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QRIS: a QuaDRiGa-Based

Simulation Platform for

Reconﬁgurable Intelligent Surfaces

ILYA BURTAKOV1, ALEKSEY KUREEV1,2 (Student Member, IEEE), ANDREY TYARIN1,

EVGENY KHOROV1,2 (Senior Member, IEEE)

1Wireless Networks Lab, Institute for Information Transmission Problems of the Russian Academy of Sciences, Moscow, Russia (e-mail: {burtakov, kureev,

tyarin, khorov}@wireless.iitp.ru)

2HSE University, Moscow, Russia

Corresponding author: Evgeny Khorov (e-mail: e@khorov.ru).

Support from the Basic Research Program of the National Research University Higher School of Economics is gratefully acknowledged.

ABSTRACT Reconﬁgurable Intelligent Surfaces (RISs) are a promising way to improve the performance

of wireless networks. However, despite the high interest in the research community, no ﬂexible enough

simulation platform allows for studying the performance of wireless communication systems with RIS in

complex scenarios. To address this issue, the paper presents an open-source channel simulation platform

to study RIS in wireless communication systems. The developed open-source platform extends the

QuaDRiGa channel model, which is widely used for the performance evaluation of 5G and Wi-Fi networks

with multiple antennas. The paper shows how to use the platform — called QRIS — to evaluate RIS in

various frequency conditions, such as indoor/outdoor scenarios and multiple-RIS deployments.

INDEX TERMS Reconﬁgurable intelligent surface (RIS), channel modeling, MIMO systems, simulation,

5G and beyond, future Wi-Fi.

I. INTRODUCTION

Reconﬁgurable Intelligent Surface (RIS) technology is a

promising way to improve the performance of wireless com-

munication systems. RIS is a two-dimensional surface that

can be built using artiﬁcial electromagnetic metamaterials.

RIS consists of periodic arrangements of specially designed

sub-wavelength structural elements called Unit Cells (UCs).

By tuning each UC, RIS achieves unique electromagnetic

properties, such as negative refraction, perfect signal ab-

sorption, or anomalous reﬂection and scattering. So, it is

possible to control the process of reﬂected wave formation.

Therefore, RIS can expand the coverage area, increase

the channel capacity and mitigate interference in wireless

communication systems.

Although multiple testbeds have been developed [1]–[6],

there is a lack of simulation tools that can be used to evaluate

the performance of various RIS-related solutions in various

scenarios, as we show in detail in Section II.

In this paper, we address this problem and present

QRIS, an open-source RIS simulation platform that elim-

inates the drawbacks of existing platforms. QRIS is

based on the QUAsi Deterministic Radio channel Gener-

ator (QuaDRiGa) [7], which already supports many well-

approved channel models for various wireless technologies

and indoor/outdoor scenarios. Its essential features consider

such physical effects as shadowing and accurate modeling

of spatial consistency, which is important for scenarios with

MIMO.

This paper describes QRIS and provides several ex-

amples, of how it can be used to study networks with

multiple RISs and endpoint devices. Speciﬁcally, we use the

developed RIS model to evaluate its effectiveness in multiple

scenarios such as indoor Wi-Fi networks and 5G network

systems with various RIS architectures and RIS conﬁgura-

tion algorithms. An interested reader can get the platform

code through the link https://wireless.iitp.ru/ris-quadriga.

QRIS is a ﬂexible and accurate platform that can be used

to evaluate the performance of wireless systems with RISs in

various scenarios with any number of RIS, transmitters (Tx),

and receivers (Rx). Compared with the existing platforms,

which are analyzed in Section II and summarized in Table 1,

QRIS has the following advantages.

•QRIS allows using various UC radiation patterns, in-

cluding realistic ones, obtained from real measurements

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Burtakov et al.: QRIS: a QuaDRiGa-Based Simulation Platform for Reconﬁgurable Intelligent Surfaces

or comprehensive simulation using CST Microwave

Studio [8].

•QRIS includes UCs models that take into account

polarization, radiation pattern, Radar-Cross Sections

(RCS), and phase shift variations depending on the

angle of incidence.

•QRIS is suitable for the evaluation of scenarios with

multiple RIS, including multi-hop transmissions.

•QRIS has an accurate channel model based on

QuaDRiGa. Consequently, QRIS can be used for a

wide frequency range, various RIS positions, and mul-

tiple antennas on Tx and Rx for MIMO evaluation.

•QRIS is ready to integrate with system-level simulation

platforms such as ns-3 [9] to study cooperation of RIS

and widely used technologies such as 5G and Wi-Fi 7,

and also those which are under development.

The rest of the paper is organized as follows. Section II

reviews existing simulation tools, and Section III describes

the QuaDRiGa channel generator. Section IV presents a RIS

system model and discusses its peculiarities. In Section V,

we describe the RIS model and its implementation in the

QRIS simulation platform, while Section VI presents our

numerical results. Section VII concludes the paper.

II. RELATED WORKS

Numerous simulation tools are already designed to study the

performance of RIS in different scenarios. However, most of

the tools are devoted to the study of certain effects arising

in particular networks with RIS. For example, in paper [10],

the authors use the simulation for precoder development in

mmWave communication systems with RIS. They explicitly

assume that the signal goes from the Tx to the Rx only via

RIS, so there is no direct subchannel between the Tx and

the Rx, which limits the range of application scenarios.

The paper [11] uses a RIS model that assumes isotropic

scattering to explore issues of channel hardening effect in

channels with RIS in the SISO system. The authors posited

the assumption of uniform distribution of scatterers in front

of the RIS, which deviates from the non-ideal conditions

observed in real-world scenarios. Furthermore, due to the

deterministic nature of the RIS position, employing random

matrix modeling for the channels leads to an inadequately

calibrated tool.

The paper [12] describes the model of the whole com-

munication system that includes a RIS but assumes free-

space propagation. Although the developed models are used

to ﬁnd such a RIS placement that maximizes the signal-to-

noise ratio (SNR) on the receiver in particular scenarios, the

results cannot be generalized because of the aforementioned

assumptions and the limitations of the models.

The authors in [13] propose a free-space path loss model

for wireless communications with RIS, based on a detailed

study of the RIS’s physical properties and electromagnetic

nature. The authors use an anechoic chamber to perform

experiments and show that the power reﬂected from a RIS

follows a scaling law that depends on many parameters, in-

cluding the size of the RIS, the mutual distances between the

Tx/Rx and the RIS (i.e., near-ﬁeld vs. far-ﬁeld conditions).

Simulation results validate that the proposed channel model

matches well with the experimental measurement. However,

in this paper, a single-antenna system is considered. In

addition, the inﬂuence of the direct channel from Tx to Rx

is not considered.

Some of the existing works consider RIS modeling based

on physical propagation and Large-scale path loss models

using the RCS concept. Paper [14] introduced a path loss

model for RIS communications, which utilized RCS as

a basis. The received power was linked to the distances

between the Tx and Rx to the RIS, the angles within the

Tx-RIS-Rx triangle, and the effective area and reﬂection

coefﬁcient of each UC. Furthermore, channel measurements

were performed in various scenarios to validate the proposed

model under both near-ﬁeld and far-ﬁeld conditions.

