# Wireless Communications Through Reconfigurable
Intelligent Surfaces

Ertugrul Basar, , Marco Di Renzo, , Julien de Rosny, Merouane Debbah, ,
Mohamed-Slim Alouini, , and Rui Zhang E. Basar is with Communications Research
and Innovation Laboratory (CoreLab), Department of Electrical and Electronics
Engineering, Koç University, Sariyer 34450, Istanbul, Turkey (e-mail:
ebasar@ku.edu.tr)M. Di Renzo is with Paris-Saclay University (L2S - CNRS,
CentraleSupélec, University Paris Sud), Paris, France. (e-mail:
marco.direnzo@l2s.centralesupelec.fr)J. de Rosny is with Institut Langevin,
Ecole Supérieure de Physique et de Chimie Industrielle, CNRS, 75005 Paris,
France. (e-mail: julien.derosny@espci.fr)M. Debbah is with CentraleSupelec,
Université Paris-Saclay, 91192 Gif-sur-Yvette, France and Mathematical &
Algorithmic Sciences Lab, Huawei Technologies France SASU, 92100 Boulogne-
Billancourt, France (e-mail: merouane.debbah@huawei.com)M.-S. Alouini is with
Computer, Electrical, and Mathematical Sciences and Engineering Division, King
Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
(e-mail: slim.alouini@kaust.edu.sa)R. Zhang is with Department of Electrical
and Computer Engineering, National University of Singapore, Singapore 117583
(email: elezhang@nus.edu.sg)The work of E. Basar is supported in part by the
Scientific and Technological Research Council of Turkey (TUBITAK) under Grant
117E869, the Turkish Academy of Sciences (TUBA) GEBIP Programme, and the
Science Academy BAGEP Programme.This paper has been presented in part at the
2019 European Conference on Networks and Communications (EuCNC), Valencia,
Spain, June 2019 [1].

###### Abstract

The future of mobile communications looks exciting with the potential new use
cases and challenging requirements of future 6th generation (6G) and beyond
wireless networks. Since the beginning of the modern era of wireless
communications, the propagation medium has been perceived as a randomly
behaving entity between the transmitter and the receiver, which degrades the
quality of the received signal due to the uncontrollable interactions of the
transmitted radio waves with the surrounding objects. The recent advent of
reconfigurable intelligent surfaces in wireless communications enables, on the
other hand, network operators to control the scattering, reflection, and
refraction characteristics of the radio waves, by overcoming the negative
effects of natural wireless propagation. Recent results have revealed that
reconfigurable intelligent surfaces can effectively control the wavefront,
e.g., the phase, amplitude, frequency, and even polarization, of the impinging
signals without the need of complex decoding, encoding, and radio frequency
processing operations. Motivated by the potential of this emerging technology,
the present article is aimed to provide the readers with a detailed overview
and historical perspective on state-of-the-art solutions, and to elaborate on
the fundamental differences with other technologies, the most important open
research issues to tackle, and the reasons why the use of reconfigurable
intelligent surfaces necessitates to rethink the communication-theoretic
models currently employed in wireless networks. This article also explores
theoretical performance limits of reconfigurable intelligent surface-assisted
communication systems using mathematical techniques and elaborates on the
potential use cases of intelligent surfaces in 6G and beyond wireless
networks.

###### Index Terms:

6G, large intelligent surfaces, meta-surfaces, reconfigurable intelligent
surfaces, smart reflect-arrays, software-defined surfaces, wireless
communications, wireless networks.

##  I Introduction

According to the February 2019 report of Cisco [2], by the year of 2022, the
number of networked devices and connections will reach up to $28.5$ billions,
and $12.3$ billions of them will consist of mobile-ready devices and
connections. Furthermore, the overall mobile data traffic is expected to grow
to $77$ exabytes per month by 2022 with a seven-fold increase over 2017.
Meanwhile, after years of research and development, the first commercial 5th
generation (5G) mobile communication standard (3GPP Release 15) has been
completed in June 2018. As of mid-2019, 5G wireless networks have been already
deployed in certain countries, while the first 5G compatible mobile devices
are being introduced to the market. The advent of 5G has led to a new vision
of mobile communications, which encompasses three use cases with different
requirements: enhanced mobile broadband, ultra-reliable and low-latency
communications, and massive machine type communications. However, one thing
has become certain during the standardization process of 5G wireless networks:
there is no single enabling technology that can support all 5G application
requirements. From this perspective, researchers have already started research
on beyond 5G, or even 6th generation (6G), technologies by escaping from the
comfort-zone of 5G-oriented solutions. Even though future 6G technologies seem
to be an extension of their 5G counterparts at present [3], as 5G technologies
were viewed 10 years ago, new user requirements, new applications and use
cases, and new networking trends will bring more challenging communication
engineering problems, which necessitate radically new communication paradigms,
especially at the physical layer.

During the past few years, there has been a growing interest in novel
communication paradigms in which the implicit randomness of the propagation
environment is exploited to either simplify the transceiver architecture
and/or to increase the quality of service (QoS). A notable example is spatial
modulation (SM) [4, 5, 6, 7], which is by far the most popular member of the
index modulation (IM) family [8, 9, 10]. SM maps information bits onto
transmit antenna indices by exploiting different fading realizations of
multiple-input multiple-output (MIMO) antennas. Taking SM one step further,
spatial scattering modulation [11] and beam IM [12] exploit the indices of the
scatterers available in the environment to convey information. Finally, media-
based modulation (MBM) utilizes reconfigurable antennas [13, 14, 15] by
encoding the information bits onto multiple distinguishable radiation patterns
[16, 17, 18]. In the aforementioned schemes, different signatures of the
received signals, which originate from the interaction of the transmitted
signals with the environment, are used to transmit information bits at a low
implementation complexity.

In the recent period, a brand-new technology was brought to the attention of
the wireless research community: reconfigurable intelligent surfaces (RISs).
The RISs are man-made surfaces of electromagnetic (EM) material that are
electronically controlled with integrated electronics and have unique wireless
communication capabilities. Current implementations include conventional
reflect-arrays, liquid crystal surfaces, and software-defined meta-surfaces
[19, 20, 21]. In contrast to any other technology currently being used in
wireless networks and current design principles of wireless communications,
the distinctive characteristic of RISs lies in making the environment
controllable by the telecommunication operators, and by giving them the
possibility of shaping and fully controlling the EM response of the
environmental objects that are distributed throughout the network [22]. The
RISs share similarities but have major differences compared with SM-based
systems: the RISs are, in fact, aimed to intentionally and deterministically
control the propagation environment in order to boost the signal quality at
the receiver [22, 23, 1].

The RISs have given rise to the emerging concept of “smart radio environments”
[23]. In contrast to current wireless networks where the environment is out of
control of the telecommunication operators, a smart radio environment is a
wireless network where the environment is turned into a smart reconfigurable
space that plays an active role in transferring and processing information.
Smart radio environments largely extend the notion of software networks.
Future wireless networks, in particular, are rapidly evolving towards a
software-based and reconfigurable platform, where every part of the network
will be capable of adapting itself to the changes in the environment [24]. In
this optimization process, however, the environment itself remains an
uncontrollable factor, i.e., it is unaware of the communication process
undergoing within it. Apart from being uncontrollable, the environment has
usually a negative effect on the communication efficiency and the QoS. The
signal attenuation limits the radio connectivity, multipath propagation
results in fading phenomena, and reflections and refractions from large
objects are the main sources of uncontrollable interference. In smart radio
environments, on the other hand, the wireless environment itself is turned
into a software-reconfigurable entity [25], whose operation is optimized to
enable uninterrupted connectivity, high QoS guarantee, and where the
information is transmitted without necessarily generating new signals but
recycling the existing ones whenever possible [23].

RIS-empowered smart radio environments are a brand-new technology that has the
potential of fundamentally changing how wireless networks are designed and
optimized today. Controlling the propagation of radio waves opens the
possibility of overcoming the negative effects of natural EM propagation,
which is highly probabilistic in nature, by shaping how the radio waves
interact with the surrounding objects that are coated with reconfigurable thin
layers of EM material. In simple terms, the RISs allow network planners to
counteract the destructive effect of multipath fading by coherently combining
the radio waves reflected, refracted, and scattered from large surfaces [1].
The core technology behind this promising concept is the meta-surfaces, which
is the 2D equivalent of meta-materials [22, 23].

It is worth noting that the RISs are different compared with other, and at the
first sight, related technologies currently employed in wireless networks,
such as relaying, MIMO beamforming, and backscatter communications. Details
will be provided in the sequel, but it suffices to say that the RISs have the
following distinguishable features:

  * •

They are nearly passive, and, ideally, they do not need any dedicated energy
source.

  * •

They are viewed as a contiguous surface, and, ideally, any point can shape the
wave impinging upon it (soft programming).

  * •

They are not affected by receiver noise, since, ideally, they do not need
analog-to-digital/digital-to-analog converters (ADCs and DACs), and power
amplifiers. As a result, they do not amplify nor introduce noise when
reflecting the signals and provide an inherently full-duplex transmission.

  * •

They have full-band response, since, ideally, they can work at any operating
frequency.

  * •

They can be easily deployed, e.g., on the facades of buildings, ceilings of
factories and indoor spaces, human clothing, etc.

These distinctive characteristics make RIS-assisted communication a unique
technology, but introduce important design challenges, which will be discussed
and elaborated in the sequel.

Although traditional meta-surfaces with fixed EM functionalities have been
used in various applications, including radar and satellite communications,
their application in mobile communications is relatively limited [26]. Passive
surfaces that do not have the ability to alter (or reconfigure) their EM
characteristics to control the propagation environment, in fact have a very
limited impact in highly dynamic wireless communication environments. For
application to wireless networks, it is fundamental that the meta-surfaces are
reconfigurable in order to adapt themselves according to the changes of the
wireless environment [19]. The modeling, analysis, and design of RISs for
application to wireless networks is a highly multidisciplinary research
endeavor at the intersection of wireless communications, communication theory,
computer science, physics, electromagnetism, and mathematics. Within this
context, we may need to revisit meta-materials from the perspective of
communication engineering by combining physical and digital domains.

The aim of the present article is to summarize the latest research activities
on RIS-empowered wireless networks, to elaborate on the fundamental
differences with other technologies, to discuss the most important open
research issues to tackle, and to highlight why the use of RISs necessitates
to rethink the communication-theoretic models currently employed in wireless
networks. We will describe, in particular, the potential use of RISs either
for sharping the radio waves or for realizing low-complexity MIMO
transmitters. Simple analytical models to convey the distinguishable features
of RISs will be used, with focus on link budget (path-loss) and error
performance.

The rest of the article is summarized as follows. We revisit the popular two-
ray model in wireless communications to illustrate the concept of controllable
wireless propagation in Section II. In Section III, we shed light on the basic
operation mechanisms of RISs. In Section IV, we provide a mathematical
framework for the calculation of the error performance of RIS-assisted
systems. RISs employed as low-complexity transmitters are introduced in
Section V. A detailed historical perspective and an overview of state-of-the-
art solutions on RISs are presented in Section VI. Potential use cases and
open research issues are discussed in Sections VII and VIII, respectively.
Finally, Section IX concludes the paper.

##  II Controllable Wireless Propagation Through Reconfigurable Intelligent
Surfaces – An Illustrative Example

In a typical wireless communication environment, a transmitted radio signal
encounters multiple objects on its way, which produce reflected, diffracted,
and scattered replicas of the transmit signal. These copies are called
multipath components, and arrive at the receiver with different (most probably
random and unpredictable) magnitudes, phases, and delays that produce
significant distortions on the received signal because of their constructive
and destructive summation. This effect is known as fading in wireless
communications and is a major limiting factor in modern and future wireless
communication systems. The main motivation of using RISs is to realize a
controllable radio environment, in which the highly probabilistic wireless
channel is turned into a deterministic space by carefully re-engineering the
propagation of the EM waves in a software-controlled fashion. In this section,
we illustrate the basic working mechanism of RISs by focusing on their use to
modify the signals reflected by large planar surfaces. We consider a simple
example that is based on revisiting the well-known two-ray channel model in a
free-space environment, but in the presence of an RIS deployed on the ground
plane.

Figure 1: Two-ray propagation model with a LOS ray and a ground-reflected ray.

###  II-A The Conventional Two-Ray System Model

In particular, we consider the two-ray channel model depicted in Fig. 1. In
this model, the received signal consists of two components: the line-of-sight
(LOS) ray and the ray reflected from the ground. Our system model and analysis
are based on a geometrical optics (or ray optics) model for the propagation of
radio waves [27]. Geometrical optics, or ray optics, is a model of optics that
describes the propagation of radio waves in terms of rays, and assumes that
the geometric size of the objects is much larger than the wavelength of the
radio wave. For ease of illustration, we assume that the ground plane is
sufficiently large compared with the transmission wavelength and that it
produces only specular reflections. Further information on the geometric size
of the RISs to behave as reflectors is given in further text. Under these
assumptions, the energy is regarded as being transported along certain curves,
i.e., the radio waves (the rays) are assumed to propagate in straight-line
paths if they travel in homogeneous media, and to bend and split in reflected
and refracted signals at the interface between two dissimilar media. In more
general terms, the propagation of radio waves modeled as rays adhere to the
Fermat’s principle, which states that the path taken by a ray between two
points is the path that is traversed in the least time.

In Fig. 1, the distance between the transmit and receive antennas is denoted
by $l$, and the distance between the point of reflection, G, on the ground and
the transmit and receive antennas are denoted by $r_{1}$ and $r_{2}$,
respectively. According to the geometrical optics and Fermat’s principle, the
point of reflection G corresponds to the trajectory that allows the
transmitted signal to reach the receiver in the least time. This is the well-
known Snell’s law of reflection. Accordingly, G is the point in which the
angle between the incident ray and the perpendicular line to the ground is the
same as the angle between the reflected ray and the perpendicular line to the
ground [28].

We denote the ground reflection coefficient by $R$, which typically depends on
the properties of the material of the ground, the polarization of the radio
wave, and the angle of incidence [29]. Without loss of generality, we assume
unit gain transmit/receive antennas and a narrow-band transmission signal,
i.e., $x(t)\approx x(t-\tau)$, where $x(t)$ is the complex baseband
transmitted signal and $\tau$ is the relative time delay between the ray
reflected from the ground and the LOS path, which is given by
$\tau=(r_{1}+r_{2}-l)/c$ with $c$ being the speed of light. Then, the received
(noise-free) baseband signal can be expressed as follows:

$r(t)=\frac{\lambda}{4\pi}\left(\frac{e^{-\frac{j2\pi
l}{\lambda}}}{l}+\frac{R\times
e^{-\frac{j2\pi(r_{1}+r_{2})}{\lambda}}}{r_{1}+r_{2}}\right)x(t)$ (1)

where $\lambda$ is the wavelength. In simple terms, the received signal is the
sum of the LOS and ground-reflected signals with phase delays $2\pi l/\lambda$
and $2\pi(r_{1}+r_{2})/\lambda$, respectively, which are proportional to the
propagation distances.

