGa, MG5G, and NY-
USIM channel models in UMa scenario
Fig.8 shows that the movement of users causes
autocorrelation functions (ACFs) to drift over time.
Moreover, from the comparison of the three channel
models, we can see that the ACF of NYUSIM is signi-
ficantly higher than that of QuaDRiGa and MG5G.
The ACF of NYUSIM is stable between 0.7 and 0.8
when the time difference exceeds 0.1 s, while QuaD-
RiGa and MG5G are stable between 0 and 0.1. This is
caused by the reflection surfaces as well as its associ-
5 10 15 20 25 30
Window position
0
20
40
60
80
100
120
140
160
180
AOA
(°)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Nt = 1 Nr = 32
Fig. 4. APS snapshot for the user trajectory in QuaDRiGa
( , )
Window position
0
20
40
60
80
100
120
140
160
180
AOA
(°)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
5 10 15 20 25 30
Nt = 1 Nr = 32
Fig. 5. APS snapshot for the user trajectory in MG5G
( , )
0
20
40
60
80
100
120
140
160
180
AOA
(°)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
5 10 15 20 25 30
Window position
Nt = 1 Nr = 32
Fig. 6. APS snapshot for the user trajectory in NYUSIM
( , )
Investigation and Comparison of 5G Channel Models: From QuaDRiGa, NYUSIM, and MG5G Perspectives 11

24. ated angle calculation approach, which leads to the nar-
rowest angle spread of NYUSIM and contributes to its
largest autocorrelation value.
Time difference, Δt (s)
0
0 0.1 0.2 0.3 0.4 0.5
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Normalized
absolute
time
ACF
MG5G Channel model, t=5 s
MG5G Channel model, t=10 s
NYUSIM Channel model, t=5 s
NYUSIM Channel model, t=10 s
QuaDRiGa Channel model, t=5 s
QuaDRiGa Channel model, t=10 s
Fig. 8. The ACF of the QuaDRiGa, MG5G, and NYUSIM
channel models in UMa scenario
Fig.9 compares how the parameters of the sub-
paths drift with the users’ movement. In Figs.9(a) and
9(b), the subpath arrival angles, azimuth spread of ar-
rival (ASA), and zenith angle spread of arrival (ZSA)
are depicted. NYUSIM shows the lower AOA and ZOA
angle spreads, while QuaDRiGa has relatively high
angle spreads. When the user moves into the next new
segment, the angles in NYUSIM and QuaDRiGa chan-
nel models present a certain degree of inconsistency due
to the change in the scattering environment. In Figs.9(c)
and 9(d), similarly, inconsistency occurs between seg-
ments. The subpath delay of NYUSIM changes
smoothly within a segment, but there are more obvious
jumps between segments, compared with QuaDRiGa.
Furthermore, the power of NYUSIM subpath changes
much more drastically because the calculation method
of NYUSIM subpath power correlates with the excess
delay of the subpath, whereas QuaDRiGa adopts the
average power method so that the power of the sub-
path does not appear random. From Fig.9, we can also
see that there is no jump for MG5G because it doesn’t
have the concept of segment while the birth-death pro-
cess of the subpath of MG5G can be seen.