In [15] authors introduced a path loss model for RIS

communications using the physical optics technique. They

also explained the presence of multiple UCs on the surface,

which individually function as diffuse scatterers but can

collaboratively beamform the signal in a speciﬁc direction

with a deﬁned beamwidth.

Research [16] presented a uniﬁcation of the contrasting

characteristics of a RIS acting as a scatterer and a mirror

using the free-space Green function. The outcomes demon-

strated that the perception of the RIS can vary based on its

dimensions and distance, appearing as a zero, one, or two-

dimensional entity. Moreover, the radiated power exhibited

a dependency on the distance, with a power relation of the

fourth, third, or second power, respectively.

Authors of [17] put forward a path loss model that

shares similarities with the model presented in [13]. The

proposed model aligns with the concept of RCS. In this

approach, the maximum RCS of the UC can be estimated

by multiplying the gain of the UC with its corresponding

area. The simulation demonstrated the effectiveness of the

suggested model in capturing the behavior of the reﬂection

coefﬁcient. The model in this paper was designed to account

for both normal and oblique incidences, as well as for both

TE and TM polarizations.

The most detailed RIS simulation platform is the open-

source SimRIS channel model described in paper [18],

[19]. SimRIS is a mmWave channel model with a RIS in

the presence of scatterers. SimRIS generates with given

probabilities LOS and NLOS channels between Tx, Rx,

and RIS. This model includes many physical characteristics.

Moreover, SimRIS uses an accurate RIS model [20] to

simulate realistic gains and response matrices of UCs.

However, the SimRIS toolbox supports only a predeﬁned

set of RIS locations with vertical orientation. Apart from

that, SimRIS is not applicable for modeling scenarios with

multiple devices and does not support low frequencies (up

to 6 GHz), RISs that have many UCs, or a close location

of the Rx or Tx antennas to the RIS. Moreover, the authors

consider the relatively simple radiation pattern of the UC. In

2VOLUME 4, 2016

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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3306954

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Burtakov et al.: QRIS: a QuaDRiGa-Based Simulation Platform for Reconﬁgurable Intelligent Surfaces

further works, these authors emphasize that RIS improves

overall communication performance if it is deployed near

Tx or Rx [21].

RIS models from the cited papers are well suited for

speciﬁc tasks but are not ﬂexible enough for RIS research

in various scenarios with different device placements and

used channel frequencies (see Table 1). Thus, it is impor-

tant to develop a model in a wide frequency range with

the possibility of arbitrary RIS placement, its orientation,

propagation, and environmental conditions based on well-

approved channel models following the requirements of

3GPP. This problem is addressed in this paper, where we

design a new QRIS model.

III. QUADRIGA DESCRIPTION

QuaDRiGa is a 3D stochastic channel model that takes

into account how the channel changes with time. It has

been developed at Fraunhofer HHI to model MIMO radio

channels for various wireless networks such as indoor,

outdoor, and satellite in 0.45-100 GHz frequency range [22].

The propagation of the signal through the wireless medium

is modeled using the interaction of the electromagnetic wave

with scatterers.

Each scatterer splits the wave into multiple scattered

sub-rays with different amplitudes and phases [23]. The

other parameters of the sub-rays such as delay, angles

of departure, and arrival are approximately the same for

all sub-rays reﬂected from the same scatterer, so sub-rays

are combined into a cluster. The cluster parameters are

determined stochastically, based on statistical distributions

extracted from the real channel measurements. While the

phase and amplitude of each sub-ray are calculated in the

model, taking into account the actual path length.

Apart from the basic channel model, QuaDRiGa con-

tains much auxiliary functionality, antenna models, path-loss

models, and built-in tables of parameters for various scenar-

ios [7]. For example, it implements the 3GPP 38.901 model

used for the performance evaluation of cellular systems. The

accuracy of the model is supported by several measurements

campaigns, e.g., 3GPP [22]. Consequently, it is used to

evaluate various proposals to 3GPP speciﬁcations. Thanks

to accurate modeling of spatial consistency, QuaDRiGa can

be used in studies related to massive MIMO and multi-

cell transmission systems [24]. Also, multiple solutions are

proposed to reduce the computational complexity of channel

models [25].

All these features make QaDRiGa attractive to model

communication systems with RIS, and we choose

QuaDRiGa as the basis for our platform.

IV. QRIS MODEL

We model the wireless channel in the presence of RIS as

follows. The emitted signal from Tx arrives at Rx as a

superposition of multiple signals passed through the channel

with RIS and clusters, see Fig. 1. The wireless channel

between Tx and Rx consists of several subchannels, a direct

Scatterers

distribution

distribution distribution

distribution

Figure 1: Considered environment of RIS, Tx, and Rx with

a random number of clusters.

subchannel (Tx, Rx), a subchannel between Tx and RIS (Tx,

RIS), and a subchannel between RIS and Rx (RIS, Rx). Each

subchannel has multiple clusters. With a given state of each

cluster, QuaDRiGa generates the channel impulse response

for a pair of Rx and Tx. Through the impulse response, it is

possible to get a complex channel gain for a certain central

frequency. For devices with multiple antennas, the channel

gain coefﬁcient becomes a matrix of coefﬁcients.

Consider the scattering process of the signal on RIS with

MUCs. The noise-free signal yreceived at the Rx in a

system with multiple Rx antennas Nr≥1and multiple Tx

antennas Nt≥1is expressed in vector form as follows:

y=Hx≡(RΦT+H)x,(1)

where x= [x1, x2, . . . , xNt]T∈CNt×1is the vector of Tx

signal, y∈CNr×1is the Rx signal vector, xidenotes an

M-ary phase shift keying/quadrature amplitude modulation

(PSK/QAM) symbol transmitted through i-th transmit an-

tenna, H∈CNr×Ntis the channel matrix between Tx and

Rx, including the direct channel H∈CNr×Ntand the RIS-

aided channel RΦT.R∈CNr×Mis the channel coefﬁcient

matrix between RIS and Rx, and T∈CM×Ntis the channel

coefﬁcients matrix between Tx and RIS. The matrix H

corresponds to the direct connection channel between Tx

and Rx.

Φ= diag α1ejβ1, . . . , αMejβM∈CM×M(2)

is a diagonal RIS reﬂection matrix, where each ele-

ment, called reﬂection coefﬁcient, determines signal gains

α1. . . αMand phase shifts β1. . . βMon the corresponding

UCs. For a MIMO channel without RIS, the throughput

is determined solely by the channel matrix H. A MIMO

channel with RIS described by equation (1) depends on the

RIS response matrix Φbecause it affects channel matrix H.

To maximize the received power of the user, all elements

of the RIS shall set their reﬂection amplitude to one (i.e.,

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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3306954

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Burtakov et al.: QRIS: a QuaDRiGa-Based Simulation Platform for Reconﬁgurable Intelligent Surfaces

Table 1: Comparison of existing RIS models and QRIS

Ref. Channel

Model

Main Features Limitations

[10] Clustered-

delay-line

mmWave

channel

Developed for millimeter-wave ultra massive

MIMO RIS-aided systems.