Assuming that the transmit power of $x(t)$ is $P_{t}$, the received power
$P_{r}$ can be expressed, from (1), in terms of $P_{t}$ as follows:

$P_{r}=P_{t}\left(\frac{\lambda}{4\pi}\right)^{2}\left|\frac{1}{l}+\frac{R\times
e^{-j\Delta\phi}}{r_{1}+r_{2}}\right|^{2}$ (2)

where $\Delta\phi=\frac{2\pi(r_{1}+r_{2}-l)}{\lambda}$ is the phase difference
between the two paths.

Assuming that the distance $d$ is large enough, i.e., $d\gg h_{t}+h_{r}$, then
we obtain $d\approx l\approx r_{1}+r_{2}$ and $R\approx-1$ for a specular
reflection from the ground [29, Eq. (2.15)]. Therefore, (2) simplifies as
follows:

$P_{r}\propto P_{t}\left(\frac{1}{d^{2}}\right)^{2}$ (3)

which decays with the fourth power of the distance $d$.

If the ground reflection is not present, i.e., the second term in (1) is equal
to zero, the LOS free-space propagation model yields a received signal power
that decays with the second power of the distance:

$P_{r}=P_{t}\left(\frac{\lambda}{4\pi d}\right)^{2}.$ (4)

Comparing (3) and (4), one can easily observe the destructive effect, on the
power of the received signal, that the uncontrollable reflection from the
ground generates because of the misaligned phases of the two paths shown in
Fig. 1. In other words, just a single uncontrollable reflection from the
ground may cause major degradations on the received signal strength, even in
the very optimistic transmission scenario with no user mobility and no random
effects induced by the environment.

###  II-B The Two-Ray System Model with a Single Reconfigurable Meta-Surface

Let us consider the same system model with the only exception that a
reconfigurable meta-surface is laid on the ground to assist the communications
between the transmitter and receiver. In particular, we assume the simple case
study where the meta-surface acts as a reflecting surface, which is capable of
modifying the direction of the reflected ray (i.e., the angle of reflection)
according to the generalized Snell’s law [28], as well as the phase of the
reflected ray as described in [30] and [31]. Further details on the operation
of reconfigurable meta-surfaces are provided in the next section. It suffices
to say that the angle and phase of the reflected ray can be modified by
engineering the phase gradient of the meta-surface [28, 30, 31]. Similar to
the reflection from the ground, the reflection coefficient usually depends on
the characteristics of the incident wave, e.g., the polarization of the
incident EM field, the material that the meta-surface is made of, and the
angles of incidence and reflection [30].

In the considered example, we focus our attention on the possibility of
optimizing the phase of the reflected ray and assume that no anomalous
reflection is needed, i.e., the Snell’s law applies. Also, we assume that the
entire ground is coated with a reconfigurable meta-surface. Conceptually, the
reconfigurable meta-surface can be viewed as an ideal phase shifter that is
capable of adjusting the phase of the reflected wave so that the LOS and
reflected rays sum up coherently, and the signal strength of their sum is
maximized. If we assume that the reconfigurable meta-surface is capable of
optimizing the phase of the reflected ray in an optimal fashion, i.e., by
coherently aligning the phases of the direct and the reflected rays for any
angles of incidence and reflection, we would obtain the following:

$P_{r}=P_{t}\left(\frac{\lambda}{4\pi}\right)^{2}\left|\frac{1}{l}+\frac{1}{r_{1}+r_{2}}\right|^{2}\approx
4P_{t}\left(\frac{\lambda}{4\pi d}\right)^{2}$ (5)

which corresponds to setting $R=e^{j\Delta\phi}$, and by considering $d\approx
l\approx r_{1}+r_{2}$.

By comparing (3) with (5), we evince that the use of reconfigurable meta-
surfaces has the potential of changing the scaling law that governs the
received power as a function of the distance: the received power does not
decay anymore with the forth power of the distance but only with the second
power of the distance, which is the same as the LOS ray. In further text, we
will show that this simple result is one of the main distinctive differences
of RISs with respect to relaying and backscatter communications.

Figure 2: Conceptual illustration of an RIS made of 20 reconfigurable meta-
surfaces whose phases ($\Phi_{i}$) can be tuned independently and whose
reflected rays are steered toward the same reflection angle. For ease of
representation, multiple incident rays are depicted, but only one incident ray
is present in practice.

###  II-C The Two-Ray System Model with An RIS Made of Many Reconfigurable
Meta-Surfaces

Let us now go one step further, and assume that the ground floor is not coated
with a single reconfigurable meta-surface but with an RIS that is made of $N$
reconfigurable meta-surfaces each of which capable of tuning the angle of
reflection according to the Snell’s law and the phase of the reflected ray
independently of the other meta-surfaces. A conceptual diagram of the
considered system model is sketched in Fig. 2. In the sequel, we will keep
using the following terminology: i) a reconfigurable (reflecting) meta-surface
is a surface that allows the angle and the phase of the reflected ray to be
arbitrarily controlled, and ii) an RIS is the collection of several
reconfigurable (reflecting) meta-surfaces that are capable of steering the
reflected ray towards the same angle and of adjusting the phase of the
reflected ray independently of the other reconfigurable meta-surfaces. Under
these assumptions, the received signal power can be written as follows:

$P_{r}=P_{t}\left(\frac{\lambda}{4\pi}\right)^{2}\left|\frac{1}{l}+\sum_{i=1}^{N}\frac{R_{i}\times
e^{-j\Delta\phi_{i}}}{r_{1,i}+r_{2,i}}\right|^{2}$ (6)

where the symbols have the same meaning as in (2), and the index $i$ is
referred to the $i$th reconfigurable meta-surface of the RIS.

From (6), we evince that the power of the received signal may fluctuate
significantly if the reflection coefficients of the $N$ reconfigurable meta-
surfaces are not optimized. Let us assume that each $R_{i}$ is optimized so
that the phase of the received signal from the $N$ reflecting meta-surfaces is
aligned with the phase of the LOS path, i.e., $R_{i}=e^{j\Delta\phi_{i}}$ and
$r_{1,i}+r_{2,i}\approx l\approx d$ for all $i$. Then, the received power can
be formulated as follows:

$P_{r}\approx(N+1)^{2}P_{t}\left(\frac{\lambda}{4\pi d}\right)^{2}.$ (7)

By direct inspection of (7), two major conclusions can be drawn: 1) the
received power is proportional to $N^{2}$, which is the number of phases of
the RIS that can be controlled independently, and 2) the received power decays
with the inverse of the square of the distance between the transmitter and the
receiver. In other words, as a function of the distance, the received power
decays as for the LOS path, while a power gain that is proportional to the
square of the number of controllable phases is obtained. This shows the
potential of using the RISs in wireless networks. It is worth mentioning that
this illustrative example is relatively simple and relies on a number of
assumptions, e.g., the possibility of optimizing the reflection phases without
any discretization error and for any angles of incidence and reflection, the
absence of reflection losses, and the perfect knowledge of the phases at the
RIS. In addition, the optimization of the phases is usually not a
straightforward task for more practical system models, e.g., [32].

###  II-D On the Geometric Size of an RIS to be a Specular Reflector

From (7), it is worth investigating the size that an RIS is expected to have
as a function of the number of reflecting meta-surfaces that it is made of,
i.e., $N$. As a reference for this study, we can consider the samples of meta-
surfaces available in [33, 34, 35, 36]. Based on, e.g., [33], we can assume
that a meta-surface that is capable of shaping the angle and the phase of the
reflected signal has a size of the order of $10\lambda\times 10\lambda$. This
size allows, in general, a meta-surface to be viewed as a specular reflector
according to geometrical optics. If we assume that an RIS is made of $N=100$
reconfigurable meta-surfaces whose reflection phase can be controlled
independently of the others, then the size of the RIS would be of the order of
$100\lambda\times 100\lambda$. If the operating frequency is of the order of
$30$ GHz, e.g., for application in the millimeter-wave frequency band where
the RISs may have promising applications for enhancing the coverage in
cellular networks as discussed in the sequel, then we have $\lambda\approx 1$
cm, which results in an RIS of 1 m2. A structure of this type can be readily
deployed either in indoors or outdoors, and yields, according to (7), the same
power decay as a function of the distance as the LOS path, but a $\sim
N^{2}=100^{2}=10^{4}=40$ dB increase of the received power. Even though, as
just mentioned, these results are obtained under a number of “comfortable”
assumptions, the potential gain is sufficiently large to motivate further
research on the potential and limitations of using the RISs in wireless
networks.

###  II-E Intelligent Reflection vs. Relaying and Backscattering: Reflectors
vs. Diffusers

We close this section, by elaborating on the main difference that renders the
concept of RISs a unique and peculiar technology when compared to, at the
first sight, similar technologies. The two technologies that are often deemed
to be equivalent to the RISs are relay-aided transmission [37] and backscatter
communications [38]. As far as the relays are concerned, in particular, the
most similar approach is the amplify-and-forward (AF) or transparent relaying
scheme [37, Sec. 2.4]. From [37, Eq. (2.118)] and [38, Eqs. (2)-(4)], we
evince that the received power decays with the fourth power of the distance
for transparent relaying and backscatter communications. By ignoring the LOS
path, in particular, the received power can be formulated as follows:

$P_{r}\propto
P_{t}\left(\frac{1}{r_{1}}\right)^{2}\left(\frac{1}{r_{2}}\right)^{2}$ (8)

where $r_{1}$ and $r_{2}$ stand for the distances between the relay and the
terminals. By assuming $r_{1}\approx r_{2}\approx d/2$, i.e., the relay is
placed mid-way between the transmitter and the receiver, we obtain
$P_{r}\propto P_{t}/d^{4}$ as reported in [37, Eq. (2.118)] and [38, Eqs.
(2)-(4)]. This is the well-known “product channel” or radar-like equation that
is usually used for analyzing relaying and backscatter communications. Based
on the above product channel model, a comprehensive overview of RIS-assisted
wireless networks can be found in [26].

By direct inspection of (7), we note that the scaling law of the received
power as a function of the distance of the RISs is in sharp contrast with the
scaling law that dictates the path-loss of relay-aided and backscatter
communications. This is a simple but net evidence of the difference between
the RISs and the two most similar approaches currently available in the
literature. The motivation of the different scaling law lies in the geometric
size of the RISs, the geometric size of the antennas that constitute the
relays (even if arrays of antennas are employed), and the geometric size of
the tags used for backscattering. The geometric size of the RISs is large
enough, i.e., much larger than the wavelength, to be modeled as specular
reflectors. The geometric size of the antennas of relays and backscattering
tags is, on the other hand, smaller than or comparable with the wavelength,
which render AF relaying and backscatter tags diffusers rather than specular
reflectors. The different path-loss function of the RISs is due, in addition,
to their passive nature, i.e., they are supposed not to store and process the
impinging signals. The geometric size of the RISs, if appropriately optimized,
may yield remarkable link budget gains in comparison with relaying and
backscatter communications. It is worth mentioning, in addition, that a
perfect RIS, i.e., with infinite size and no reflection losses, in the
presence of transmitters and receivers with omni-directional antennas is
capable of focusing towards the receiver half of the transmit power regardless
of the distance.

Finally, we emphasize that the path-loss model in (1) is widely used in ray
tracing in order to model specular reflections, e.g., [39]. The difference
between the specular reflections in (1) and the diffusely scattered field in
(8) is briefly discussed in [40]. The path-loss model in (1) was recently
employed in [41] to analyze the performance of RISs for application to
millimeter-wave communications.

##  III Reconfigurable Intelligent Surfaces: How Do They Work?

The RISs are reconfigurable sheets of EM material that intentionally control
the propagation in the environment in order to enhance the signal quality at
the receiver. The RISs are made of a large number of low-cost and passive
elements that are capable of modifying the radio waves impinging upon them in
ways that naturally occurring materials are not capable of. A simple example
where the RIS is made of meta-surfaces that act as programmable reflectors is
depicted in Fig. 2. Unlike other similar technologies, e.g., relays and MIMO
beamforming, the RISs do not require any power source and complex processing,
encoding, and decoding algorithms. It is worth mentioning that the RISs are
often referred to as software-defined surfaces (SDSs) in analogy with the
concept of software-defined radio (SDR), i.e., “a radio in which some or all
of physical layer functions are software defined”. According to this
terminology, an RIS can be viewed as an SDS whose response of the radio waves
is programmed in software. In this section, we describe the operating
principle of the RISs in simple but general terms.

In [42], the authors introduced intelligent walls that are equipped with
frequency-selective surfaces. These surfaces have a planar structure and PIN
diodes are embedded on the metal connection parts of each surface element.
These PIN diodes are switched on and off by an external bias and provide two
different states for the intelligent wall. In the first state (when the PIN
diodes are off), an almost transparent surface, which allows the incoming
energy to pass through, is obtained. When the PIN diodes are switched to the
second state, on the other hand, the majority of the incident energy is
reflected. In other words, two important EM functionalities (the waves either
pass through or are reflected from the surface) are realized by an intelligent
wall. In Fig. 3, the structure of this intelligent wall is illustrated.

Figure 3: The structure of an RIS (intelligent wall) that is made of an active
frequency-selective surface.

In [43], the authors fabricated a $0.4$ m2 spatial microwave modulator that
consists of $102$ controllable EM reflectors, and operates at a working
frequency of $2.47$ GHz. These $102$ reflectors are controlled by using two
Arduino $54$-channel digital controllers. The authors demonstrated that
spatial microwave modulators can efficiently shape, in a passive way, complex
existing microwave fields in reverberating environments with a non-coherent
energy feedback. In particular, the authors showed that binary-only phase
state tunable meta-surfaces allow one to obtain a good control of the waves,
owing to the random nature of the EM fields in complex media. Subsequent
research works from the same group of researchers can be found in [44, 45].