Distance from start point (m)
−200
−150
−100
−50
0
50
100
150
0 20 40 60 80 100
0 20 40 60 80 100 0 20 40 60 80 100
0 20 40 60 80 100
ASA of MG5G
Subpath angles of MG5G
Subpath angles of MG5G
ASA of NYUSIM
Subpath angles of NYUSIM
Subpath angles of NYUSIM
ASA of QuaDRiGa
Subpath angles of QuaDRiGa
Subpath angles of QuaDRiGa
Distance from start point (m)
−30
−20
−10
0
10
20
30
40
ZSA of MG5G
Subpath angle of MG5G
Subpath angle of MG5G
ZSA of NYUSIM
Subpath angle of NYUSIM
Subpath angle of NYUSIM
ZSA of QuaDRiGa
Subpath angle of QuaDRiGa
Subpath angle of QuaDRiGa
(a) Drifting subpath AOAs in UMa scenario (b) Drifting subpath ZOAs in UMa scenario
Distance from start point (m)
0
100
200
300
400
500
600
700
Subpath delay of MG5G
Subpath delay of MG5G
Subpath delay of NYUSIM
Subpath delay of NYUSIM
Subpath delay of QuaDRiGa
Subpath delay of QuaDRiGa
Distance from start point (m)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Subpath power of MG5G
Subpath power of MG5G
Subpath power of NYUSIM
Subpath power of NYUSIM
Subpath power of QuaDRiGa
Subpath power of QuaDRiGa
(c) Drifting subpath delays in UMa scenario (d) Drifting subpath power in UMa scenario
AOA
(°)
ZOA
(°)
Delay
(ns)
Normalized
power
Fig. 9. Drifting subpath SSPs of QuaDRiGa, MG5G, and NYUSIM channel models in UMa scenario
12 Chinese Journal of Electronics 2022

25.
3. Frequency domain
The absolute values of the frequency correlation
functions (FCFs) of QuaDRiGa, MG5G, and NYUSIM
channel models are shown in Fig.10. As can be seen
from the figure that the FCF fluctuates and goes down
as the frequency interval increases. Coherence band-
width is the frequency interval when the FCF is 0.5, so
the coherence bandwidth is 2.3 MHz for QuaDRiGa, 1.2
MHz for MG5G, and 1.5 MHz for NYUSIM, approxim-
ately. This can be attributed to the fact that although
the multipath delay of three models is updated based
on geometric relationships, the multipath delay in
QuaDRiGa does not include the excess delay value
while the other two models include it. Given that the
coherence bandwidth and maximum multipath delay
are approximately in a reciprocal relationship, QuaD-
RiGa exhibits a relatively large coherence bandwidth.
0 5 10 15 20
Frequency seperation, Δf (MHz)
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
MG5G Channel model, f=28 GHz
NYUSIM Channel model, f=28 GHz
QuaDRiGa Channel model, f=28 GHz
The
FCF
Fig. 10. The FCFs of QuaDRiGa, MG5G and NYUSIM
channel models in UMa scenario
The Power delay profiles (PDPs) of the three chan-
nel models are shown in the Figs.11–13. The initial dis-
tance from the transmitter to the receiver is 100 m in
the simulation here. During the movement of the mo-
bile terminal in NYUSIM and QuaDRiGa, the user has
experienced the change of the scenario from the LOS to
the NLOS and then back to the LOS scenario. MG5G
only experiences the LOS scenario in the simulation be-
cause MG5G does not support scene-changing during
the user trajectory. It can be seen that the number of
clusters of QuaDRiGa and MG5G is greater than that
of NYUSIM. As illustrated, the delay and power of the
clusters drift with distance, the LOS component has
more power, and the birth-death process occurs along
the user trajectory. In addition, QuaDRiGa has more
paths than NYUSIM in the NLOS scenario. Meanwhile,
smooth transition between LOS and NLOS can be ob-
served in both QuaDRiGa and NYUSIM. However, the
birth-death process of clusters in MG5G channel model
is more dramatic than that of QuaDRiGa and NY-
USIM. The main reason for the obvious changes in the
birth and death of clusters in MG5G lies in the fact
that the model assigns a corresponding birth-death
1
46
91
0 0.32 0.64 0.96 1.28 1.60
Delay (μs)
1.92 2.24
5
0
−5
−10
−15
−20
−25
−30
−35
Distance
(m)
Fig. 11. PDP for the user from LOS to NLOS and then
back to LOS trajectory in QuaDRiGa
1
46
91
0 0.32 0.64 0.96 1.28 1.60
Delay (μs)
1.92 2.24
5
0
−5
−10
−15
−20
−25
−30
−35
Distance
(m)
Fig. 12. PDP for the user from LOS to NLOS and then
back to LOS trajectory in NYUSIM
1
46
91
0 0.32 0.64 0.96 1.28 1.60
Delay (μs)
1.92 2.24
5
0
−5
−10
−15
−20
−25
−30
−35
Distance
(m)
Fig. 13. PDP for the user trajectory of LOS scenario in MG5G
Investigation and Comparison of 5G Channel Models: From QuaDRiGa, NYUSIM, and MG5G Perspectives 13

26. probability to each cluster. Moreover, the birth-death
process of the cluster occurs in every snapshot in MG5G
channel model, whereas the birth-death process only oc-
curs between segments in the other two models.