Cannot be applied for lower fre-

quency channels due to exploiting

the sparsity of mmWave channels.

Uses a simpliﬁed radiation pattern

[11] Rayleigh

fading channel

Adopted for isotropic scattering, takes into ac-

count channel properties, including rank and

channel hardening for SISO systems.

Applied only for scenarios with

isotropic scattering environment. Im-

possible to vary distances between

devices.

[12] Free-space

channel

Aimed to ﬁnd the optimal RIS placement with

respect to the transmitter and receiver antenna

positioning in a mmWave SISO system.

Cannot be applied for multi-antenna

systems. Uses a simpliﬁed channel

model.

[13] Experimental

channel

Includes a free-space path loss model for RIS-

assisted wireless communications based on the

electromagnetic and physics properties of the

RISs. Veriﬁed by experimental measurements.

Can be applied in a limited set of

scenarios and RIS architectures.

[14]–[17] Physical based

large scale path

loss

Take into account the features of RIS due to

the use of the RCS concept. The impact of

the radiation patterns of the antennas and RIS

elements was considered.

Not applicable for modeling mul-

tiuser systems with RIS. Take into

account only large-scale parameters

of path loss.

[18], [19], [21] Clustered-

delay-line

mmWave

channel

Includes a 3GPP compliant clustered-delay-line

channel for RIS-assisted systems.

Can be applied only for a limited set

of device orientations and frequen-

cies. The platform is equipped with

one simpliﬁed radiation pattern.

QRIS

(developed

in this paper)

Geometric

Stochastic

Channel Model

Same features but without the limitations of the

models above.

The radiation pattern does not de-

pend on the angles of arrival of the

sub-rays.

αi= 1,∀i= 1, . . . , M ) for maximum signal reﬂection.

V. QRIS DESIGN

The original purpose of the QuaDRiGa simulation platform

is to study MIMO systems. Consequently, the off-the-box

version of QuaDRiGa cannot simulate scenarios involving

RIS. To incorporate RIS-assisted communication into the

simulations, we approach the problem by treating each

subchannel in (1) as a conventional MIMO channel and

employ QuaDRiGa to acquire the respective channel matrix.

A RIS can be presented as a combination of receiving and

transmitting antenna arrays: for the subchannel (Tx, RIS),

the RIS acts as a receiver, and for the subchannel (RIS, Rx),

it acts as a transmitter. As QuaDRiGa restricts the creation

of an object that is a receiver and a transmitter, at the same

time, two copies of the RIS antenna array are placed at the

same point in space [26].

By default, we model RIS as an array of UCs, located

in a square grid with the step equal to the half-wavelength

d=λ

2[27] forming a planar antenna array, where λis

the incident radiation wavelength. To deﬁne the RIS, we

set the number Mof UCs, the type of UC used in RIS,

the position of the center of the RIS, and the direction of

the RIS, which is deﬁned by rotation angles of the RIS

relative to the coordinate axes as shown in Fig. 2. Tx and

Rx locations are also conﬁgurable.

A. RADIATION PATTERNS OF VARIOUS UCS

In QRIS, various RISs may have different UCs but all

UCs of the same RIS have the same radiation pattern.

We have implemented various radiation patterns in QRIS,

see Fig. 4. In all the cases, we consider that a UC is

an electrically small low-gain element above a conducting

ground layer [20].

The ﬁrst type of UC denoted as COS-UC has a widely

used amplitude radiation pattern √γcosq[28], where

γ= 2(2q+ 1) is a normalization factor that analytically

ensures that the effective aperture of the UC in the direction

of the transmitter is equal to λ

22[20]. After normalization

the power radiation pattern Geof a UC is as follows:

Ge(θ) = 2(2q+ 1) sin2q(θ),0≤θ≤π, (3)

where θis the elevation angle of incidence for the sub-

ray from the cluster on the UC or the elevation angle of

reﬂecting from the UC. Note that this deﬁnition of θdiffers

from the original QuaDRiGa deﬁnition. Consequently, in

this paper, the antenna pattern uses function sin() instead

of cos(). In COS-UC, 2(2q+1) = πor q≈0.285 according

to [20] because Geπ

2=π.

To implement COS-UC, we use the parametric antenna

template of QuaDRiGA with the following amplitude radi-

ation pattern:

E(θ, φ) = AqB+ (1 −B)·(sin θ)C·exp (−D·φ2),

(4)

where φdenotes the azimuth angle of incidence or reﬂec-

tion, A, B, C, D are the coefﬁcients, which are obtained by

equating Ge(θ) = E2(θ, φ)(3) and (4): A=√π,B=

0,C= 2q,D= 0. A three-dimensional graph of the

considered radiation pattern is shown in Fig. 4-a.

Since a signiﬁcant part of the experimental RIS stud-

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Burtakov et al.: QRIS: a QuaDRiGa-Based Simulation Platform for Reconﬁgurable Intelligent Surfaces

𝛳𝑟

𝜑𝑟

𝑅𝐼𝑆

𝒁

𝑿

𝒀

𝑅𝑥

𝑇𝑥

1𝜑𝑖

𝛳𝑖

𝒀

𝛳𝑎

𝜑𝑎

𝛳𝑑

𝜑𝑑𝒀

𝑿

𝑿

Figure 2: Geometry and signal propagation in RIS-aided

system.

ies [29]–[32] considers UCs with patch-antennas, the second

type of implemented UCs corresponds to them. QuaDRiGa

presents a patch-antenna model (Q-UC), the radiation pat-

tern of which is shown in Fig. 4-b. Note that the existing

QuaDRiGa patch-antenna model did not imply normaliza-

tion in terms of the effective aperture of the UC, unlike

COS-UC. To ﬁx it, we multiply its radiation pattern by the

normalization factor.

To take into account the inﬂuence of polarization in the

simulation, we copy the corresponding COS-UC or Q-UC

radiation pattern to the same point in space rotating them

by 90◦and assigning them to interact with a different

polarization. However, both COS-UC and Q-UC models

do not take into account that the radiation pattern changes

when the RIS is reconﬁgured and the angle of incidence

is changed. To consider this effect, we introduce the third

UC model, which corresponds to a patch antenna controlled

by a PIN diode. Such a UC is widely used in many RIS

Figure 3: The structure of the CST-UC.

(a) (b)

Figure 5: Equivalent circuits of the PIN diode (a) in the ON

state and (b) in the OFF state.

prototypes [29]–[32].

We model this UC in the CST Microwave Studio [33],

so, the third UC model is referred to as CST-UC. Figure 3

illustrates the structure of the modeled UC. The top layer

consists of a square copper patch with thin 35 µm and

a PIN diode (SMPA1320-079LF). The middle layer is a

dielectric Taconic TLX-8 whose dielectric constant ϵis 2.55

and loss tangent is 0.0017. The bottom layer is full metal

for grounding. The anode of the PIN diode is connected

to the edge of the patch and the cathode is connected to

the ground via the dielectric. Other dimensions of UC are

a= 26 mm, px= 13.55 mm, h= 1.6 mm.

.

.

°, °

.

°, °

.

°, °

.