Another approach for obtaining reconfigurable and smart reflect-arrays is to
use varactor-tuned resonators [46], as shown in Fig. 4. The idea of this
implementation is to change the resonant frequency of the available patches by
electronic tuning instead of changing the resonator dimensions as done in non-
reconfigurable reflect-arrays. In this setup, a tunable capacitor (varactor)
is used in each reflector unit and a tunable phase shift is obtained by
adjusting the bias voltage applied to the varactor in order to change its
capacity. Using this approach, a smart reflector with $48$ patch elements is
constructed in [47]. In particular, the EM response of the patch elements can
be altered by using micro-controllers, which generate input signals to tune
the varactors and to change the phase of the reflected signal. A more advanced
reflect-array with $224$ reconfigurable patches is designed in [48] for
application to $60$ GHz WiFi signals, which is made of electronically-
controlled relay switches. In this implementation, each reflector can be
turned on and off according to the status of its switch. A beam searching-
based reflect-array control algorithm is introduced as well, where the access
point (AP) and the reflect-array perform beam searching to ensure a maximized
signal quality at the intended user. Due to hardware limitations, however, a
binary phase control (two possible phases) is considered, which causes a
degradation of the received signal-to-noise ratio (SNR).

Figure 4: Reconfigurable reflect-arrays with tunable resonators.

The concept of HyperSurfaces is based on coating objects, such as walls or
furniture, with thin sheets of EM material that enable one to control the EM
behavior of a wireless environment through software [22]. The HyperSurfaces
belong to the family of software-controlled meta-surfaces. Depending on the
states of electronic switches that are embedded throughout the meta-surface,
the distribution of the current can be controlled, which enables the meta-
surface to adapt its response depending on the impinging radio wave and on the
desired response. From this perspective, one can easily notice the conceptual
similarity between this implementation of a meta-surface and reconfigurable
antennas, in which the resulting radiation pattern is altered by changing the
current distribution. In [22], the constituent meta-surfaces depicted in Fig.
2 are referred to as “tiles”, which are rectangular structures that can
realize functions such as wave steering, wave polarization, and wave
absorption, in a software-defined fashion. In simple terms, a HyperSurface
tile supports various software-defined EM functions, which can be programmed
in software by setting the direction of the incident wave, the direction of
the intended reflection, and the frequency band of interest, etc.

Liquid-crystal reconfigurable meta-surface-based reflectors are proposed in
[49] by exploiting electronically tunable liquid crystals to enable the real-
time reconfigurability of the meta-surfaces for beam steering. By varying DC
voltages on microstrip patches of liquid crystal loaded unit cells, the
effective dielectric constant of each unit can be adjusted. Consequently, the
phase shifts at various locations of a meta-surface can be controlled in real-
time and the reflected wave can be manipulated.

##  IV Controlling the Multipath Through Reconfigurable Intelligent Surfaces

In this section, we present the system model of a generic RIS-based single-
input single-output (SISO) scheme and introduce a unified framework for the
calculation of the symbol error probability (SEP) through the derivation of
the received SNR distribution. The block diagram of the considered RIS-based
transmission scheme is shown in Fig. 5, where $h_{i}$ and $g_{i}$ are the
fading channels between the single-antenna source (S) and the RIS, and between
the RIS and the single-antenna destination (D) for the $i$th reflecting meta-
surface $(i=1,2,\ldots,N)$, and $N$ is the number of reflecting meta-surfaces
of the RIS. Under the assumption of Rayleigh fading channels, we have
$h_{i},g_{i}\sim\mathcal{CN}(0,1)$, where $\mathcal{CN}(0,\sigma^{2})$ stands
for complex Gaussian distribution with zero mean and $\sigma^{2}$ variance.
For clarity, we emphasize that, as usual practice, the path-loss is not
considered in the fading coefficients $h_{i}$ and $g_{i}$, since it is
implicitly taken into account in the (receiver) SNR that is defined in further
text. Therefore the structure of the RIS is similar to that depicted in Fig.
2, and we assume that it provides adjustable phase shifts that are controlled
by and programmed through a communication-oriented software. In our analysis,
we assume perfect knowledge of the channel phases of $h_{i}$ and $g_{i}$ for
$i=1,2,\ldots,N$ at the RIS, which corresponds to the best scenario in terms
of system operation and yields a performance benchmark for practical
applications.

Figure 5: Reflection from an RIS in a dual-hop communication scenario without
a line-of-sight path between S and D.

Let us assume a slowly varying and flat fading channel model. The received
signal reflected by an RIS that is made of $N$ reflecting meta-surfaces can be
expressed as follows:

$r=\left[\sum_{i=1}^{N}h_{i}e^{j\phi_{i}}g_{i}\right]x+n$ (9)

where $\phi_{i}$ is the adjustable phase induced by the $i$th reflecting meta-
surface of the RIS, $x$ stands for the data symbol selected from an $M$-ary
phase shift keying/quadrature amplitude modulation (PSK/QAM) constellation and
$n\sim\mathcal{CN}(0,N_{0})$ is the additive white Gaussian noise (AWGN)
sample. As far as the channels are concerned, we have
$h_{i}=\alpha_{i}e^{-j\theta_{i}}$ and $g_{i}=\beta_{i}e^{-j\psi_{i}}$. In
matrix form, (9) can be also re-written as follows:

$r=\mathbf{g}^{\mathrm{T}}\Phi\mathbf{h}x+n$ (10)

where
$\mathbf{h}=\begin{bmatrix}h_{1}&h_{2}&\ldots&h_{N}\end{bmatrix}^{\mathrm{T}}$
and
$\mathbf{g}=\begin{bmatrix}g_{1}&g_{2}&\ldots&g_{N}\end{bmatrix}^{\mathrm{T}}$
represent the vectors of channel coefficients between the two terminals and
the RIS, and
$\Phi=\mathrm{diag}(\left[e^{j\phi_{1}}\,\,e^{j\phi_{2}}\,\,\ldots\,\,e^{j\phi_{N}}\right])$
is a diagonal matrix that contains the phase shifts applied by the reflecting
meta-surfaces of the RIS. By direct inspection of (10), the obtained model
resembles that of a precoding/beamforming system in conventional MIMO systems.
However, the precoding/beamforming operation is realized over the transmission
medium (the environment) rather than at the transmitter or at the receiver. As
far as the fast fading is concerned, we note, in particular, that the channel
model for the RISs and the relays are similar, while their path-loss models
are different if the RISs are sufficiently large to behave as reflectors.

From (9), the instantaneous SNR at D can be formulated as follows:

$\gamma=\frac{\left|\sum_{i=1}^{N}\alpha_{i}\beta_{i}e^{j(\phi_{i}-\theta_{i}-\psi_{i})}\right|^{2}E_{s}}{N_{0}}$
(11)

where $E_{s}$ is the average transmitted energy per symbol. It is not
difficult to infer that $\gamma$ is maximized by eliminating the channel
phases (similar to co-phasing in classical maximum ratio combining schemes),
i.e., the optimal choice of $\phi_{i}$ that maximizes the instantaneous SNR is
$\phi_{i}=\theta_{i}+\psi_{i}$ for $i=1,\ldots,N$. This solution, notably,
requires that the channel phases are known to the RIS. How to perform channel
estimation in RIS-empowered wireless networks along with the challenges that
this entails if the RISs are assumed to be passive, as opposed to, e.g., the
relays, is discussed in the sequel. In detail, the optimal choice of the
phases, $\phi_{i}=\theta_{i}+\psi_{i}$ for $i=1,\ldots,N$, is obtained from
the identity:

$\left|\sum_{i=1}^{N}z_{i}e^{j\xi_{i}}\right|^{2}=\sum_{i=1}^{N}z_{i}^{2}+2\sum_{i=1}^{N}\sum_{k=i+1}^{N}z_{i}z_{k}\cos(\xi_{i}-\xi_{k})$
(12)

which is maximized if $\xi_{i}=\xi$ for all $i$.

Therefore, the maximized SNR can be formulated as follows:

$\gamma=\frac{\left(\sum_{i=1}^{N}\alpha_{i}\beta_{i}\right)^{2}E_{s}}{N_{0}}=\frac{A^{2}E_{s}}{N_{0}}.$
(13)

Since $\alpha_{i}$ and $\beta_{i}$ are independently Rayleigh distributed
random variables (RVs), the mean value and the variance of their product are
$\mathrm{E}[\alpha_{i}\beta_{i}]=\frac{\pi}{4}$ and
$\mathrm{VAR}[\alpha_{i}\beta_{i}]=1-\frac{\pi^{2}}{16}$, respectively. For a
sufficiently large number of reflecting meta-surfaces, i.e., $N\gg 1$,
according to the central limit theorem (CLT), $A$ converges to a Gaussian
distributed random variable with parameters $\mathrm{E}[A]=\frac{N\pi}{4}$ and
$\mathrm{VAR}[A]=N\left(1-\frac{\pi^{2}}{16}\right)$. Therefore, $\gamma$ is a
non-central chi-square random variable with one degree of freedom and has the
following moment generating function (MGF) [50]:

$M_{\gamma}(s)=\left(\dfrac{1}{1-\frac{sN(16-\pi^{2})E_{s}}{8N_{0}}}\right)^{\frac{1}{2}}\\!\\!\exp\left(\dfrac{\frac{sN^{2}\pi^{2}E_{s}}{16N_{0}}}{1-\frac{sN(16-\pi^{2})E_{s}}{8N_{0}}}\right).$
(14)

Furthermore, the average received SNR can be written as follows:

$\mathrm{E}\left[\gamma\right]=\frac{(N^{2}\pi^{2}+N(16-\pi^{2}))E_{s}}{16N_{0}}$
(15)

which is proportional to $N^{2}$, i.e., $\mathrm{E}\left[\gamma\right]\propto
N^{2}\frac{E_{s}}{N_{0}}$, if $N\gg 1$. This result is in agreement with the
received signal power in (7).

From (14), we can compute the average SEP of $M$-ary phase shift keying (PSK)
signaling as follows [51]:

$P_{e}=\frac{1}{\pi}\int_{0}^{(M-1)\pi/M}M_{\gamma}\left(\frac{-\sin^{2}(\pi/M)}{\sin^{2}\\!\eta}\right)d\eta$
(16)

which for binary PSK (BPSK) simplifies to

$\displaystyle P_{e}=\frac{1}{\pi}\\!\int_{0}^{\pi/2}\\!\\!$
$\displaystyle\left(\dfrac{1}{1+\frac{N(16-\pi^{2})E_{s}}{8\sin^{2}\\!\eta
N_{0}}}\right)^{\\!\frac{1}{2}}$
$\displaystyle\times\exp\left(\dfrac{-\frac{N^{2}\pi^{2}E_{s}}{16\sin^{2}\\!\eta
N_{0}}}{1+\frac{N(16-\pi^{2})E_{s}}{8\sin^{2}\\!\eta
N_{0}}}\right)\\!\\!d\eta.$ (17)

In order to gain further insights, (IV) can be upper bounded by setting
$\eta=\pi/2$, which yields:

$P_{e}\leq\frac{1}{2}\left(\dfrac{1}{1+\frac{N(16-\pi^{2})E_{s}}{8N_{0}}}\right)^{\\!\frac{1}{2}}\\!\\!\\!\exp\left(\dfrac{-\frac{N^{2}\pi^{2}E_{s}}{16N_{0}}}{1+\frac{N(16-\pi^{2})E_{s}}{8N_{0}}}\right).$
(18)

Figure 6: Theoretical average SEP of RIS-based scheme for $N=16$ and $N=32$
with BPSK.

In Fig. 6, we plot the average SEP of the considered RIS-based scheme by using
(IV) and (18) for $N=16$ and $N=32$. From Fig. 6, we observe that an RIS-based
scheme achieves significantly better SEP performance compared with the
classical BPSK scheme operating in an AWGN channel. In other words, an RIS is
capable of converting a hostile wireless environment into a reliable
communication channel that provides one with a low SEP for low values of the
SNR, which outperforms AWGN channels. Let us analyze this behavior in detail.

By observing Fig. 6, we note that the average SEP has a waterfall region and a
slowly-decaying region. For $\frac{NE_{s}}{N_{0}}\ll 10$, in particular, (18)
unveils that $P_{e}$ is proportional to:

$P_{e}\propto\exp\left(-\frac{N^{2}\pi^{2}E_{s}}{16N_{0}}\right).$ (19)

This explains the superior performance of an RIS-based scheme. In this range,
notably, even though the SNR $E_{s}/N_{0}$ is relatively low, the average SEP
is quite low. This is due to the $N^{2}$ term inside the exponential function.

If $\frac{NE_{s}}{N_{0}}\gg 1$, on the other hand, (18) can be approximated as
follows111It is worth noting that $NE_{s}/N_{0}$ is measured in linear scale
and the given two critical points have been determined based on several
calculations for the considered values of $N$.:

$\displaystyle P_{e}$
$\displaystyle\propto\left(\frac{N(16-\pi^{2})E_{s}}{8N_{0}}\right)^{-\frac{1}{2}}\exp\left(-\frac{N\pi^{2}}{2(16-\pi^{2})}\right)$
(20)

which explains the slowly-decaying behavior of the SEP for high values of the
SNR. In particular this slowly-decaying region is caused by the scaling factor
in front of the exponential function, which decays with the negative square
root of the SNR. However, the SEP still decays exponentially as a function of
$N$, and, therefore, $P_{e}$ can be reduced by appropriately increasing $N$.

Figure 7: Simulated average SER performance (BPSK) of an RIS-based scheme with
a varying number of reflecting meta-surfaces - Comparison with the theoretical
formula in (IV).

In Fig. 7, we depict the average SEP of an RIS-based scheme for different
numbers of reflecting meta-surfaces $N$ and by assuming BPSK signaling. Fig. 7
confirms that our theoretical approximation in (IV) that is based on the CLT
is sufficiently accurate for large values of $N$. In the considered setup, our
proposed approximation is accurate for $N\geq 32$. In particular, we note that
doubling $N$ yields approximately $6$ dB improvement (four-fold decrease) of
the required SNR in the waterfall region. This can be readily verified from
(19).

Wit the aid of the MGF of the received SNR, $M_{\gamma}(s)$, we can obtain the
average SEP for square $M$-QAM constellations as well [51]:

$\displaystyle P_{e}=\,\,$
$\displaystyle\,\frac{4}{\pi}\left(1-\frac{1}{\sqrt{M}}\right)\int_{0}^{\pi/2}M_{\gamma}\left(\frac{-3}{2(M-1)\sin^{2}\\!\eta}\right)d\eta$
$\displaystyle-\frac{4}{\pi}\left(1-\frac{1}{\sqrt{M}}\right)^{2}\int_{0}^{\pi/4}M_{\gamma}\left(\frac{-3}{2(M-1)\sin^{2}\\!\eta}\right)d\eta.$
(21)

This integral can be upper bounded by letting $\eta=\pi/2$ and $\eta=\pi/4$ in
its first and second terms, respectively. By assuming $\frac{NE_{s}}{N_{0}}\ll
10$, which is the SNR region of interest, the average SEP can be expressed as
follows:

$P_{e}\propto\exp\left(-\frac{3N^{2}\pi^{2}E_{s}}{32(M-1)N_{0}}\right)$ (22)

where the second exponential term coming from (IV) is ignored since its
contribution is negligible. Since $M$ appears in the exponent of (22), an RIS-
based scheme experiences a degradation of the error performance if the
modulation order is increased, as in conventional modulation schemes. However,
RISs can take advantage of large values of $N^{2}$ to counteract the
detrimental effect of increasing the modulation order. This can be a remedy
for increasing the energy efficiency of future extreme mobile broadband
applications of 6G and beyond, which will rely on high-order constellations to
support extremely high data rates.