4. Polarization
Fig.14 describes the influence of polarization on the
path power of QuaDRiGa channel model during the
movement. In particular, Fig.14(a) shows the traject-
ory diagram of the transmitter and receiver. In addi-
tion, the motion and rotation pattern of the dipole are
also shown in Fig.14(a). Meanwhile, Fig.14(b) illus-
trates the LOS power in the movement.
Position on circle (°)
−0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
−20
Dipole antenna
−20 LOS
power
(linear
scale)
−10 0 10 20 0 45 90 135 180 225 270 315 360
x-coord (m)
y-coord
(m)
−15
−10
−5
0
5
10
15
20
(a) The trajectory of transmitter and receiver (b) The power of LOS component
Tx-position
Rx-position
Rx-track
Rotation pattern
Motion of dipole
Fig. 14. The influence of polarization on the path power of QuaDRiGa
At this time, the heights of transmitter antenna
and receiver antenna are 1.5 m and both dipoles are ro-
tated to 45 degrees. Therefore, there exists an inclina-
tion angle as shown in Fig.14(a). The receiver makes a
circular movement with a radius of 20 m from the east,
and the transmitter is at the center of the circle and
stays fixed. The blue line in Fig.14(b) indicates the
power of LOS component. We can see from the figure
that when the receiver moves to 45 degrees, the two di-
poles cross in space. This situation causes the received
power to drop to 0. It is not until 135 degrees that the
received power gradually increases. When the receiver
moves to 270 degrees, the angles of two dipoles are
aligned. Therefore, the received power reaches the max-
imum. The XPR allows part of the power of the vertic-
al polarization to be absorbed into the horizontal polar-
ization so that the received power changes as the di-
pole rotates. As for both MG5G and NYUSIM channel
models, the influence of polarization during movement
can not be precisely described.
5. Comprehensive evaluation
Nt Nr
20
Fig.15 depicts the time complexity of the three
channel models in UMa scenario. Omnidirectional an-
tennas are used for both receiver and transmitter anten-
nas. and are set to be 8. The receiver in the
single-mobility scenario moves linearly along the east
side of the transmitter. In the dual-mobility scenario,
the transmitter and receiver simultaneously move at
m/s toward each other. We recorded the time of the
three model programs running 100 times as the time
complexity results. When the transmitter or the receiv-
er is moving, the complexity increases significantly com-
pared with the stationary state, the most when it is in
the dual-mobility case. This is caused by the time evol-
ution process that calculates geometric relationships
and updates parameters. Additionally, one major obser-
vation obtained from the simulation results is that the
complexity of MG5G is much greater than that of the
other two channel models, especially in the case of dual
mobility. This lies in the calculation of the birth and
death process for each cluster on each snapshot in MG5G,
including the allocation of parameters for the newly
Stationary Single-mobility Dual-mobility
0
0.5
1.0
1.5
2.0
2.5
Seconds
3.0
3.5
4.0
×104
NYUSIM
QuaDRiGa
MG5G
Nt = 8
Nr = 8
Fig. 15. Time complexity of QuaDRiGa, MG5G, and NY-
USIM channel models in UMa scenario ( ,
)
14 Chinese Journal of Electronics 2022

27. born cluster and its belonging visible cluster sets of the
antenna. At the same time, it can be seen from the fig-
ure that QuaDRiGa maintains a good time complexity
characteristic regardless of whether it is stationary or
mobile.