°,°

.

°, °

.

°, °

.

°, °

.

°, °

Figure 4: Illustration of various radiation patterns of UC.

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Burtakov et al.: QRIS: a QuaDRiGa-Based Simulation Platform for Reconﬁgurable Intelligent Surfaces

QuaDRiGa operates with power radiation patterns Ge,

which are applicable for active antennas. However, CST-

UC is a passive element that cannot be described solely

by the power radiation pattern. To solve this issue, we use

the Radar Cross-Section (RCS) approach, which takes into

account the scattering ﬁeld from CST-UC. To calculate CST-

UC bistatic RCS in CST, we radiate a plane wave with linear

polarization and with a ﬁxed angle of incidence on a single

CST-UC in open space. The RCS depends on the shape,

sizes, and material of CST-UC, as well as the polarization

and the incident angle of impinging plane wave. To obtain

Geof CST-UC for each angle of incidence θi, φivia its

RCS σ, we use expression from work [26]:

Ge(θ, φ) = p4πσ (θ, φ)

λ,(5)

where θ, φ are angles of directions on Rx or each cluster

in subchannel (RIS, Rx). Note that such an approach does

not take into account the mutual coupling effect. However,

in Section V-C, we show that this assumption is acceptable

for the considered UC as it gives small errors. Using the

described approach, it is possible to model the radiation

pattern of any UC in the CST and integrate this pattern into

QRIS.

The PIN diode of the CST-UC has two states: OFF and

ON, which modify its radiation patterns as shown in Fig. 4-c

to 4-j for two polarizations. These patterns are non-identical

due to the different equivalent schemes of the OFF and ON

states, shown in Fig. 5. In the ON state, the equivalent circuit

of the PIN diode is a serial connection of a resistor (R = 2 Ω)

and an inductor (L = 0.6 nH), see Fig. 5-a. In the OFF state,

the equivalent circuit of the PIN diode is a serial connection

of a capacitor (C = 0.3 pF) and an inductor (L = 0.6 nH),

see Fig. 5-b.

We consider the UC radiation patterns in vertical and

horizontal polarization. The radiation patterns of CST-UC

have much in common with the COS-UC and Q-UC patterns

in the case of normally impinging wave, see Fig. 4-a and 4-

d. However, in contrast to COS-UC and Q-UC, the CST-UC

pattern strongly depends on the angle of incidence. More

precisely, the direction of the main lobe of the patterns

changes as shown in Fig.4-e to 4-j when we change the

impingement angles from normal incidence with φi=

90◦, θi= 90◦to φi= 10◦, θi= 90◦. QRIS takes into

account this difference in the radiation patterns.

To obtain phase shifts of CST-UC in the CST, we consider

boundary conditions with similar UCs nearby one. Figure 6

shows the frequency response of CST-UC operating in the

5.3GHz band, obtained with simulation in the CST for

the normal incidence case. The parameters of the CST-UC

are chosen in such a way that the phase shift difference

between the two states equals πfor 5.3 GHz for the

horizontal polarization and normal incidence. At the same

time, the phase shift difference is close to zero for the

vertical polarization. When the angle of incidence changes,

the phase shift difference remains close to zero for the

4 4.5 5 5.3 5.5

freq. [GHz]

-3

-2

-1

0

1

2

3

Phase shift [rad]

ON state, Vert. pol.

ON state, Horiz. pol.

OFF state, Vert. pol.

OFF state, Horiz. pol.

Operating band

Figure 6: Frequency response of the CST-UC for ON and

OFF states for normal impinging with θi= 90◦,φi= 90◦.

0102030 40 50 60 70 80 90

100

110

120

130

140

150

160

170

180

-2

-1

0

1

2

3

4

Phase shift [rad]

ON state, Vert. pol.

ON state, Horiz. pol.

OFF state, Vert. pol.

OFF state, Horiz. pol.

Horiz. pol. phase diff.

Vert. pol. phase diff.

Figure 7: Phase shift difference depending on the angle

of incidence for CST-UC in ON, OFF states with two

polarizations, θi= 90◦, central frequency is 5.3 GHz.

vertical polarization. For horizontal polarization, the phase

shift difference changes, which is shown in Fig. 7, because

the impedances of CST-UC in the ON and OFF states

depend on the angle of incidence [34]. The increase in

the angle of incidence causes the deviation of phase shift

difference from πbetween the ON and OFF states.

B. RIS CONFIGURATION

Consider the diagonal matrix Φfrom (1), which describes

the incident signal ampliﬁcation and the phase shift of

each UC. Being passive, RIS adjusts mainly the phase shift

because changing the amplitude requires much power. By

tuning the phase shifts of UCs, the RIS induces some phase

between reﬂected signals and makes them interfere either

constructively to increase the desired signal strength at the

receiver or destructively to mitigate the co-channel interfer-

ence. Let us describe how to conﬁgure the aforementioned

UCs.

1) COS-UC and Q-UC conﬁguration

If we consider a COS-UC or Q-UC with radiation patterns

independent of the phase shift, a particular conﬁguration of

the RIS is determined by Φbecause the channel matrices

during one experiment are ﬁxed. Consequently, Φcan be

chosen to maximize the throughput with given channel

matrices. Although QRIS supports customization of the

algorithm used for RIS conﬁguration, by default, we assume

6VOLUME 4, 2016

content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3306954

Burtakov et al.: QRIS: a QuaDRiGa-Based Simulation Platform for Reconﬁgurable Intelligent Surfaces

the perfect knowledge of channel matrices at all devices

involved in communications via RISs. Such information

can be measured with the techniques from [35] and then

distributed to the other devices.

Given the channel matrices used in (1), we use the non-

convex MIMO channel capacity maximization algorithm

from the paper [36] to obtain the matrix Φand power dis-

tribution matrix on Tx that maximize the ergodic achievable

rate for the MIMO system:

R= log2det INr+1

PN

HQHH,(6)

where INris the identity matrix of size Nr,Q≜

ExxH∈CNt×Nt,Q⪰0,E∥x∥2≤Ptis the trans-

mitted signal covariance power matrix, PNis the channel

noise power. E[·]denotes the statistical expectation, His

the matrix of the MIMO channel with the RIS from (1).

The algorithm implements the Alternative Optimization

technique and maximizes the ergodic achievable rate under

the following constraints. First, the total Tx power is limited.

Second, the modulus of the complex reﬂection coefﬁcients

shall equal one: αi= 1, i = 1 . . . M . The algorithm

iteratively optimizes one UC’s phase shift βi,i= 1 . . . M,

and the Tx covariance matrix Qat each time, while the

other variables are ﬁxed. Each outer iteration consists of

two steps. In the ﬁrst step, given the current Tx covariance

matrix Qand N−1UCs phase shifts βi,i=jother than the

considered one βj, the optimal value of βjcan be obtained.

In the second step, given all UCs phases βi,i= 1 . . . M , the

optimal solution of the Tx covariance matrix is calculated

via the water-ﬁlling technique [37].