##  V Reconfigurable Intelligent Surface as a Low-Complexity and Energy-
Efficient Transmitter

In this section, we discuss the potential of using RISs as a technology that
enables low-complexity and energy-efficient implementations of MIMO
transmitters. The basic idea consists of illuminating an RIS with a feeder
(antenna), and of encoding the data to transmit onto the phases of the signals
reflected from the different reconfigurable meta-surfaces that realize the
RIS. If the RIS is made of $N$ reconfigurable meta-surfaces whose reflection
phase can be optimized independently of the others, then a $N$-stream virtual
MIMO system can be realized by using a single RF active chain [1]. A similar
solution (even though not based on reconfigurable meta-surfaces) is the
concept of symbiotic radio, where a backscatter device modulates its own
information over an incident signal from a transmitter by varying its
reflection coefficient [52]. This solution is similar to distributed SM
applied to relay-aided systems [53].

Recently, the idea of using an RIS as a transmitter was validated with the aid
of a testbed platform. In [54], in particular, the authors have realized a
$8$-PSK transmitter that utilizes a programmable surface with $256$
reconfigurable elements. By changing the bias voltage of varactor diodes, a
high phase modulation resolution is obtained. The authors showed that an
unmodulated carrier can be modulated by the reconfigurable meta-surface
through a series of DACs that control bias voltages. In [55], the same authors
have realized a virtual quadrature phase shift keying (QPSK) constellation
based on the same principle and by using a smaller reconfigurable meta-surface
that is made of $128$ reconfigurable elements. In [56], the idea of joint
passive beamforming and data transmission is considered in the context of an
RIS-assisted uplink transmission scheme. In this scenario, the authors
considered the communication of a multi-antenna base station (BS) with a
single-antenna user, where the on/off states of the RIS elements convey
additional data. These results substantiate the potential of RISs for
realizing low-complexity MIMO transmitters with a large number of equivalent
radiating elements, but a few, even a single, RF chain.

In this section, we analyze the error performance of RISs when utilized as
transmitters. For simplicity, as single-stream transmitter is considered. The
block diagram of the considered RIS-based concept is shown in Fig. 8. The RIS
is illuminated by a nearby RF signal generator or contains an attachment that
transmits an unmodulated carrier signal $\cos(2\pi f_{c}t)$ at a certain
carrier frequency $f_{c}$ towards the RIS. The unmodulated carrier can be
generated by an RF DAC with an internal memory and a power amplifier [57], and
information bits are conveyed only through the reflection-induced phases of
the RIS. We assume that the RF source is close enough to the RIS that its
transmission is not affected by fading. On the other hand, the channel between
the $i$th reflector of the RIS and D is modeled as
$g_{i}=\beta_{i}e^{-j\psi_{i}}$.

Figure 8: Using an RIS as a transmitter.

In the considered communication scenario, RIS-induced phases carry information
in addition to perform intelligent reflections that improve the received SNR.
In other words, the RIS adjusts the phases of its reflecting elements with the
aim of optimizing the reflected phases that maximize the received SNR, and, at
the same time, that properly align the reflected signals so as to create a
virtual bi-dimensional $M$-ary signal constellation diagram. For this system
model, the received signal is expressed as follows:

$r=\sqrt{E_{s}}\left[\sum_{i=1}^{N}g_{i}e^{j\phi_{i}}\right]+n$ (23)

where $E_{s}$ is the average transmitted signal energy of the unmodulated
carrier and $\phi_{i}$ is the reconfigurable phase induced by the $i$th
reflecting element of the RIS.

We assume that $\log_{2}(M)$ bits are transmitted in each signaling interval
by adjusting the reflection phases so that the equality
$\phi_{i}=\psi_{i}+w_{m}$ is fulfilled, where
$w_{m},m\in\left\\{1,2,\ldots,M\right\\}$, is the message-dependent phase term
introduced by the RIS that carries the information of the $m$th message. In
light of this, the received signal can be expressed as follows:

$r=\sqrt{E_{s}}\left[\sum_{i=1}^{N}\beta_{i}\right]e^{jw_{m}}+n=\sqrt{E_{s}}Be^{jw_{m}}+n.$
(24)

It is worth noting that the equivalent received signal resembles a PSK-
modulated signal that is transmitted through a channel whose gain is $B$.
Consequently, to minimize the average SEP, the information phases
$w_{1},w_{2},$ $\ldots,w_{M}$ of this $M$-ary signaling scheme can be selected
as in the classical $M$-PSK scheme, i.e., $w_{m}=2\pi(m-1)/M$ for
$m=1,2,\ldots,M$.

Therefore, the instantaneous received SNR can be written as follows:

$\gamma=\frac{E_{s}B^{2}}{N_{0}}.$ (25)

By using again the CLT for large values of $N$ and by assuming that
$\beta_{i}$ is a Rayleigh-distributed random variable with mean $\sqrt{\pi}/2$
and variance $(4-\pi)/4$, we obtain $B\sim\mathcal{N}(m_{B},\sigma_{B}^{2})$,
where $m_{B}=N\sqrt{\pi}/2$ and $\sigma_{B}^{2}=N(4-\pi)/4$. Consequently, the
MGF of $\gamma$, which is a non-central chi-square random variable, is the
following:

$M_{\gamma}(s)=\left(\dfrac{1}{1-\frac{sN(4-\pi)E_{s}}{2N_{0}}}\right)^{\\!\frac{1}{2}}\\!\\!\exp\left(\dfrac{\frac{sN^{2}\pi
E_{s}}{4N_{0}}}{1-\frac{sN(4-\pi)E_{s}}{2N_{0}}}\right).$ (26)

The average SEP of the proposed scheme can be calculated by substituting the
obtained MGF in the SEP formula of $M$-PSK signaling given in (16). In
particular, for binary signaling $(w_{1}=0$ and $w_{2}=\pi)$, we obtain the
following:

$P_{e}=\frac{1}{\pi}\\!\int_{0}^{\pi/2}\\!\\!\left(\dfrac{1}{1+\frac{N(4-\pi)E_{s}}{2\sin^{2}\\!\eta
N_{0}}}\right)^{\\!\frac{1}{2}}\\!\\!\\!\exp\left(\dfrac{-\frac{N^{2}\pi
E_{s}}{4\sin^{2}\\!\eta N_{0}}}{1+\frac{N(4-\pi)E_{s}}{2\sin^{2}\\!\eta
N_{0}}}\right)\\!\\!d\eta.$ (27)

By letting $\eta=\pi/2$ and considering the SNR range of interest
$\frac{NE_{s}}{N_{0}}\ll 10$, $P_{e}$ becomes proportional to:

$P_{e}\propto\exp\left(-\frac{N^{2}\pi E_{s}}{4N_{0}}\right).$ (28)

Two main conclusions can be drawn from (28). If the RIS is used as a
transmitter, it can convey information with high reliability, similar to using
the RIS as a reflector (see Fig. 5). In addition, by comparing (19) and (28),
$1$ dB gain in the SNR is obtained by an RIS used as a transmitter with
respect to using it as a reflector.

If $M$-ary signaling is considered, we can plug (26) in (16) and obtain the
average SEP in the form of the definite integral as follows:

$\displaystyle P_{e}=\frac{1}{\pi}$
$\displaystyle\\!\int_{0}^{(M-1)\pi/M}\\!\\!\left(\dfrac{1}{1+\frac{N(4-\pi)\sin^{2}(\pi/M)E_{s}}{2\sin^{2}\\!\eta
N_{0}}}\right)^{\\!\frac{1}{2}}$
$\displaystyle\times\exp\left(\dfrac{-\frac{N^{2}\pi\sin^{2}(\pi/M)E_{s}}{4\sin^{2}\\!\eta
N_{0}}}{1+\frac{N(4-\pi)\sin^{2}(\pi/M)E_{s}}{2\sin^{2}\\!\eta
N_{0}}}\right)\\!\\!d\eta.$ (29)

By letting $\eta=\pi/2$, an upper-bound can be obtained. By considering the
SNR range of interest, we obtain:

$P_{e}\propto\exp\left(-\sin^{2}(\pi/M)\frac{N^{2}\pi E_{s}}{4N_{0}}\right).$
(30)

Comparing the obtained result with (22), we conclude that an SNR loss is
expected for higher order signaling $M\geq 16$. However, this loss may not be
significant by taking into account that, in the relatively low SNR range of
interest, the SEP can be reduced by increasing $N$.

##  VI Historical Perspective and State-of-the-Art Solutions

Within the context of unconventional wireless communication paradigms, there
has been a growing interest in controlling the propagation environment in
order to increase the QoS and/or spectral efficiency. IM-based emerging
schemes such as MBM [13, 14, 15], spatial scattering modulation [11], and beam
IM [12], use the variations in the signatures of received signals by
exploiting reconfigurable antennas or scatterers to transmit additional
information bits in rich scattering environments [9]. Similarly, SM schemes
[4, 5, 6, 8, 9] create a new and extended signal constellation by exploiting
the indices of the available transmit antennas of MIMO systems, still
benefiting from the distinguishable propagation characteristics of different
transmit antennas in rich scattering environments. On the other hand, the RISs
are smart devices that intentionally control the propagation environment to
boost the signal quality at the receiver. In this section, we present a
comprehensive survey of state-of-the-art solutions based on intelligent
surfaces.

###  VI-A Origins

In this subsection, we briefly review the first concepts and ideas on
reconfigurable wireless systems and intelligent surfaces that focus on the
manipulation of EM waves in a deliberate manner to improve specified key
performance indicators.

Transmission through intelligent walls is reported in [42] and is based on
active frequency-selective surfaces that control the signal strength. In
particular, the authors focused their attention on controlling the propagation
environment by influencing radio channel parameters in order to enhance the
overall system performance, including signal coverage and interference. The
main motivation of the authors is to change the EM properties of the material
of the walls deploying frequency-selective surfaces in an indoor environment.
It is shown that an active frequency-selective surface can be realized by
changing the on/off states of PIN diodes, where a fully transparent or a
highly reflective surface is obtained. It is shown by computer simulations
that an active wall can be an efficient instrument for controlling the signal
coverage and QoS.

In [58], the same authors of [42] extended their intelligent walls concept by
considering an artificial neural network (ANN) to learn the best configuration
for the intelligent walls. According to sensory data and relative system
performance measures, two binary outputs are obtained (walls with on and off
states) by the ANN. In other words, a cognitive engine equipped with machine
learning algorithms is deployed to make decisions and control the intelligent
walls. In [59], an active frequency-selective surface-based antenna is
considered to enable switched beamforming by adjusting the on/off states of
the PIN diodes mounted on surface panels. By exploiting the on and off
combinations of the diodes on each surface panel, beam steering is achieved in
various directions.

In [43], the authors demonstrated that spatial microwave modulators are
capable of shaping, in a passive way, complex microwave fields in complex
wireless environments, by using only binary phase state tunable metasurfaces.

In an attempt to control the indoor wireless coverage, 3-D reflectors were
realized by the authors of [60] in order to be mounted around wireless APs.
Optimized but non-reconfigurable (static-shaped) reflector designs are
introduced to extend the wireless coverage in indoor environments.

The concept of coding meta-materials is proposed in [61] for manipulating EM
waves by changing the phase response of the surface elements. A meta-surface
composed of binary elements (with $0$ or $\pi$ phase differences) is
considered. By changing the coding bit sequences, the authors demonstrated the
feasibility of changing the scattering patterns. Particularly, the authors
determined the optimal coding sequences (the pattern of binary phases) to
minimize the radar cross section of surfaces by redirecting the incoming EM
energy into all directions. Similar to [42], the authors used the on/off
status of PIN diodes to change the phase responses of meta-material elements.
Finally, the authors constructed an FPGA hardware to control programmable
meta-surfaces through PIN diodes. It is argued by the authors that these
programmable meta-materials can be used to reduce the scattering features of
targets and to manipulate radiation beams of antennas. The same authors
introduced the concept of space-time coding digital metasurfaces in [62] by
exploiting the time dimension, which enables the control of EM waves in both
space and frequency domains. In particular, the authors aimed to control not
only the propagation direction but also the harmonic power distribution.

Another programmable meta-surface design that is based on PIN diodes, is
proposed in [63]. It is shown by the authors that besides a reconfigurable
phase, polarization control, scattering, and beam focusing are possible
through adjusting the on/off status of PIN diodes.

With the aid of reconfigurable reflect-arrays realized through tunable
(varactor-tuned) resonators [46], the concept of communications that employ
smart reflect-arrays with passive reflector elements is proposed in [47]. The
authors use smart reflect-arrays as an alternative to beamforming techniques
that require large number of antennas to focus the transmitted or received
signals, which is relatively difficult to implement for portable, wearable, or
even smaller devices. It is demonstrated that reflect-arrays can be used in an
effective way to change the phase of incoming signals via smart reflections
without buffering or processing them, and that the received signal quality can
be enhanced by optimizing the phase shift of each reflecting element of the
reflect-array. Through experiments and simulations, the authors showed that
higher spectral efficiencies can be obtained by using smart reflect-arrays
without any major modification in the hardware and software of user equipment.
The authors introduced a phase optimization algorithm to maximize the
transport capacity as well. In their later study, the authors investigated the
feasibility of using reconfigurable reflect-arrays to support millimeter-wave
communications in the context of the IEEE 802.11ad standard [48]. In this
work, the authors considered electronically-controlled relay switches with
on/off states in order to control reflector units instead of PIN diodes. A
relatively large reflect-array was built and a beam-searching protocol was
designed for application to 802.11ad networks and to overcome the LOS blockage
problem of millimeter-wave networks.

As a beyond massive MIMO solution, the large intelligent surface (LIS) concept
is proposed in [64, 65, 66]222It is worth noting that the term of LIS is also
used by other researchers [1, 67, 68, 69, 70, 71, 72, 32] in lieu of smart
reflect-arrays and RISs.. In particular, a system with a massive number of
radiating and sensing elements, which create a contiguous and
electromagnetically active surface, is considered. A major difference between
traditional massive MIMO [73] and the LIS is the employment of the whole
contiguous surface for transmission and reception. The authors also introduced
a signal model by considering a procedure called match filtering (through 2-D
integration of the received signals on the surface) and investigated the
capacity of the system.