Fig.16 shows the comparison of channel capacity
and cumulative distribution functions (CDFs) of the ei-
genvalues in QuaDRiGa, MG5G, and NYUSIM, re-
spectively. Currently, the base station side and user
equipemnt side are equipped with 8 omnidirectional an-
tennas. The receiver moves linearly along the east direc-
tion of the transmitter. The simulation is based on the
snapshots of channel coefficients on the user’s move-
ment track. Obviously, the channel capacity of QuaD-
RiGa is in general large in the high signal-to-noise ra-
λ1
λ4 λ8
tio (SNR) cases compared to MG5G and NYUSIM,
both in UMa and RMa scenarios. On the contrary, the
channel capacity of MG5G is relatively high in the low
SNR cases. Meanwhile, it can be observed from Figs.16(b)
and 16(d) that in MG5G is the largest among the
three models, while and are generally small. The
eigenvalues of QuaDRiGa are large and their distribu-
tion range is concentrated. This verifies why the chan-
nel capacity provided by QuaDRiGa is relatively large.
Fig.16(d) illustrates that NYUSIM has a relatively large
eigenvalue while the other two eigenvalues are very
small, which indicates the sparse feature of NYUSIM.
This is probably caused by the small number of clusters
and narrow angular spreads by NYUSIM in RMa scen-
ario.
SNR (dB)
0
10
20
30
40
50
60
70
80
90
100
−10 0 10 20 30 40
SNR (dB)
−10 0 10 20 30 40
−400 −300 −200 −100 0 100
−400 −300 −200 −100 0 100
200 300 400
NT=8, NR=8 MG5G
NT=8, NR=8 NYUSIM
NT=8, NR=8 QuaDRiGa
NT=8, NR=8 MG5G
NT=8, NR=8 NYUSIM
NT=8, NR=8 QuaDRiGa
Eigenvalue magnitude (dBm)
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
CDF
MG5G λ1
MG5G λ4
MG5G λ8
NYUSIM λ1
NYUSIM λ4
NYUSIM λ8
QuaDRiGa λ1
QuaDRiGa λ4
QuaDRiGa λ8
MG5G λ1
MG5G λ4
MG5G λ8
NYUSIM λ1
NYUSIM λ4
NYUSIM λ8
QuaDRiGa λ1
QuaDRiGa λ4
QuaDRiGa λ8
(a) Channel capacity in UMa scenario (b) CDFs of the eigenvalues in UMa scenario
0
10
20
30
40
50
60
70
80
90
100
Eigenvalue magnitude (dBm)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
CDF
(c) Channel capacity in RMa scenario (d) CDFs of the eigenvalues in RMa scenario
Channel
capacity
(bps/Hz)
Channel
capacity
(bps/Hz)
Nt = 8 Nr = 8
Fig. 16. Channel capacity and the CDFs of eigenvalues of QuaDRiGa, MG5G, and NYUSIM channel models in UMa and RMa
scenarios, respectively ( , )
V. Conclusions
This paper investigates and compares three 5G
channel models, i.e., QuaDRiGa, NYUSIM, and MG5G
from the perspectives of modeling methodologies, para-
meter settings, and channel simulations. The three
models all apply geometry-based approach to model the
time evolution feature of wireless channels, but they
take different routes to update clusters and subpath
parameters as time evolves. These cause the three chan-
Investigation and Comparison of 5G Channel Models: From QuaDRiGa, NYUSIM, and MG5G Perspectives 15

28. nel models having different performance, e.g., in terms
of time/array nonstationarity and spatial consistency
characteristics. Specifically, TCSL modeling approach
seems more realistic than the other two models.
However, its angle spreads are quite small and the res-
ulting channel is sparse. Due to these reasons, NY-
USIM channel model is probably more suitable in RMa
scenario. On the other hand, QuaDRiGa channel model
can accurately characterize most of the wireless
propagation effects in UMa scenario and at the same
time maintains low implementation complexity. As for
MG5G, it uses the full birth-death process to model
channel nonstationary characteristics. As a result, the
speed of channel update is faster and the complexity be-
comes higher. Therefore, it is more suitable for the sim-
ulation of rapidly changing channel scenario. In the
near future, we will perform actual channel measure-
ment experiments, in order to use practical channel
measurement data to further verify the validity of these
channel models. Additionally, we plan to investigate
more mainstream 5G channel models for comparison.