The algorithm stops when the required accuracy is

achieved or the maximum number of outer iterations is

reached. In contrast to [36], at the second step we consider

only quantized values of βjof RIS elements because on real

devices the phase can take values only from some discrete

set, e.g., βi∈ {0, π},i= 1 . . . M [38], [39].

2) CST-UC conﬁguration

If the RIS consists of more realistic UCs, the radiation pat-

terns of which depend on the UC state, the aforementioned

Optimal algorithm cannot be used because channel matrices

change when the state of each UC changes. For example,

the CST-UC has different radiation patterns for the OFF/ON

states shown in Fig. 4-c to 4-j. Therefore, to conﬁgure a RIS

consisting of CST-UCs, we need another algorithm, e.g., the

one base on the idea from [40], which is referred to as ON-

OFF and works as follows.

To ﬁnd the most effective RIS conﬁguration, we measure

the receive power obtained for multiple RIS conﬁgurations

with random states of UCs. For each UC, we divide all the

measurements into two groups: when the UC’s state is OFF

or ON. Then, we select the state of the considered UC based

on the group, where the average Rx power is higher.

1 5 10 15

xRIS [m]

0

5

10

15

20

25

SNR [dB]

SimRIS 28GHz

QRIS 28GHz

SimRIS 73GHz

QRIS 73GHz

Figure 8: QRIS validation in the Indoor scenario.

C. VALIDATION

The validation consists of three steps. First, we compare

QRIS with the SimRIS platform to validate the proposed

approach for modeling RIS itself with COS-UC. Second,

we validate the CST-UC model using full-wave simulation

in CST with small RISs. Third, for large RISs, we use a

hybrid approach with full RIS RCS calculated in CST and

analytical expressions for free-space propagation.

1) QRIS vs. SimRIS

We have validated QRIS by comparing its results with the

results obtained with SimRIS under the same conditions.

Note that since QRIS is more ﬂexible than SimRIS, some

experiments can not be done in SimRIS.

To provide validation experiments, we consider synthetic

scenarios when polarization, clusters, and the shadowing

effect for QRIS and SimRIS models are disabled. Such an

experiment allows testing only the RIS model implemented

in QuaDRiGa and COS-UC (because only this type of UC

is supported by SimRIS), excluding the complex features of

channel models.

We consider the 3GPP 38.901 Indoor and 3GPP 38.901

UMi channel models with the corresponding geometric

arrangements of single-antenna Tx, RIS, and single-antenna

Rx for different numbers of RIS elements and different

frequencies. We compare the Signal-to-Noise ratios (SNR)

obtained in QRIS and SimRIS if RIS contains COS-UCs.

We vary the location of RIS along the xaxis, while the

coordinates of Tx and Rx are ﬁxed. Subchannels (Tx,

RIS) and (RIS, Rx) are LOS, and subchannel (Tx, Rx) is

NLOS. In all validation experiments, the transmit power is

Pt= 5 dBm, and the noise power is PN=−120 dBm. We

use the optimal RIS conﬁguration algorithm from paper [36]

in both QRIS and SimRIS platforms.

Figure 8 shows the results obtained in the Indoor scenario.

The coordinates of the Tx, Rx, and RIS are (0, 0, 3) m,

(20, 0, 2) m, (xRIS, 5, 3) m respectively, xRIS ∈[1; 15] m.

Operating frequencies are 28 GHz and 73 GHz, number of

UCs M= 256.

Figure 9 shows the results of another validation experi-

ment for the UMi scenario, where coordinates of the Tx,

Rx, and RIS are (0, 0, 3) m, (50, 0, 2) m, (xRIS, 15, 3) m,

VOLUME 4, 2016 7

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Burtakov et al.: QRIS: a QuaDRiGa-Based Simulation Platform for Reconﬁgurable Intelligent Surfaces

5 10 20 30 40 50 60 65 70

xRIS [m]

10

12

14

16

18

20

22

24

SNR [dB]

SimRIS 3.7 GHz

QRIS 3.7 GHz

SimRIS 5.3 GHz

QRIS 5.3 GHz

Figure 9: QRIS validation in the UMi scenario.

xRIS ∈[5; 65] m. Operating frequencies are 3.7 GHz and

5.3 GHz and M= 64.

Figures 8 and 9 demonstrate that QRIS accurately es-

timates the performance of the RIS-assisted networks and

SNR in QRIS coincides with SNR estimated using SimRIS.

In more complex scenarios, QRIS inherits QuaDRiGa

features such as shadow fading and stochastic arrangement

of clusters that are validated by QuaDRiGa developers [41].

The developed platform is ﬂexible and can use complex

radiation patterns for each UC, including experimental ones.

2) QRIS vs. CST full-wave simulation

To validate CST-UC behavior, we made a comparison of

two free-space scenarios using QRIS with CST-UC and

full-wave simulation in CST. For that, we model Tx and

Rx with a half-wave dipole antenna in CST and import

their radiation patterns into QRIS so that the transmitter and

receiver behavior is the same in both approaches. The Dipole

antenna has a total length of 26 mm, the gap between arms

is 0.5mm, and the diameter of the arm is 0.2mm. There

is no possibility of completely excluding subchannel (Tx,

Rx) in CST, so it signiﬁcantly interferes with the analysis

of subchannels in which RIS participates. We consider a

scenario with the following coordinates: Tx (0,0,2) m, Rx

(dTR,0,2) m, RIS (dTR

2,dTR

2,2) m, where distance between

Tx and Rx dTR ∈ {0.5,1,1.5,5,10}m, so azimuth angle

of incidence φi= 45◦and azimuth angle of reﬂection

φr= 135◦. We vary the number of RIS UCs to determine

the discrepancy between to approaches as the number of

UCs increased. Also, we consider the states of RIS in which

all UCs are in the OFF state (Full OFF) or the ON (Full

ON) state. In addition, we calculate Rx power without RIS

impact using the Friis equation for free-space propagation

PRx =λ2PTxGTx (φd, θd)GRx (φa, θa)

(4π)2d2

TR

,(7)

where λis wavelength, PTx is transmitting power, GTx and

GRx are power radiation patterns of Tx and Rx respectively,

φd, θdare angles of departure of signal from the Tx, φa, θa

are angles of arrival of signal at the Rx, see Fig. 2. For the

validation, we take PTx = 1 W power emission in QRIS

and CST.

0 1 2 3 4 5 6 7 8 9 10

dTR[m]

-70

-65

-60

-55

-50

-45

-40

-35

-30

PRX [dB]

CST Full OFF 1x1

QRIS Full OFF 1x1

CST Full ON 1x1

QRIS Full ON 1x1

CST Full OFF 2x2

QRIS Full OFF 2x2

CST Full ON 2x2

QRIS Full ON 2x2

CST Full OFF 3x3

QRIS OFF 3x3

CST Full ON 3x3

QRIS Full ON 3x3

Friis

Figure 10: QRIS vs. CST full-wave validation.

Figure 10 shows the Rx power as a function of dTR. For

any number of RIS UCs and any distance, the difference

between the Rx received power both in the OFF state and

in the ON state, compared to the power obtained with (7),

is negligible. This is because the size of the RIS in these

experiments is extremely small and in any state, RIS does

not make a signiﬁcant contribution to the power, since there

is a free-space line-of-sight subchannel between Tx and Rx.