Finally, the concept of software-controlled HyperSurfaces is proposed in [22,
19, 74, 75] with the aim of enabling full EM manipulation of the radio waves.
Equipped with a lightweight Internet-of-Things (IoT) gateway, intelligent
surfaces consisting of ultra-thin meta atoms receive commands from a
centralized controller and adjust their EM behavior by absorbing, focusing,
and steering the impinging EM waves in any direction. The functional and
physical architecture of HyperSurfaces, equipped with controllable switch
elements (with on/off states) and square patches, is defined. By computer
simulations, it is demonstrated that the signal coverage and signal strength
can be improved in indoor environments for communication at $60$ GHz, which is
usually affected by high propagation losses in non-LOS transmission.

The same authors generalized their programmable wireless environment concept
considering a general multi-user scenario and proposed solutions for
interference minimization, eavesdropping, and multipath mitigation [74]. A
general optimization problem was formulated to maximize the received signal
power and to minimize the maximum delay spread for arbitrarily distributed
users.

TABLE I: Initial Examples of Communications Through Intelligent Surfaces Year | Refs. | Scheme | Architecture | Functionality
---|---|---|---|---
2012 | [42, 58] | Intelligent wall | Active frequency selective surfaces with PIN diodes | Fully transparent/reflecting surfaces
2014 | [43] | Spatial microwave modulators | Binary phase state tunable meta-surfaces | Shaping complex microwave fields
2014 | [61] | Coding meta-materials |  Meta-surfaces with binary elements ($0$ or $\pi$ phases) | Reconfigurable scattering patterns
2016 | [63] | Programmable meta-surface | Meta-surfaces with PIN diode-equipped cells |  | Reconfigurable phase, polarization,
---
and scattering
2016 | [47] | Reconfigurable reflect-arrays | Reflect-arrays with tunable (varactor-tuned) resonators | Adjustable reflection phase
2017 | [64, 65, 66] | Large intelligent surface | Active contiguous surface for transmission and reception | Gains compared to massive MIMO
2018 | [22, 74] | Software-controlled hypersurface | Meta-surfaces equipped with IoT gateways |  | Wave absorption, polarization,
---
and steering

In Table 1, we summarize the main studies on intelligent surfaces reviewed in
this subsection by highlighting their architectures and functionalities.

###  VI-B State-of-The-Art Solutions

In the last few months, several studies and innovative solutions related to
RISs have been conducted by many authors. In particular, different authors
have used different terms to denote the RISs, which include: reconfigurable
intelligent surfaces, large intelligent surfaces, smart reflect-arrays,
intelligent reflecting surfaces, passive intelligent mirrors, artificial radio
space, and so on. Specifically, researchers focused on theoretical SNR and SEP
derivations, channel estimation, signal-to-interference-ratio (SINR)
maximization, and joint active and passive beamforming optimization problems,
investigated the application of machine learning tools, explored physical
layer layer security solutions, and evaluated the potential of intelligent
surfaces for application to millimeter-wave/terahertz, free space optical, and
visible light communication systems. Furthermore, the first attempts on
combining RISs with orthogonal frequency division multiplexing (OFDM) and
SM/space shift keying (SSK) schemes have been reported.

In this section, we briefly describe research works on intelligent surfaces,
which appeared in the literature during the past 1-2 years by following a
historical order.

Figure 9: Multi-user downlink transmission through an RIS (the LOS path is
optional).

An RIS-assisted downlink transmission scenario is considered in [76] to
support multiple users. More specifically, the authors considered the system
model shown in Fig. 9, where a multi-antenna BS supports $K$ single-antenna
users through intelligent reflections without a clear LOS path between the
users and the BS. The RIS elements only reflect phase shifted versions of the
incoming signals as in the model of Fig. 5. As a result, denoting the number
of BS antennas and RIS elements (reflecting meta-surfaces) by $M$ and $N$,
respectively, the received signal at user $k$ can be expressed as

$r_{k}=\mathbf{g}_{k}^{\mathrm{T}}\Phi\mathbf{H}\mathbf{x}+n_{k}$ (31)

where $\mathbf{x}\in\mathbb{C}^{M\times 1}$ is the precoded data vector,
$\mathbf{H}\in\mathbb{C}^{N\times M}$ is the matrix of channel coefficients of
the BS-RIS link, $\mathbf{g}_{k}\in\mathbb{C}^{N\times 1}$ is the vector of
channel coefficients between the RIS and user $k$, and $n_{k}$ is the AWGN
sample at this user. Similar to (10),
$\Phi=\mathrm{diag}(\left[e^{j\phi_{1}}\,\,e^{j\phi_{2}}\,\,\cdots\,\,e^{j\phi_{N}}\right])$
is a diagonal matrix that yields the phase shifts applied by the reflecting
elements of the RIS. The authors focused their attention on the maximization
of the system sum-rate by optimizing the phases of the RIS and user powers.
The resulting non-convex optimization problem is solved by combining
alternating maximization and majorization-minimization algorithms, and
improvements in the overall system throughput are reported.

In [72], the same authors studied the maximization of the energy efficiency by
considering RIS reflectors with finite resolution phases. It is shown by
computer simulations that even $1$-bit phase ($0$ and $\pi$) resolution
reflecting elements increase the energy efficiency of the system compared to
conventional AF relaying systems. In [32], the authors reported more
comprehensive simulation results on energy efficiency and system sum-rate for
a more practical system setup and system parameters. It is worth noting that
the implementation of this scheme requires full channel state information
(CSI) and the knowledge of RIS phase terms at the BS for transmit beamforming.

Th problem of joint active and passive beamformer design is investigated in
[77] and [78]. The authors, in particular, analyzed the minimization of the
total transmit power at the BS by jointly optimizing the transmit beamforming
vectors of the BS and the phase shifts of the RIS by imposing SINR constraints
for the users considering a multi-user downlink communication system. The
obtained non-convex optimization problem is tackled by employing semidefinite
relaxation and alternating optimization techniques. The authors showed that
the transmit power of the BS scales by $1/N^{2}$ as the number of reflector
units $N$ goes to infinity. In their follow-up study [79], the authors
considered the same power minimization problem by assuming discrete (finite
resolution) RIS phase shifts. It is shown that, compared to the ideal case
with continuous phase shifts, an RIS with discrete phase shifts achieves the
same power scaling law as a function of $N$ as for infinite-resolution phase
shifts but a constant performance loss is observed. The effect of erroneous
reflector phases on the error performance is investigated recently as well
[80].

In [1], the author introduced a mathematical framework for the calculation of
the average SEP of RIS-assisted systems. Furthermore, the possibility of using
an RIS as an AP (transmitter) by utilizing an unmodulated carrier for
intelligent reflection is studied. With the aid of computer simulations, it is
shown that doubling the number of reflecting elements provides one with a $6$
dB reduction of the SNR to obtain the same SEP. In other words, the average
received SNR of RIS-based systems is shown to be proportional to the square of
the total number of reflecting elements of the RIS $(N)$. In the follow-up
study [67], the author considered the concept of intelligent surfaces-assisted
IM by proposing LIS-SM and LIS-SSK schemes. In these schemes, the optimization
of the phases of intelligent surfaces and the concept of IM are jointly
exploited for improving the signal quality and the spectral efficiency.
Maximum energy-based suboptimal (greedy) and exhaustive search-based optimal
(maximum likelihood) detectors are formulated, and a unified framework to
calculate the theoretical average bit error probability is proposed. It is
shown by computer simulations that the resulting bit error probability is
significantly better than conventional fully-digital precoding-based receive
SSK schemes. A trade-off between the receiver cost and the BER performance is
investigated as well.

The authors of [68] investigated the maximization of the minimum SINR for an
RIS-assisted multi-user MISO scheme in rank-one or full-rank LOS channels
between the BS and the RIS. Furthermore, correlated Rayleigh channels are
considered between RIS elements and users, and an algorithm to optimize the
RIS phases in the presence of large-scale fading is proposed. For the same
system, the authors of [81] focused their attention on optimizing the transmit
beamformer and RIS phases, and proposed two algorithms to jointly optimize the
beamformer at the AP and the phase shifts at the RIS by considering the
maximum achievable spectral efficiency. It is shown that, unlike [77] and
[68], the proposed algorithms guarantee locally optimal solutions with the
perfect knowledge of channels at the AP and the RIS.

In [70], the authors considered an RIS-assisted large-scale antenna system in
which a BS communicates with a single-antenna user. Similar to previous works,
assuming the knowledge of CSI and RIS phases at the BS, precoding can be
performed, and the ergodic capacity of the system is maximized through the
optimization of the RIS phases. The authors also investigated the effect of
different Rician $K$ factors and discrete phase shifts. It is shown by
computer simulations that the use of $2$-bit phase shifts can ensure a high
capacity.

The authors of [82] proposed a new channel estimation protocol for an RIS-
assisted single-user MISO system with energy harvesting. In particular, the
authors take into account that RISs do not have active components. To enable
efficient power transfer, both active and passive near-optimal beamforming
designs are formulated. In [71], with the motivation of reducing the training
overhead in the presence of passive RIS elements, the authors considered two
separate methods for the RIS design in the presence of unknown channel
knowledge: one is based on compressive sensing and the other is based on deep
learning. In this work, a novel RIS architecture based on sparse channel
sensors, in which some of the RIS units are active (have a connection to a
baseband processor), is proposed. In other words, a number of active elements
are used at the RIS to simplify the channel estimation process. The authors
considered a deep learning-based solution, in which the RIS learns how to
interact with the incoming signals in an optimal way by exploiting the
estimated channels at the active elements. On the other hand, the authors of
[69] considered the problem of cascaded channel estimation with fully passive
RIS elements for the communication of multi-antenna terminals through an RIS.
A general concept is proposed for the estimation of the S-RIS-D cascaded MIMO
channel.

For the scenario of multi-user uplink transmission, the performance of the
over-the-air computation scheme, in which the AP computes a target function of
the aggregated data from all users, is investigated in [83] by exploiting
RISs.

The physical layer security of RIS-based systems is investigated in [84] and
[85]. Specifically, the authors of [84] considered an RIS-assisted secure
communication system that consists of a legitimate receiver and an
eavesdropper. Both the beamformer at the BS and the RIS phase shifts are
jointly optimized to enhance the secrecy rate. Furthermore, the secrecy rate
of massive MIMO and RIS-aided systems is compared and it is revealed that
increasing the number of RIS reflecting elements is more beneficial than
increasing the number of antenna elements at the BS. A downlink MISO broadcast
system with multiple legitimate receivers and eavesdroppers is considered in
[85]. The authors formulated a minimum-secrecy-rate maximization problem by
jointly optimizing the beamformer at the BS and reflecting coefficients (both
discrete and continuous) at the RIS. Globally optimal and low-complexity
suboptimal algorithms are proposed. Recently, the authors of [86] focused on
the problem of stronger eavesdropping channel and maximized the secrecy rate
of the legitimate user by jointly designing the AP’s transmit beamformer and
the RIS phases.

Recent research contributions on the design of HyperSurfaces include the
following. A sensing system to estimate the radio waves impinging upon the
HyperSurfaces that can operate without additional external or internal
hardware such as field nano-sensors, is proposed in [87]. It is shown that
HyperSurfaces can be programmed for wave sensing and wave manipulation
simultaneously. Finally, an approach based on machine learning (neural
networks) to adaptively configure HyperSurfaces-aided programmable
environments is proposed in [88].

As for using RISs for transmission and reception, researchers focused their
attention on outage probability [89], asymptotic data rate [90], and uplink
spectral efficiency [91].

More recently, researchers considered RIS-assisted millimeter-wave/terahertz
communications [92], visible light communications [93], free space optical
communications [94], and OFDM systems [95].

TABLE II: State-of-the-Art Schemes with Intelligent Surfaces Refs. | Contribution
---|---
[76, 72, 32] |  | A precoding-aided RIS scheme is considered for multi-user downlink transmission.
---
Maximization of sum-rate and energy efficiency is performed with finite
resolution reflectors.
[77, 78, 79] |  | The problem of joint active and passive beamformer design is investigated for a MISO multi-user system.
---
Minimization of the BS transmit power is performed through optimization and
square-law scaling in transmit power is demonstrated.
[1] |  | A mathematical framework is proposed for the calculation of the average SEP of RIS (LIS)-assisted systems.
---
The concept of using the RIS as an AP (transmitter) is also introduced.
[67] |  | The concept of RIS (LIS)-assisted IM is proposed by considering the RIS as an AP.
---
Greedy and maximum likelihood detectors are formulated for LIS-SM and LIS-SSK
schemes along with theoretical derivations.
[68] |  | The maximization of the minimum SINR is investigated for an RIS-assisted multi-user MISO system.
---
Rank-one and full-rank LOS channels, correlated RIS channels, and large-scale
fading statistics are considered for phase optimization.
[81] |  | The problem of optimal transmit beamformer and RIS phase shifter is investigated to maximize the achievable spectral efficiency.
---
It has been shown that the proposed algorithms guarantee locally optimal
solutions.
[70] |  | An RIS-assisted large-scale MISO system is considered with Rician fading.
---
Ergodic capacity of the system is maximized by the optimization of LIS phases.
[82] |  | A new channel estimation protocol for an RIS-assisted MISO system with energy harvesting is proposed.
---
Active and passive near-optimal beamforming designs are formulated to enable
efficient power transfer.
[71] |  | A new RIS architecture based on sparse channel sensors, in which some of the existing RIS units are active, is proposed.
---
Two separate methods, based on compressive sensing and deep learning, are
considered for the RIS design.
[69] |  | Considered the problem of cascaded channel estimation with fully passive RIS elements.
---
An RIS-assisted massive multi-user MIMO system is considered and a three-stage
channel estimation algorithm is proposed.
[84] |  | An RIS-assisted secure communication system with a legitimate receiver and an eavesdropper is considered.
---
Showed that increasing the number of RIS reflecting elements is more
beneficial than increasing the number of antenna elements at BS.
[85] |  | A downlink MISO broadcast system with multiple legitimate receivers and eavesdroppers is considered.
---
A minimum-secrecy-rate maximization problem is formulated by jointly
optimizing the BS beamformer and RIS reflecting coefficients.