6G has further expanded its frequency bands, ap-
plication scenarios, and technical requirements on the
basis of 5G. Therefore, 6G wireless communications
have different types of channels, including terahertz,
optical bands, satellites, unmanned aerial vehicles,
oceans, underwater acoustics, high-speed rail, and large-
scale/super-large-scale antennas[29,30]
. As can be seen,
the above three models should better adapt to the new
propagation characteristics associated with these scen-
arios. Specifically, the new channel modeling needs to
be able to handle high mobility, multiple mobilities, the
uncertainty of motion trajectory, the non-stationary
nature of time/frequency/space domains, and so on and
so forth[31]
. In addition, the evolved channel models
should be capable of characterizing the channels of 6G
new technologies such as holographic radio, intelligent
reflective surface, and artificial intelligence communica-
tion[32]
. It is worthwhile to mention that the channel
characteristics for each individual channel show great
differences in 6G wireless communications. Therefore,
how to incorporate these distinct channel characterist-
ics into one general channel modeling framework de-
serves further investigations.
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[32]
PANG Lihua received the B.E.,
M.S. and Ph.D. degrees from Xidian Uni-
versity, Xi’an, China, in 2006, 2009, and
2013, respectively, all in electrical engin-
eering. She is currently an Associate Pro-
fessor with the School of Communication
and Information Engineering, Xi’an Uni-
versity of Science and Technology, Xi’an,
China. Her research interests include sig-
nal processing for wireless communications, stochastic network
optimization, and network performance analysis.
(Email: lhpang.xidian@gmail.com)
ZHANG Jin received the B.E.
degree in information security from Xidi-
an University, Xi’an, China, in 2018. She
is currently working toward the M.S. de-
gree with the School of Telecommunica-
tions Engineering, Xidian University, Xi’a
n, China. Her research interests include
channel measurement and modeling for
millimeter-wave wireless communications.
(Email: 844194003@qq.com)
ZHANG Yang (corresponding
author) received the Ph.D. degree in elec-
trical engineering from Xidian University,
Xi’an, China, in 2011. During 2009
to 2010, he was a Visiting Scholar with
the Department of Electrical and Com-
puter Engineering, University of Califor-
nia, Davis, CA, USA. After working as a
Research Engineer at Huawei Technolo-
gies, he rejoined Xidian University in 2013 and is currently an As-
sociate Professor. His main area of research includes wireless
channel measurement and modeling, signal processing for massive
MIMO systems, green communications, and resource allocation
strategies. (Email: yangzhang1984@gmail.com)
HUANG Xinyi received the
B.E. degree in communications engineer-
ing from Xidian University, Xi’an, China,
in 2019. She is currently working toward
the M.S. degree with the School of Tele-
communications Engineering, Xidian Uni-
versity. Her current research interest is
massive MIMO channel modeling for 5G
wireless communications.
(Email: 1412684190@qq.com)
CHEN Yijian received the B.S.
degree in automation from Central South
University, Changsha, China, in 2006. He
is currently a Senior Engineer with the
ZTE Corporation, Shenzhen, China. His
research interests include intelligent elec-
tromagnetic surface, orbital angular mo-
mentum-based communications, and cell
free networks.
(Email: chen.yijian@zte.com.cn)
LI Jiandong received the B.E.,
M.S. and Ph.D. degrees in electrical en-
gineering from Xidian University, Xi’an,
China, in 1982, 1985, and 1991, respect-
ively. Since 1985, he has been with Xidi-
an University, where he has been a Pro-
fessor since 1994. From 2002 to 2003, he
was a Visiting Professor with the Depart-
ment of Electrical and Computer Engin-
eering, Cornell University, Ithaca, NY, USA. His current re-
search interests include mobile communications, broadband wire-
less systems, ad hoc networks, cognitive and software radio, self-
organizing networks, and game theory for wireless networks.
(Email: jdli@xidian.edu.cn)
Investigation and Comparison of 5G Channel Models: From QuaDRiGa, NYUSIM, and MG5G Perspectives 17