However, this experiment shows that the values of received

power obtained from the QRIS, CST simulation, and the

Friis equation can be compared. Unfortunately, increasing

the number of UCs in full-wave simulation dramatically

raises computational time and memory. To validate QRIS

performance in scenarios, where the impact of RIS to Rx

power is signiﬁcant, we apply a hybrid approach based on

the Friis equation, described below.

3) QRIS vs. ERRE RCS

The disadvantage of the previous approach to validation is

the impossibility of eliminating the inﬂuence of subchannel

(Tx, Rx), as well as the extremely limited number of UCs in

CST experiments. To increase the size of the RIS as well as

to take into account the features of the RCS modeling in the

CST and remove the inﬂuence of the subchannel (Tx, Rx)

on the system, we propose a hybrid approach to validation

using an Extended Radar Range Equation (ERRE) [42]

PRx =λ2PTxGTx (φd, θd)GRx (φa, θa)σRIS

(4π)3d2

TId2

IR

,(8)

where σRIS is the RCS of the whole RIS, dTI and dIR are the

distances from Tx to RIS and from RIS to Rx respectively.

For validation, we consider a free-space scenario where Tx

has coordinates (0,0,2) m, Rx is located at (7k, 0,2) m,

and the RIS is in the point (5k, 5k, 2) m, k∈1,2. So,

in both coordinate sets φi= 45◦,φr≈22◦. In these

experiments, the direct subchannel (Tx, Rx) is blocked. It is

not considered in the analysis because it does not interfere

with channels associated with the RIS. In these scenarios,

8VOLUME 4, 2016

content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3306954

Burtakov et al.: QRIS: a QuaDRiGa-Based Simulation Platform for Reconﬁgurable Intelligent Surfaces

0 10 20 30 40 50 60 70 80 90 100

M

-130

-125

-120

-115

-110

-105

-100

-95

-90

-85

-80

-75

PRx [dB]

Figure 11: Conﬁgured and Full OFF states vs. ERRE RCS

validation.

we study how the divergence between ERRE and QRIS

depends on dTR. We consider two cases: (i) the RIS is

conﬁgured with the ON-OFF algorithm, and (ii) it is in

the Full OFF state. To obtain the RCS of the entire RIS

conﬁgured with the ON-OFF algorithm in CST, we ﬁrst

obtain optimal conﬁguration information from QRIS and

then create the RIS with the appropriate conﬁguration in

CST.

Figure 11 shows the Rx power as a function of the number

of UCs M. The minimum of PRx near M= 40 for the

Full OFF state is caused by the behavior of the RCS of

the entire RIS. Speciﬁcally, by varying M, we change RCS

σRIS, and according to (8) PRx is proportional to RCS σRIS .

The RCS depends on the direction and the strength of side

lobes, which non-monotonically depend on the size of the

RIS.

For a large RIS, the degree of discrepancy between QRIS

and ERRE does not exceed 1dB when the RIS is conﬁgured

with the ON-OFF algorithm and 1.7dB when the RIS is

in the Full OFF state. Note that in practice, the Full OFF

state is rarely optimal, which means that the discrepancy is

below 1.7 dB. In addition, the discrepancy between ERRE

and QRIS weakly depends on the distance.

The discrepancy between QRIS and ERRE is explained

by the assumptions used in the QRIS: the method of recal-

culating the RCS into radiation pattern [26] and neglecting

mutual coupling between UCs. A higher discrepancy in the

Full OFF state is explained by the increased mutual coupling

of the UCs in the OFF state due to the operation of UCs

at the resonant frequency in this state [43]. A limitation

of our platform is the usage of identical UCs, however, in

practice, the same UCs are used on RIS prototypes [44]–

[46]. Additionally, the mutual coupling effect introduces

a distinction between the isolated and grid-embedded UC

patterns. Moreover, the pattern of the center UC differs from

that of the RIS-edge UC also due to the mutual coupling.

However, when UCs are located at a distance close to a

half-wave, the effect of mutual coupling is signiﬁcantly

reduced [47]. Thus, in this work, we assume that all UCs

exhibit an identical pattern and can be modeled free of

surrounding ones. Our further step is to include mutual

coupling effects into our platform, using approaches from

papers [14], [17], [48].

VI. NUMERICAL RESULTS

Let us compare the performance of QRIS with various

radiation patterns and conﬁguration algorithms in the SISO

scenario taking into account polarization, changes in the

radiation patterns, and phase shifts difference with a change

of the incidence angle. In all experiments, we consider a

noise power spectral density of −173 dBm/Hz with a 9 dB

noise ﬁgure, and a system bandwidth of 80 MHz, which

yields PN=−85 dBm.

In the ﬁrst experiment, we compare the behavior of differ-

ent UCs radiation patterns in the same scenario. We consider

the UMi scenario for those experiments with LOS subchan-

nels (Tx, RIS) and (RIS, Rx), while the subchannel (Tx,

Rx) is NLOS. The coordinates of the Tx, Rx, and RIS are

(0, 0, 2) m, (100, 0, 2) m, (0, 10, 2) m. Operating frequency

is 5.3 GHz and Tx power is PTx = 30 dBm. We vary the

number of RIS elements Mand measure SNR at Rx. Let

the RIS supports 1-bit quantization. For the COS-UC and

Q-UC, we use zero phase shift in the OFF state for both

polarization and 180◦phase shift in the ON state for both

polarization. For more realistic CST-UC, the phase shifts

are calculated using the CST which takes into account the

state of the UC and polarization of the impinging wave.

To conﬁgure the RIS, we use the Optimal and ON-OFF

algorithms described in Section V-B.

Listing 1: Sample of QRIS scenario

%1 C r ea t e a l i n e a r p o l a r i z e d a n t e nn a a r r a y o f ha l f −wa ve − d ip o l e a n t e n na s f o r Tx

Tx_ 1 = T x _ l i n e a r ( T x _ P o s i ti o n , Tx Nu mb er Of An t en na s , s . w av e l en g t h , ...

' h a l f −wa ve − d i p o l e ' , CentralFrequency ) ;

%2 C r ea t e a l i n e a r p o l a r i z e d a n t e nn a a r r a y o f ha l f −wa ve − d ip o l e a n t e n na s f o r Rx

Rx_1 = Rx _ l i n e a r ( R x _ P o s i ti o n , RxN um be rO fA n te nn as , s . w a v e le n g th , ...

' h a l f −wa ve − d i p o l e ' , CentralFrequency ) ;

%3 C re a t e 2D ar r a y of a n t en n a s f or RI S w i th P o l a r i z a t i o n

RIS_ CST = RI S _ d u a l p ol ( R I S _ P o s i t i o n , RISN An t_x , RIS NA nt _z , s . w a v el e n g t h , ...