The emerging concept of programmable radio environments is receiving
considerable attention from the research community of computer scientists as
well. In the last couple of years, in particular, a few large scale-scale
testbeds have been realized, and promising results have been obtained. Notable
examples include [96, 97, 98]. The underlying idea of these research
activities is to prove the feasibility of moving beamforming functions from
the radio end-points to the environment. In [98], in particular, the author
realized a testbed that is today considered to be the largest RIS currently
available in the world: The prototype has $3,720$ inexpensive antennas that
are deployed on a $6$ square-meter surface. Experimental results in an indoor
environment have shown an improvement of the median of the signal strength of
$10.5$x, and an improvement of the median of the data rate of $2.1$x [98, Fig.
11].

In Table II, we present a summary of the major contributions of the recent
studies reviewed in this subsection.

##  VII Potential Use Cases

In this section, we briefly discuss a few use cases in which the RISs may play
a fundamental role either for enhancing the coverage probability, spectral
efficiency, and energy efficiency or for reducing the implementation
complexity and power consumption of wireless networks. Five potential use
cases are briefly discussed.

Overcoming non-LOS Scenarios – One of the most promising use cases to leverage
the RISs in wireless networks consists of employing them as reconfigurable
reflectors in scenarios in which the LOS path is either blocked or is not
strong enough to support cell-edge users. For example, RISs can be easily
attached to walls or ceilings in indoors, and can be integrated into the
facades of buildings in outdoors. This application scenario seems to be
relevant in the high frequency transmission range, e.g., in the millimeter-
wave spectrum, in the D-band spectrum ($>100$ GHz), and in the visible light
spectrum. In these cases, in fact, the LOS path is often obstructed, and the
possibility of realizing strong and reconfigurable non-LOS links is a
promising use case, especially because of the promising scaling laws as a
function of the distance and of the number of reflecting meta-surfaces that
are obtained in Section II. Field trials that substantiate the applicability
and potential gains of this case study were recently reported for application
to vehicular networks by research scientists of NTT Docomo and Metawave
Corporation [99].

Overcoming Localized Coverage Holes – Another promising case study consists of
using the RISs to counteract localized coverage holes in urban scenarios, and
indoor harsh propagation environments. In many urban and densely populated
cities worldwide, in fact, there exist localized dead zones where the signal
quality is not sufficiently good. Similar issues occur in indoor environments,
like industrial factories and underground metro stations. In these scenarios,
conventional solutions to overcome coverage holes are to deploy more BSs or
relays/repeaters. Unfortunately, these solutions are expensive and increase
the carbon footprint of wireless communications. The deployment of RISs is, on
the other hand, a cost-effective and environmental-friendly solution to solve
the problems of localized coverage holes.

Reducing the EM Pollution – In contrast to other communication technologies,
one of the main features of RISs is recycling the radio waves in a
constructive and energy-efficient manner. Multipath propagation is, in fact,
often perceived as incontrollable and is usually counteracted by increasing
the complexity of the transmitters and the receivers. This usually entails an
increase of the number of radio waves emitted, e.g., by deploying more BSs or
relays, which produce additional signals in the environment. This results in
increasing the emission of EM radiations. The use of RISs, on the other hand,
does not foresee the generation of new signals, but their intelligent
utilization. So, the concept of RISs is a promising solution to lower the
levels of EM radiations, with major applications in scenarios like, hospitals
and airplanes.

Energy-Free Internet of Things – The IoT is regarded as a fundamental
component of 5G and 6G wireless networks. The possibility of collecting data
from large numbers of sensors deployed throughout the network has a myriad of
applications. These devices, however, are expected to communicate the sensed
data to fusion centers, which are then in charge of the subsequent processing
and analysis. The total amount of energy that these devices need in order to
report the sensed data cannot be underestimated, and is one of the bottlenecks
to make the IoT a pervasive technology. The concept of RISs in combination
with backscatter communications is a promising alternative to allow IoT
devices to report the sensed data in a energy free manner. Imagine to realize
clothings and IoT devices with reconfigurable meta-surfaces that are capable
of modifying the reflected waveforms according to the sensed data. By using
the backscatter communications principle, the sensed data may be piggybacked
into the shape of the reflected signals at no overhead and at a zero energy
cost [23].

Low-Complexity and Energy-Efficient Massive Transmitters – The advantages of
using MIMO, and, more recently, massive MIMO are undeniable. However, these
gains are not obtained for free. In particular, the benefits of MIMO come at
the cost of using a number of RF chains that is usually the same as the number
of radiating elements. As a result, the complexity and power consumptions
associated with using large numbers of antennas cannot be under estimated. The
use of RISs as transmitters offers a unique opportunity to realize very large
antenna arrays with a few, possibly one, RF chain [1]. The testbed recently
implemented in [54] and [56] is a tangible example that realizing low-
complexity massive MIMO is possible, by leveraging the principles of SM, MBM,
and, more in general, IM.

##  VIII Open Research Issues

In this section, we briefly discuss the major open research issues that need
to be tacked in order to leverage the potential of RISs in wireless networks.
We discuss, in particular, how the use of RISs necessitates to rethink how
wireless networks are analyzed and designed today.

Physics- and EM-Compliant Models – A major limitation of current research on
RISs in wireless networks is the lack of accurate and tractable models that
describe the reconfigurable meta-surfaces as a function of their EM
properties. The vast majority of research works available to date rely, e.g.,
on the assumption that the meta-surfaces act as perfect reflectors. However,
the meta-surfaces are meant to apply functions other than reflection, and,
more importantly, their response to the radio waves depends on several
factors, among which the angle of incidence, the angle of reflection, the
angle of refraction, the polarization of the incident wave, and the specific
materials that the meta-surface is made of. Also, the size of the meta-surface
plays an important role on its properties. Physics- and EM-compliant models
are, therefore, needed in order to avoid studying too simplistic system models
and to obtain too optimistic performance predictions. The zero-thickness sheet
model of the meta-surfaces is a good starting point to build such models
[100].

Experimentally-Validated Channel Models – To date, there exist no
experimentally-validated channel models that provide wireless researchers with
accurate and realistic information on the path-loss, shadowing, and fast-
fading statistics for RISs. In Section II, we have predicted that the path-
loss function of an RIS that acts as a perfect reflector scales with the sum
of the distances between the RIS and the transmitter and receiver. This
theoretical model is based on geometrical optics approximations (and the
application of Fermat’s principle), whose accuracy depends on the geometry and
the materials that the meta-surface is made of. In addition, the signals
reflected from the elementary meta-surfaces or even by each of their
individual scattering particles may not necessarily be uncorrelated. Some
level of spatial correlations are likely to be expected, which may affect the
ultimate performance limits and achievable scaling laws.

Information- and Communication-Theoretic Models – The use of RISs renders the
information- and communication-theoretic models employed in wireless
communications obsolete [23, Fig. 6]. The conventional formulation of the
Shannon capacity, in particular, needs to be revisited, since the system
itself can be programmed, and the distribution of the input needs to be
adapted to the possible states that the system itself can assume. The possible
states of the system are dictated by the functions that are applied by the
meta-surfaces, and by their statistical distribution. The achievable limits of
the RISs and their interpretation in small-scale and large-scale wireless
networks need to be determined. In the presence of RISs, in fact, not only the
input is an optimization variable, but the system itself becomes an
optimization variable, and they need to be jointly optimized.

Communication-Theoretic Performance Limits – With the exception of [1, 67,
101], the optimization of, e.g., the phases of the RISs that act as
intelligent reflecting surfaces are obtained by solving complicated, and often
non-convex, optimization problems. The optimal reflecting phases, which are
channel-dependent, are, therefore, only available numerically. This prevents
communication theorists from developing tractable analytical frameworks for
analyzing the performance of RIS-empowered wireless networks, and to shed
light on the corresponding performance trends. In the present paper, we have
proposed simple analytical frameworks under simple system models, where the
optimal phases can be easily determined. By slightly modifying the system
model as in [101], we readily note that the analytical complexity of the
resulting optimal phases to employ increases significantly. Due to the
insights for system design that we gained in the previous sections based on
simple system models, it is considered to be important to develop tractable
and insightful communication-theoretic frameworks for unveiling the achievable
performance of RISs in future wireless networks, as a function of their many
constituent parameters.

Spatial Models for System-Level Analysis and Optimization – As just mentioned,
the analysis and optimization of point-to-point wireless networks in the
presence of RISs is an open and not an easy task. More difficult is,
therefore, the analysis and optimization of large-scale networks where
multiple RISs are spatially distributed according to some complex spatial
patterns. A simple framework was recently introduced in [102] by using the
theory of line processes, and under the assumption that the RISs can reflect
signals coming from any direction and can direct them towards any direction.
In contrast with typical approaches that are used in the literature for
system-level analysis, which are primarily based on stochastic geometry
models, the use of RISs necessitates new models and methods of analysis. It is
known, in fact, that signals’ reflections are usually ignored when analyzing
large-scale networks due to the inherent analytical complexity [103].
Furthermore, the spatial correlation that originates from the spatial
positions of objects, transmitters, and receivers is not easy to be modeled in
a tractable manner [104]. In RIS-empowered wireless networks, reflections,
refractions, and blockages due to the objects are exploited and optimized for
increasing the network performance, since the RISs aim to take advantage of
them. Major open issues on the modeling and analysis of large-scale networks
need, therefore, to be solved.

Channel Estimation and Feedback Overhead – One of the fundamental assumptions
that make RIS-assisted communications a competitive low-complexity and energy-
efficient technology lies in its almost passive implementation. This implies
that, ideally, the RISs are expected not to rely on power amplifiers, channel
estimators, ADCs, DACs, etc. Therefore, the fundamental issue lies in how the
RISs can estimate the channels that are needed for optimizing, e.g., the
reflection phases, and how they can report them to the transmitter, the
receiver, or any network controller in charge of computing the optimal phases
as a function of the channels themselves. A possible solution may be to embed
low-power sensors throughout the RISs, possibly powered by energy harvested
modules, which are in charge of sensing the channels, and then reporting them
to a gateway that is located on the RISs, which, in turn, sends them to the
network controller. This solution relies on the availability of at least one
power amplifier and one ADC, but has the advantage that no computation is made
on the RISs [23]. Another promising solution relies on estimating the optimal
phases without assuming the knowledge of the individual channels between the
RIS and the transmitter and the receiver, but by considering only the combined
(or product) channel between them. This solution was recently reported in
[69]. The optimality of this approach compared with knowing the individual
channels, and the associated complexity vs. performance vs. estimation
overhead trade-offs are an important open issue. More in general, the design
of efficient channel estimation and feedback protocols that make the RISs as
passive as possible, and the associated performance vs. complexity trade-offs
are not known yet.

Implementation, Testbeds, and Field Trials – In the last few years, a few
promising testbeds and experimental activities to substantiate the potential
gains offered by RISs have been reported [99, 43, 48, 74, 98]. These research
activities are promising, and have initially confirmed that the use of RISs in
wireless networks is a practically viable solution. However, the details of
these experimental tests are often difficult to find, and are not sufficient
to judge the actual potential of RISs in realistic operating conditions.

Data-Driven Optimization – RIS-empowered wireless networks are much more
complex systems to model, analyze, and design than current and next-generation
wireless networks. Due to their inherent system complexity, the use of data-
driven methods based on deep learning, reinforcement learning, and transfer
learning are promising tools to leverage [25]. Machine learning, in
particular, is a promising solution to simplify the implementation of RIS-
based communication systems, and to increase the efficiency of the
communication system [71, 88].

Integration of RISs with Emerging Technologies – The concept of RIS-empowered
wireless networks has at its core the fundamental idea of moving the functions
that are usually implemented at the radio end-points of a communication
network, i.e., the base stations, the APs, and the mobile terminals, to the
environment. In that regard, the synergistic integration of RISs with state-
of-the-art and emerging technologies, such as small cells, massive MIMO,
millimeter-wave communications, visible light communications, terahertz
communication, free space optics, IoT, drones-aided communications, energy
harvesting, physical layer security, beamforming, etc., is a promising and
unexplored research direction. For example: i) RISs can be used for sculpting
and exploiting non-LOS reflections at very high transmission frequencies,
e.g., above $100$ GHz, where it is very difficult to have reliable LOS links
in mobile environments [99], ii) RISs can be leveraged to introduce a new
concept of beamforming that is not realized at the transmitter anymore, but is
moved to the environment, thus enabling low-complexity and tiny single-antenna
devices to benefit from high-precision beamforming [98], and iii) RISs may be
a fundamental enabler to realize advanced security schemes at the physical
layer, thanks to their potential of sculpting the wireless environment [84,
85, 86].

##  IX Conclusion

In this paper, we have summarized the latest research activities on the
emerging field of RIS-empowered wireless networks. We have illustrated the
main differences that render RISs a new technology compared with, at the first
sight, similar technologies, such as relaying and backscatter communications.
We have described two possible uses to leverage the potential of RISs in
wireless networks: to shape the radio waves in order to control, in a
deterministic fashion, the multipath propagation, e.g., the signals reflected
from walls are steered toward specified directions, and to realize low-
complexity and energy efficient transmitters that require only a limited,
ideally one, active RF chain. The error probability of both implementations
has been studied by capitalizing on the CLT, and it has been shown that the
error probability exhibits a water-fall behavior as a function of the number
of reconfigurable elements of the RISs and of the SNR. As far as the link
budget analysis is concerned, we have highlighted that the concept of RISs is
different from relaying and backscatter communications, since, if their
geometric size is sufficiently larger than the wavelength, they can be treated
as specular reflectors, and the received power as a function of the distance
is determined, at the first-order, by the Fermat’s principle. Finally, we have
illustrated possible use cases where the RIS may play a significant role, and
have discussed fundamental research issues to tackle in order to fully exploit
the potential of RISs in wireless networks.