CentralFrequency) ;

% P o l a r i z e d CST−UCs

[ a _O n _p o l1 , a _O n_ p ol 2 , a _ O ff _ p o l 1 , a _ O f f _ p o l 2 ] = M ake_ RIS_U C ( Ce n t r a l F r e q u e n c y , ...

x_ RI S_ coo rd ) ;

%4 C r e at e l a y o u t

l = Cr ea te _Q ua D Ri Ga _l ay ou t ( s , Tx_1 , Rx_1 , RIS_ CST , sc en ar io _n am e_ LO S , ...

scenario_name_NLOS) ;

%5 O bt a i n c h a n n e l s

Ch ann els = Ma ke_C han ne ls ( l , Tx_1 , Rx_1 , RI S_CST , a_ On _po l1 , a_O n_ po l2 , ...

a_ O ff _ po l1 , a _ Of f _p o l2 , N um be rO f Su bF r eq ue nc i es , Ba n dw id th ) ;

%6 T r a i n i n g ph a s e

Ca lc ul at e_ Ch an ne ls _Wi th _O nO ff _A lg o (K, M, C ha nne ls , S am ple _I nde x , p ath , ...

x_ RI S_ coo rd )

As an example of using the QRIS platform, we pro-

vide a part of a code in Listing 1, where we create and

conﬁgure Tx, Rx, and RIS with CST-UCs, etc. First, we

create a linear polarized half-wave-dipole antenna for the

Tx entity. Second, we create an Rx entity similar to the ﬁrst

step. Variable sis QRIS parameters class responsible for

simulation options and constants for other classes. Third,

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Burtakov et al.: QRIS: a QuaDRiGa-Based Simulation Platform for Reconﬁgurable Intelligent Surfaces

0 50 100 150 200 250 300 350 400

M

17

18

19

20

21

22

23

24

SNR [dB]

No RIS

Optimal, COS-UC

ON-OFF, COS-UC

ON-OFF, CST-UC

Optimal, Q-UC

ON-OFF, Q-UC

Figure 12: SNR vs. the number of RIS elements for different

UCs and conﬁguration algorithms.

we create the RIS entity and UCs for ON-OFF states and

two polarizations. Fourth, we obtain the network layout l

of a simulation. Then, we calculate channel matrices for

our scenario. Finally, the RIS reﬂection matrix is optimized

according to the channel matrices calculated at the previous

stage. The other experiments are constructed similarly.

Figure 12 demonstrates the results of the described ex-

periment. In this experiment, the RIS equipped with Q-UCs

achieves much lower performance than estimated for COS-

UC or CST-UC because the amplitude diagram is weaker

for Q-UC.

We also evaluate the performance of the ON-OFF algo-

rithm described in Section V-B2. The training set for the

ON-OFF algorithm contains 500 random channel samples.

The ON-OFF algorithm achieves almost the same perfor-

mance as the Optimal one for COS-UC and Q-UC. However,

the implementation of the Optimal algorithm is much more

complex in practice [35].

In the second experiment, we vary the RIS position

xRIS and measure SNR for COS-UC, Q-UC, and CST-UC.

We use the ON-OFF algorithm to conﬁgure the RIS. We

consider PTx = 30 dBm, the noise power PN=−85

dBm. The coordinates of the Tx, Rx, and RIS are (0, 0,

2) m, (100, 0, 2) m, (xRIS, 10, 2) m respectively, where

xRIS ∈[−50; 150] m. The operating frequency is 5.3 GHz,

and the number of UCs M={256,400}. Tx and Rx are

equipped with one half-wave-dipole antenna each. Figure 13

demonstrates the results. SNR reaches its maximum values

for all types of UCs when RIS is close to the Tx and Rx

positions. This result coincides with the achieved in the

previous papers [19], [49] and can be explained by the fact

that since simpliﬁed Path Loss is proportional to the product

of the squares of the distances from Tx to RIS and from RIS

to Rx d2

TId2

IR, the receiver SNR improves when the RIS is

closer to the transmitter or the receiver. For a RIS equipped

with COS-UCs or Q-UCs, the maximum value of SNR value

is approximately the same on the left and right peaks with

xRIS = 0 m and xRIS = 100 m respectively. However, for

-50

-40

-30

-20

-10

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

xRIS [m]

17

18

19

20

21

22

23

24

SNR [dB]

ON-OFF, COS-UC, M = 256

ON-OFF, CST-UC, M = 256

ON-OFF, Q-UC, M = 256

ON-OFF, COS-UC, M = 400

ON-OFF, CST-UC, M = 400

ON-OFF, Q-UC, M = 400

No RIS

Figure 13: SNR vs. RIS position for different RIS UCs and

number of RIS elements.

a RIS with the CST-UC, the maximum SNR is achieved

when xRIS = 0 m. To explain this effect in detail, let us

focus on how CST-UC’s RCS and phase shifts depend on

the incidence angle. The area of high performance of CST-

UC in Fig. 13 corresponds to phase shift differences close to

180◦for the horizontal polarization, i.e., the incidence angle

φi∈(30◦,150◦), see Fig. 7. The phase shift difference

for the vertical polarization of the modeled CST-UC is

resilient to the changes in the incident angle. For the right

peak in Fig. 13, the angle of incidence is about 6◦, which

corresponds to a phase shift between ON and OFF states

of about 97◦for horizontal polarization which signiﬁcantly

reduces the efﬁciency of the RIS [34]. In addition, as the

RIS moves away from the Tx, the effective area of the UC

decreases, which in turn reduces the amount of energy that

the RIS can effectively re-radiate to the Rx [50].

Let us demonstrate how to use QRIS to estimate the

throughput of different RIS-assisted MIMO systems in var-

ious scenarios and propagation conditions. For example, we

evaluate MIMO 64×45G scenario band and a two-RIS Wi-

Fi scenario. In both scenarios, we consider the NLOS case,

where the RIS signiﬁcantly improves the system through-

put. To obtain statistically meaningful results, we average

the throughput over 500 channel realizations. We use the

ON-OFF algorithm and evaluate the performance of RIS

equipped with COS-UC, Q-UC, and CST-UC. As a baseline,

we consider the same communication system but without the

RIS. In this case, the most efﬁcient power distribution at Tx

is obtained with the water-ﬁlling algorithm.

Figure 14 shows the achievable rate with different Tx

power in the 3GPP 38.901 UMi outdoor scenario and the

3.6 GHz band. The Tx device represents a 5G base station

equipped with 64 cross-polarized 3gpp-macro antennas,

while the Rx device is a user equipment (UE) with four

cross-polarized half-wave-dipole antennas. The Tx, Rx, and

RIS coordinates are (0, 0, 25) m, (150, 0, 2) m, and (150,

5, 3) m. The subchannels (Tx, RIS) and (RIS, Rx) are LOS,

10 VOLUME 4, 2016

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Burtakov et al.: QRIS: a QuaDRiGa-Based Simulation Platform for Reconﬁgurable Intelligent Surfaces

while the subchannel (Tx, Rx) is blocked by obstacles, i.e., it

is NLOS. These results show that RIS signiﬁcantly improves

the performance of MIMO systems.