## References

  * [1] E. Basar, “Transmission through large intelligent surfaces: A new frontier in wireless communications,” in _Proc. European Conf. Netw. Commun. (EuCNC 2019)_ , Valencia, Spain, June 2019. [Online]. Available: arXiv:1902.08463v2
  * [2] “Cisco visual networking index: Global mobile data traffic forecast update, 2017–2022,” Feb. 2019. [Online]. Available: https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white-paper-c11-738429.pdf
  * [3] A. Gatherer. (2018, June) What will 6G be? [Online]. Available: https://www.comsoc.org/publications/ctn/what-will-6g-be
  * [4] R. Mesleh, H. Haas, S. Sinanovic, C. W. Ahn, and S. Yun, “Spatial modulation,” vol. 57, no. 4, pp. 2228–2241, July 2008.
  * [5] M. Di Renzo, H. Haas, and P. Grant, “Spatial modulation for multiple-antenna wireless systems: A survey,” _IEEE Commun. Mag._ , vol. 49, no. 12, pp. 182–191, Dec. 2011.
  * [6] M. Di Renzo, H. Haas, A. Ghrayeb, S. Sugiura, and L. Hanzo, “Spatial modulation for generalized MIMO: Challenges, opportunities, and implementation,” _Proc. IEEE_ , vol. 102, no. 1, pp. 56–103, Jan. 2014.
  * [7] P. Yang, M. Di Renzo, Y. Xiao, S. Li, and L. Hanzo, “Design guidelines for spatial modulation,” _IEEE Commun. Surveys Tutorials_ , vol. 17, no. 1, pp. 6–26, 1st Quart. 2015.
  * [8] E. Basar, “Index modulation techniques for 5G wireless networks,” _IEEE Commun. Mag._ , vol. 54, no. 7, pp. 168–175, June 2016.
  * [9] E. Basar, M. Wen, R. Mesleh, M. D. Renzo, Y. Xiao, and H. Haas, “Index modulation techniques for next-generation wireless networks,” _IEEE Access_ , vol. 5, pp. 16 693–16 746, Sept. 2017.
  * [10] M. Wen, X. Cheng, and L. Yang, _Index Modulation for 5G Wireless Communications_. Berlin, Germany: Springer, 2017.
  * [11] Y. Ding, K. J. Kim, T. Koike-Akino, M. Pajovic, P. Wang, and P. Orlik, “Spatial scattering modulation for uplink millimeter-wave systems,” _IEEE Commun. Lett._ , vol. 21, no. 7, pp. 1493–1496, July 2017\.
  * [12] Y. Ding, V. Fusco, A. Shitvov, Y. Xiao, and H. Li, “Beam index modulation wireless communication with analog beamforming,” _IEEE Trans. Veh. Technol._ , vol. 67, no. 7, pp. 6340–6354, July 2018.
  * [13] E. Seifi, M. Atamanesh, and A. K. Khandani, “Media-based MIMO: Outperforming known limits in wireless,” in _Proc. 2016 IEEE Int. Conf. Commun. (ICC)_ , Kuala Lumpur, Malaysia, May 2016, pp. 1–7.
  * [14] Y. Naresh and A. Chockalingam, “On media-based modulation using RF mirrors,” _IEEE Trans. Veh. Technol._ , vol. 66, no. 6, pp. 4967–4983, June 2017.
  * [15] E. Basar, “Media-based modulation for future wireless systems: A tutorial,” _IEEE Wireless Commun. (to appear)_ , Mar. 2019. [Online]. Available: arXiv:1811.08730
  * [16] M. Di Renzo, “Spatial modulation based on reconfigurable antennas – A new air interface for the IoT,” in _2017 IEEE Military Commun. Conf. (MILCOM)_ , Baltimore, MD, USA, Oct. 2017, pp. 495–500.
  * [17] D. Nguyen Viet, M. Di Renzo, V. Basavarajappa, B. Bedia Exposito, J. Basterrechea, and D.-T. Phan-Huy, “Spatial modulation based on reconfigurable antennas: Performance evaluation by using the prototype of a reconfigurable antenna,” _EURASIP J. Wireless Commun. Netw._ , vol. 2019, no. 1, p. 149, June 2019.
  * [18] D. Phan-Huy et al., “Single-carrier spatial modulation for the Internet of Things: Design and performance evaluation by using real compact and reconfigurable antennas,” _IEEE Access_ , vol. 7, pp. 18 978–18 993, Jan. 2019.
  * [19] C. Liaskos, S. Nie, A. Tsioliaridou, A. Pitsillides, S. Ioannidis, and I. Akyildiz, “Realizing wireless communication through software-defined hypersurface environments,” in _Proc. 2018 IEEE 19th Int. Symp. ”A World of Wireless, Mobile and Multimedia Networks” (WoWMoM)_ , Chania, Greece, June 2018, pp. 14–15.
  * [20] G. Lavigne, K. Achouri, V. S. Asadchy, S. A. Tretyakov, and C. Caloz, “Susceptibility derivation and experimental demonstration of refracting metasurfaces without spurious diffraction,” _IEEE Trans. Antennas Propag._ , vol. 66, no. 3, pp. 1321–1330, Mar. 2018.
  * [21] F. Liu, A. Pitilakis, M. S. Mirmoosa, O. Tsilipakos, X. Wang, A. C. Tasolamprou, S. Abadal, A. Cabellos-Aparicio, E. Alarcón, C. Liaskos, N. V. Kantartzis, M. Kafesaki, E. N. Economou, C. M. Soukoulis, and S. Tretyakov, “Programmable metasurfaces: State of the art and prospects,” in _Proc. 2018 IEEE Int. Symp. Circuits Syst. (ISCAS)_ , Florence, Italy, May 2018.
  * [22] C. Liaskos, S. Nie, A. Tsioliaridou, A. Pitsillides, S. Ioannidis, and I. Akyildiz, “A new wireless communication paradigm through software-controlled metasurfaces,” _IEEE Commun. Mag._ , vol. 56, no. 9, pp. 162–169, Sept. 2018.
  * [23] M. Di Renzo et al., “Smart radio environments empowered by reconfigurable AI meta-surfaces: An idea whose time has come,” _EURASIP J. Wireless Commun. Net._ , vol. 2019, p. 129, May 2019.
  * [24] (2017) 5GPPP vision on software networks and 5G SN WG. [Online]. Available: https://5g-ppp.eu/wp-content/uploads/2014/02/5G-PPPSoftNetsWG_whitepaper_v20.pdf
  * [25] A. Zappone, M. D. Renzo, and M. Debbah, “Wireless networks design in the era of deep learning: Model-based, AI-based, or both?” Feb. 2019. [Online]. Available: arXiv:1902.02647
  * [26] Q. Wu and R. Zhang, “Towards smart and reconfigurable environment: Intelligent reflecting surface aided wireless network,” May 2019. [Online]. Available: arXiv:1905.00152v2
  * [27] M. Born and E. Wolf, _Principles of Optics: Electromagnetic Theory of Propagation, Interference and Diffraction of Light_. Cambridge, UK: Cambridge University Press, 1999.
  * [28] N. Yu, P. Genevet, M. A. Kats, F. Aieta, J.-P. Tetienne, F. Capasso, and Z. Gaburro, “Light propagation with phase discontinuities: Generalized laws of reflection and refraction,” _Science_ , vol. 334, no. 6054, pp. 333–337, Oct. 2011.
  * [29] A. Goldsmith, _Wireless Communications_. Cambridge, UK: Cambridge University Press, 2005.
  * [30] V. S. Asadchy, M. Albooyeh, S. N. Tcvetkova, A. Díaz-Rubio, Y. Ra’di, and S. A. Tretyakov, “Perfect control of reflection and refraction using spatially dispersive metasurfaces,” _Phys. Rev. B_ , vol. 94, p. 075142, Aug. 2016.
  * [31] A. Díaz-Rubio, V. S. Asadchy, A. Elsakka, and S. A. Tretyakov, “From the generalized reflection law to the realization of perfect anomalous reflectors,” _Science Advances_ , vol. 3, no. 8, Aug. 2017.
  * [32] C. Huang, A. Zappone, G. C. Alexandropoulos, M. Debbah, and C. Yuen, “Reconfigurable intelligent surfaces for energy efficiency in wireless communication,” _IEEE Trans. Wireless Commun._ , June 2019. [Online]. Available: arXiv:1810.06934
  * [33] K. Achouri, B. A. Khan, S. Gupta, G. Lavigne, M. A. Salem, and C. Caloz, “Synthesis of electromagnetic metasurfaces: Principles and illustrations,” _EPJ Applied Metamaterials_ , vol. 2, p. 12, Jan. 2016.
  * [34] G. Lavigne, K. Achouri, V. S. Asadchy, S. A. Tretyakov, and C. Caloz, “Susceptibility derivation and experimental demonstration of refracting metasurfaces without spurious diffraction,” _IEEE Trans. Antennas Propag._ , vol. 66, no. 3, pp. 1321–1330, Mar. 2018.
  * [35] K. Achouri and C. Caloz, “Design, concepts, and applications of electromagnetic metasurfaces,” _Nanophotonics_ , vol. 7, no. 6, pp. 1095–1116, June 2018.
  * [36] F. Liu, O. Tsilipakos, A. Pitilakis, A. C. Tasolamprou, M. S. Mirmoosa, N. V. Kantartzis, D.-H. Kwon, M. Kafesaki, C. M. Soukoulis, and S. A. Tretyakov, “Intelligent metasurfaces with continuously tunable local surface impedance for multiple reconfigurable functions,” _Phys. Rev. Applied_ , vol. 11, p. 044024, Apr. 2019.
  * [37] M. Dohler and Y. Li, _Cooperative communications: Hardware, channel and PHY_. Chichester, UK: Wiley, 2010.
  * [38] J. D. Griffin and G. D. Durgin, “Complete link budgets for backscatter-radio and RFID systems,” _IEEE Antennas Propag. Mag._ , vol. 51, no. 2, pp. 11–25, Apr. 2009.
  * [39] O. Landron, M. J. Feuerstein, and T. S. Rappaport, “A comparison of theoretical and empirical reflection coefficients for typical exterior wall surfaces in a mobile radio environment,” _IEEE Trans. Antennas Propag._ , vol. 44, no. 3, pp. 341–351, Mar. 1996.
  * [40] K. R. Schaubach, N. J. Davis, and T. S. Rappaport, “A ray tracing method for predicting path loss and delay spread in microcellular environments,” in _Proc. Veh. Technol. Society 42nd VTS Conf. - Frontiers of Technology_ , May 1992, pp. 932–935 vol.2.
  * [41] R. Mehrotra, R. I. Ansari, A. Pitilakis, S. Nie, C. Liaskos, N. V. Kantartzis, and A. Pitsillides, “3D channel modeling and characterization for Hypersurface empowered indoor environment at 60 GHz millimeter-wave band,” 2019. [Online]. Available: arXiv:1907.00037
  * [42] L. Subrt and P. Pechac, “Controlling propagation environments using intelligent walls,” in _Proc. 2012 6th European Conf. Antennas Propag. (EUCAP)_ , Prague, Czech Republic, Mar. 2012, pp. 1–5.
  * [43] N. Kaina, M. Dupré, G. Lerosey, and M. Fink, “Shaping complex microwave fields in reverberating media with binary tunable metasurfaces,” _Scientific Reports_ , vol. 4, no. 1, p. 6693, Oct. 2014.
  * [44] M. Dupré, P. del Hougne, M. Fink, F. Lemoult, and G. Lerosey, “Wave-field shaping in cavities: Waves trapped in a box with controllable boundaries,” _Phys. Rev. Lett._ , vol. 115, p. 017701, July 2015.
  * [45] P. del Hougne, M. Fink, and G. Lerosey, “Optimally diverse communication channels in disordered environments with tuned randomness,” _Nature Electronics_ , vol. 2, no. 1, pp. 36–41, 2019.
  * [46] S. V. Hum and J. Perruisseau-Carrier, “Reconfigurable reflectarrays and array lenses for dynamic antenna beam control: A review,” _IEEE Trans. Antennas Propag._ , vol. 62, no. 1, pp. 183–198, Jan. 2014.
  * [47] X. Tan, Z. Sun, J. M. Jornet, and D. Pados, “Increasing indoor spectrum sharing capacity using smart reflect-array,” in _Proc. 2016 IEEE Int. Conf. Commun. (ICC)_ , Kuala Lumpur, Malaysia, May 2016, pp. 1–6.
  * [48] X. Tan, Z. Sun, D. Koutsonikolas, and J. M. Jornet, “Enabling indoor mobile millimeter-wave networks based on smart reflect-arrays,” in _Proc. IEEE Conf. Comput. Commun. (INFOCOM)_ , Honolulu, HI, USA, Apr. 2018, pp. 270–278.
  * [49] S. Foo, “Liquid-crystal reconfigurable metasurface reflectors,” in _Proc. 2017 IEEE Int. Symp. on Antennas Propag._ , San Diego, CA, USA, July 2017, pp. 2069–2070.
  * [50] J. G. Proakis, _Digital Communications_ , 5th ed. New York: McGraw-Hill, 2008.
  * [51] M. Simon and M. S. Alaouni, _Digital Communications over Fading Channels_ , 2nd ed. New Jersey: John Wiley & Sons, 2005.
  * [52] R. Long, H. Guo, G. Yang, Y.-C. Liang, and R. Zhang, “Symbiotic radio: A new communication paradigm for passive Internet-of-Things,” Oct. 2018. [Online]. Available: arXiv:1810.13068
  * [53] M. Di Renzo, S. Narayanan, F. Graziosi, and H. Haas, “Distributed spatial modulation: A cooperative diversity protocol for half–duplex relay–aided wireless networks,” vol. 65, no. 5, pp. 2947–2964, May 2016.
  * [54] W. Tang, J. Y. Dai, M. Chen, X. Li, Q. Cheng, S. Jin, K. Wong, and T. J. Cui, “Programmable metasurface-based RF chain-free 8PSK wireless transmitter,” _Elect. Lett._ , vol. 55, no. 7, pp. 417–420, Apr. 2019.
  * [55] J. Y. Dai, W. K. Tang, J. Zhao, X. Li, Q. Cheng, J. C. Ke, M. Z. Chen, S. Jin, and T. J. Cui, “Wireless communications through a simplified architecture based on time-domain digital coding metasurface,” _Advanced Materials Technologies_ , p. 1900044, Feb. 2019.
  * [56] W. Yan, X. Kuai, and X. Yuan, “Passive beamforming and information transfer via large intelligent surface,” May 2019. [Online]. Available: arXiv:1905.01491
  * [57] R. Mesleh and A. Alhassi, _Space Modulation Techniques_. New Jersey: John Wiley & Sons, 2018.
  * [58] L. Subrt and P. Pechac, “Intelligent walls as autonomous parts of smart indoor environments,” _IET Commun._ , vol. 6, no. 8, pp. 1004–1010, May 2012\.
  * [59] G. H. Elzwawi, H. H. Elzwawi, M. M. Tahseen, and T. A. Denidni, “Frequency selective surface-based switched-beamforming antenna,” _IEEE Access_ , vol. 6, pp. 48 042–48 050, July 2018.
  * [60] X. Xiong, J. Chan, E. Yu, N. Kumari, A. A. Sani, C. Zheng, and X. Zhou, “Customizing indoor wireless coverage via 3D-fabricated reflectors,” in _Proc. 4th ACM Int. Conf. Systems for Energy-Efficient Built Environments_ , Delft, Netherlands, 2017, pp. 1–10.
  * [61] T. J. Cui, M. Q. Qi, X. Wan, J. Zhao, and Q. Cheng, “Coding metamaterials, digital metamaterials and programmable metamaterials,” _Light: Science & Applications_, vol. 3, p. e218, Oct. 2014.
  * [62] L. Zhang, X. Q. Chen, S. Liu, Q. Zhang, J. Zhao, J. Y. Dai, G. D. Bai, X. Wan, Q. Cheng, G. Castaldi, V. Galdi, and T. J. Cui, “Space-time-coding digital metasurfaces,” _Nature Commun._ , vol. 9, no. 1, p. 4334, Oct. 2018.
  * [63] H. Yang, X. Cao, F. Yang, J. Gao, S. Xu, M. Li, X. Chen, Y. Zhao, Y. Zheng, and S. Li, “A programmable metasurface with dynamic polarization, scattering and focusing control,” _Scientific Reports_ , vol. 6, p. 35692 EP, Oct. 2016\.
  * [64] S. Hu, F. Rusek, and O. Edfors, “The potential of using large antenna arrays on intelligent surfaces,” in _Proc. 2017 IEEE 85th Veh. Technol. Conf. (VTC Spring)_ , Sydney, Australia, June 2017, pp. 1–6.
  * [65] ——, “Beyond massive MIMO: The potential of data transmission with large intelligent surfaces,” _IEEE Trans. Signal Process._ , vol. 66, no. 10, pp. 2746–2758, May 2018.
  * [66] ——, “Beyond massive MIMO: The potential of positioning with large intelligent surfaces,” _IEEE Trans. Signal Process._ , vol. 66, no. 7, pp. 1761–1774, Apr. 2018.
  * [67] E. Basar, “Large intelligent surface-based index modulation: A new beyond MIMO paradigm for 6G,” Apr. 2019. [Online]. Available: arXiv:1904.06704
  * [68] Q.-U.-A. Nadeem, A. Kammoun, A. Chaaban, M. Debbah, and M.-S. Alouini, “Asymptotic analysis of large intelligent surface assisted MIMO communication,” Mar. 2019. [Online]. Available: arXiv:1903.08127
  * [69] Z.-Q. He and X. Yuan, “Cascaded channel estimation for large intelligent metasurface assisted massive MIMO,” May 2019. [Online]. Available: arXiv:1905.07948
  * [70] Y. Han, W. Tang, S. Jin, C.-K. Wen, and X. Ma, “Large intelligent surface-assisted wireless communication exploiting statistical CSI,” Dec. 2018\. [Online]. Available: arXiv:1812.05429
  * [71] A. Taha, M. Alrabeiah, and A. Alkhateeb, “Enabling large intelligent surfaces with compressive sensing and deep learning,” Apr. 2019. [Online]. Available: arXiv:1904.10136
  * [72] C. Huang, G. C. Alexandropoulos, A. Zappone, M. Debbah, and C. Yuen, “Energy efficient multi-user MISO communication using low resolution large intelligent surfaces,” in _Proc. IEEE Global Commun. Conf._ , Abu Dhabi, UAE, Dec. 2018.
  * [73] (2018) World’s first 5G NR radio. [Online]. Available: https://www.ericsson.com/en/networks/offerings/5g/5g-nr-radio
  * [74] C. Liaskos, S. Nie, A. Tsioliaridou, A. Pitsillides, S. Ioannidis, and I. Akyildiz, “A novel communication paradigm for high capacity and security via programmable indoor wireless environments in next generation wireless systems,” _Ad Hoc Networks_ , vol. 87, pp. 1 – 16, May 2019.
  * [75] C. Liaskos, A. Tsioliaridou, S. Nie, A. Pitsillides, S. Ioannidis, and I. Akyildiz, “Modeling, simulating and configuring programmable wireless environments for multi-user multi-objective networking,” Dec. 2018. [Online]. Available: arXiv:1812.11429
  * [76] C. Huang, A. Zappone, M. Debbah, and C. Yuen, “Achievable rate maximization by passive intelligent mirrors,” in _Proc. 2018 IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP)_ , Calgary, Canada, Apr. 2018, pp. 3714–3718.
  * [77] Q. Wu and R. Zhang, “Intelligent reflecting surface enhanced wireless network: Joint active and passive beamforming design,” in _Proc. IEEE Global Commun. Conf._ , Abu Dhabi, UAE, Dec. 2018.
  * [78] ——, “Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming,” Oct. 2018. [Online]. Available: arXiv:1810.03961v2
  * [79] ——, “Beamforming optimization for intelligent reflecting surface with discrete phase shifts,” in _Proc. 2019 IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP)_ , Brighton, UK, May 2019. [Online]. Available: arXiv:1810.10718
  * [80] M.-A. Badiu and J. P. Coon, “Communication through a large reflecting surface with phase errors,” June 2019. [Online]. Available: arXiv:1906.10751
  * [81] X. Yu, D. Xu, and R. Schober, “MISO wireless communication systems via intelligent reflecting surfaces,” Apr. 2019. [Online]. Available: arXiv:1904.12199
  * [82] D. Mishra and H. Johansson, “Channel estimation and low-complexity beamforming design for passive intelligent surface assisted MISO wireless energy transfer,” in _Proc. 2019 IEEE Int. Conf. Acoustics, Speech Signal Process. (ICASSP)_ , Brighton, UK, May 2019.
  * [83] T. Jiang and Y. Shi, “Over-the-air computation via intelligent reflecting surfaces,” Apr. 2019. [Online]. Available: arXiv:1904.12475
  * [84] X. Yu, D. Xu, and R. Schober, “Enabling secure wireless communications via intelligent reflecting surfaces,” Apr. 2019. [Online]. Available: arXiv:1904.09573
  * [85] J. Chen, Y.-C. Liang, Y. Pei, and H. Guo, “Intelligent reflecting surface: A programmable wireless environment for physical layer security,” May 2019. [Online]. Available: arXiv:1905.03689
  * [86] M. Cui, G. Zhang, and R. Zhang, “Secure wireless communication via intelligent reflecting surface,” May 2019. [Online]. Available: arXiv:1905.10770
  * [87] C. Liaskos, A. Tsioliaridou, A. Pitilakis, G. Pirialakos, O. Tsilipakos, A. Tasolamprou, N. Kantartzis, S. Ioannidis, M. Kafesaki, A. Pitsillides, and I. Akyildiz, “Joint compressed sensing and manipulation of wireless emissions with intelligent surfaces,” Apr. 2019. [Online]. Available: arXiv:1904.10670
  * [88] C. Liaskos, A. Tsioliaridou, S. Nie, A. Pitsillides, S. Ioannidis, and I. Akyildiz, “An interpretable neural network for configuring programmable wireless environments,” May 2019. [Online]. Available: arXiv:1905.02495
  * [89] M. Jung, W. Saad, Y. Jang, G. Kong, and S. Choi, “Reliability analysis of large intelligent surfaces (LISs): Rate distribution and outage probability,” Mar. 2019. [Online]. Available: arXiv:1903.11456
  * [90] ——, “Performance analysis of large intelligent surfaces (LISs): Asymptotic data rate and channel hardening effects,” Feb. 2019. [Online]. Available: arXiv:1810.05667
  * [91] M. Jung, W. Saad, and G. Kong, “Performance analysis of large intelligent surfaces (LISs): Uplink spectral efficiency and pilot training,” Mar. 2019\. [Online]. Available: arXiv:1904.00453
  * [92] S. Nie, J. M. Jornet, and I. F. Akyildiz, “Intelligent environments based on ultra-massive MIMO platforms for wireless communication in millimeter wave and terahertz bands,” Apr. 2019. [Online]. Available: arXiv:1904.07958
  * [93] C. Valagiannopoulos, T. A. Tsiftsis, and V. Kovanis, “Metasurface-enabled interference suppression at visible-light communications,” Apr. 2019. [Online]. Available: arXiv:1904.08858
  * [94] M. Najafi and R. Schober, “Intelligent reflecting surfaces for free space optical communications,” May 2019. [Online]. Available: arXiv:1905.01094
  * [95] Y. Yang, S. Zhang, and R. Zhang, “IRS-enhanced OFDM: Power allocation and passive array optimization,” May 2019. [Online]. Available: arXiv:1905.00604
  * [96] A. Welkie, L. Shangguan, J. Gummeson, W. Hu, and K. Jamieson, “Programmable radio environments for smart spaces,” in _Proc. 16th ACM Workshop Hot Topics Netw._ , 2017, pp. 36–42.
  * [97] Z. Li, Y. Xie, L. Shangguan, R. I. Zelaya, J. Gummeson, W. Hu, and K. J. and, “Towards programming the radio environment with large arrays of inexpensive antennas,” in _Proc. 16th USENIX Symp. Netw. Systems Design Implementation_ , BOSTON, MA, USA, Feb. 2019, pp. 285–299.
  * [98] V. Arun and H. Balakrishnan, “RFocus: Practical beamforming for small devices,” May 2019. [Online]. Available: arXiv:1905.05130
  * [99] (2018) NTT DOCOMO and Metawave announce successful demonstration of 28 GHz-band 5G using world’s first meta-structure technology. [Online]. Available: https://bit.ly/2I7yApd
  * [100] K. Achouri, M. A. Salem, and C. Caloz, “General metasurface synthesis based on susceptibility tensors,” _IEEE Trans. Antennas Propag._ , vol. 63, no. 7, pp. 2977–2991, July 2015.
  * [101] A. Zappone and M. Di Renzo, “Feedback-aware design of reconfigurable intelligent surfaces in smart radio environments (in submission),” June 2019\.
  * [102] M. Di Renzo and J. Song, “Reflection probability in wireless networks with metasurface-coated environmental objects: an approach based on random spatial processes,” _EURASIP J. Wireless Commun. Netw._ , vol. 2019, no. 1, p. 99, Apr. 2019.
  * [103] A. Narayanan, S. T. V., and R. K. Ganti, “Coverage analysis in millimeter wave cellular networks with reflections,” in _Proc. 2017 IEEE Global Commun. Conf._ , Singapore, Dec. 2017, pp. 1–6.
  * [104] J. Lee and F. Baccelli, “On the effect of shadowing correlation on wireless network performance,” in _Proc. IEEE Conf. Comput. Commun._ , Honolulu, HI, USA, Apr. 2018, pp. 1601–1609.