An important feature of the QRIS is the support of

scenarios with multiple RISs conﬁgured for both multi-

hop and single-hop communications. For example, let us

consider a communication system with two RISs where the

second RIS (labeled as RIS2) is deployed near the Rx in

addition to the ﬁrst RIS (labeled as RIS1) deployed near

the Tx. Respectively, besides Rand Tfor the preexisting

subchannels (RIS1, Rx) and (Tx, RIS1), we let ˜

Rand ˜

T

denote the subchannels (RIS2, Rx) and (Tx, RIS2) due to

the newly added RIS. Furthermore, there exists the inter-RIS

subchannel between RIS1 and RIS2, which is denoted by

U[51]. Thus, the effective subchannel between Tx and Rx

is the superimposition of the double-reﬂection link, the two

single-reﬂection links, and the direct link, which is given by

H=˜

R˜

ΦUΦT+˜

R˜

Φ˜

T+RΦT+H.(9)

The second experiment corresponds to an indoor Wi-Fi

network, where multiple RISs can provide a connection

between two devices even if the direct link is fully blocked,

see Fig. 15. Let the networking devices and two identical

RISs be located according to the coordinates (in meters)

shown in Fig. 15. The endpoint devices have four omni

antennas and use the central frequency of 5.3GHz. We use

only the Optimal algorithm for tuning COS-UC and Q-UC

in this scenario because the ON-OFF algorithm does not

apply to the conﬁguration of multiple RISs.

Both RISs have the same number of UCs. The propa-

gation condition of subchannels (Tx, RIS1), (RIS1, RIS2),

and (RIS2, Rx) is LOS, while all other subchannels are

unavailable because of obstacles. Compared with the ﬁrst

experiment, we see that the throughput of such a system

depends on the number of UCs much more strongly, and

enlarging the RIS size from 64 to 900 UCs may increase

the throughput dozens of times in the area of low transmit

power.

0 5 10 15 20 25 30 35 40

PTx [dBm]

0

10

20

30

40

50

60

70

75

Achievable Rate [bit/sec/Hz]

COS-UC, M = 64

COS-UC, M = 400

CST-UC, M = 64

CST-UC, M = 400

Q-UC, M = 64

Q-UC, M = 400

No RIS

Figure 14: Achievable rate of a RIS-assisted 5G 64 ×4

MIMO system.

𝑅𝐼𝑆2

𝑇𝑥

𝑅𝑥

𝒁

𝑿

𝒀

𝑅𝐼𝑆1

(0, 5, 2)

(20, 0, 3)(40, 15, 2)

𝑈

𝑇

෨

𝑅

(20,20, 3)

𝑅𝐼𝑆1, 𝑅𝑥 𝑏𝑙𝑜𝑐𝑘𝑎𝑔𝑒

𝑇𝑥, 𝑅𝐼𝑆2 𝑏𝑙𝑜𝑐𝑘𝑎𝑔𝑒

Figure 15: A Wi-Fi network with two identical RISs.

0 5 10 15 20 25 30

PTx [dBm]

0

5

10

15

20

25

Achievable Rate [bit/sec/Hz]

COS-UC, M = 64

COS-UC, M = 400

COS-UC, M = 900

Q-UC, M = 64

Q-UC, M = 400

Q-UC, M = 900

Figure 16: Achievable rate of a Wi-Fi network.

The given system-level simulation results show that the

designed model is ﬂexible enough to evaluate the perfor-

mance of RIS-assisted systems in a wide range of scenarios.

VII. CONCLUSION

In this paper, we present a QRIS simulation platform that

can be used to evaluate the performance of RIS-assisted

wireless communication systems. QRIS supports various ra-

diation patterns for RIS UCs and various geometric locations

with different numbers of UCs, operating frequencies, and

scenario conﬁgurations. A distinctive feature of QRIS is

the ability to consider the simultaneous usage of several

RISs and their interaction. Using QRIS we evaluate the

performance of RIS-assisted wireless systems with various

unit cells, including those modeled in the CST Microwave

Studio. Our next steps are related to the following directions.

First, we plan to integrate QRIS with a widely used

network simulator ns-3 to enable system-level analyses of

various wireless networks, e.g., 5G/6G and Wi-Fi 7.

Second, we are going to integrate the mutual coupling

effect into our platform and explore techniques for its

reduction.

Third, we are going to develop and implement a low-

complexity RIS conﬁguration algorithm for multi-RIS envi-

ronments.

VOLUME 4, 2016 11

content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3306954

Burtakov et al.: QRIS: a QuaDRiGa-Based Simulation Platform for Reconﬁgurable Intelligent Surfaces

ACKNOWLEDGMENT

The authors thank the anonymous reviewers for their very

useful comments. Thanks to them, the simulation platform

has been signiﬁcantly improved

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BURTAKOV ILYA is a student at School of Radio

Engineering and Computer Technology, Moscow

Institute of Physics and Technology. He received

his BS degree with honors in applied mathemat-

ics and physics from the Moscow Institute of

Physics and Technology in 2022. He is a junior

researcher at the Wireless Networks Lab, Institute

for Information Transmission Problems of the

Russian Academy of Sciences. His research inter-

ests include analysis and performance evaluation

of wireless local area and cellular networks.

ALEKSEY KUREEV (Student Member, IEEE)

received his BS degree and MS degree with hon-

ors in applied mathematics and physics from the

Moscow Institute of Physics and Technology in

2015 and 2017, respectively. He received the PhD

degree in telecommunications under the supervi-

sion of Evgeny Khorov. Currently, he is a senior

researcher at the Wireless Network Lab, Institute

for Information Transmission Problems of the

Russian Academy of Sciences and Telecommu-

nication Systems Lab of the Higher School of Economics. He authors

more than twenty research papers and patents. He supervises students and

lectures on the fundamentals of telecommunications and SDR prototyping.

His professional interests are related to testbeds, software-deﬁned radios,

massive machine-to-machine communication, and ultra-dense networks.

ANDREY TYARIN is a postgraduate student

at School of Radio Engineering and Computer

Technology, Moscow Institute of Physics and

Technology. He received his BS degree and MS

degree with honors in applied mathematics and

physics from the Moscow Institute of Physics and

Technology in 2019 and 2021, respectively. He is

a junior researcher at the Wireless Networks Lab,

Institute for Information Transmission Problems

of the Russian Academy of Sciences. His research

interests are related to prototyping, semiconductor devices, and modeling

of electromagnetic ﬁeld propagation.

EVGENY KHOROV (Ph.D.’12; D.Sc.’22; Senior

Member, IEEE) is the Head of the Wireless Net-

works Laboratory of the Institute for Information

Transmission Problems of the Russian Academy

of Sciences. Also, he is Associate Professor at

MIPT, NRU HSE, and MSU. His main research

interests are related to 5G/6G systems, next-

generation Wi-Fi, Wireless Internet of Things,

and QoS-aware optimization. He has led dozens

of academic and industrial projects. Being a vot-

ing member of IEEE 802.11, he contributed to the Wi-Fi 6 standard. He has

authored over 180 papers, which received several Best Paper Awards. Also,

he was awarded national and international prizes in science and technology.

Evgeny Khorov gives tutorials and participates in panels at large IEEE

events. He chaired the TPC of various IEEE and IETF conferences and

workshops. In 2020, he was awarded as the Editor of the Year of Ad Hoc

Networks.

VOLUME 4, 2016 13

content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3306954