|  Ertugrul Basar (S’09-M’13-SM’16) was born in Istanbul, Turkey, in 1985. He
received the B.S. degree (Hons.) from Istanbul University, Turkey, in 2007,
and the M.S. and Ph.D. degrees from Istanbul Technical University, Turkey, in
2009 and 2013, respectively. He is currently an Associate Professor with the
Department of Electrical and Electronics Engineering, Koç University,
Istanbul, Turkey and the director of Communications Research and Innovation
Laboratory (CoreLab). His primary research interests include MIMO systems,
index modulation, waveform design, visible light communications, and signal
processing for communications. Recent recognition of his research includes the
Science Academy (Turkey) Young Scientists (BAGEP) Award in 2018, Mustafa
Parlar Foundation Research Encouragement Award in 2018, Turkish Academy of
Sciences Outstanding Young Scientist (TUBA-GEBIP) Award in 2017, and the
first-ever IEEE Turkey Research Encouragement Award in 2017. Dr. Basar
currently serves as an Editor of the IEEE Transactions on Communications and
Physical Communication (Elsevier), and as an Associate Editor of the IEEE
Communications Letters. He served as an Associate Editor for the IEEE Access
from 2016 to 2018.
---|---
|  Marco Di Renzo (S’05-AM’07-M’09-SM’14) was born in L’Aquila, Italy, in
1978. He received the Laurea (cum laude) and Ph.D. degrees in electrical
engineering from the University of L’Aquila, Italy, in 2003 and 2007,
respectively, and the Habilitation à Diriger des Recherches (Doctor of
Science) degree from University Paris-Sud, France, in 2013. Since 2010, he has
been a Chargé de Recherche CNRS (CNRS Associate Professor) in the Laboratory
of Signals and Systems (L2S) of Paris-Saclay University - CNRS,
CentraleSupélec, Univ Paris Sud, Paris, France. He serves as the Associate
Editor-in-Chief of IEEE Communications Letters, and as an Editor of IEEE
Transactions on Communications, and IEEE Transactions on Wireless
Communications. He is a Distinguished Lecturer of the IEEE Vehicular
Technology Society and IEEE Communications Society. He is a recipient of
several awards, including the 2013 IEEE-COMSOC Best Young Researcher Award for
Europe, Middle East and Africa, the 2013 NoE-NEWCOM Best Paper Award, the
2014-2015 Royal Academy of Engineering Distinguished Visiting Fellowship, the
2015 IEEE Jack Neubauer Memorial Best System Paper Award, the 2015-2018 CNRS
Award for Excellence in Research and Ph.D. Supervision, the 2016 MSCA Global
Fellowship (declined), the 2017 SEE-IEEE Alain Glavieux Award, the 2018 IEEE-
COMSOC Young Professional in Academia Award, and 7 Best Paper Awards at IEEE
conferences (2012 and 2014 IEEE CAMAD, 2013 IEEE VTC-Fall, 2014 IEEE ATC, 2015
IEEE ComManTel, 2017 IEEE SigTelCom, EAI 2018 INISCOM, IEEE ICC 2019).
---|---
|  Julien De Rosny received the M.S. and Ph.D. degrees in wave physics from
University Pierre and Marie Curie, Paris, France, in 1996 and 2000,
respectively. He held a Postdoctoral position with the Scripps Research
Institute, CA, USA, from 2000 to 2001. In 2001, he joined with the Laboratoire
Ondes et Acoustique, CNRS, France. Since 2014, he has been a CNRS Senior
Scientist with the Institut Langevin, Paris, France. His research interests
include telecommunications in complex media, acoustic, and electromagnetic
waves-based imaging.
---|---
|  Mérouane Debbah (S’01-M’04-SM’08 -F’15) received the M.Sc. and Ph.D.
degrees from the Ecole Normale Supérieure Paris-Saclay, France. He was with
Motorola Labs, Saclay, France, from 1999 to 2002, and also with the Vienna
Research Center for Telecommunications, Vienna, Austria, until 2003. From 2003
to 2007, he was an Assistant Professor with the Mobile Communications
Department, Institut Eurecom, Sophia Antipolis, France. From 2007 to 2014, he
was the Director of the Alcatel-Lucent Chair on Flexible Radio. Since 2007, he
has been a Full Professor with CentraleSupelec, Gif-sur-Yvette, France. Since
2014, he has been a Vice-President of the Huawei France Research Center and
the Director of the Mathematical and Algorithmic Sciences Lab. He has managed
8 EU projects and more than 24 national and international projects. His
research interests lie in fundamental mathematics, algorithms, statistics,
information, and communication sciences research. He is an IEEE Fellow, a WWRF
Fellow, and a Membre émérite SEE. He was a recipient of the ERC Grant MORE
(Advanced Mathematical Tools for Complex Network Engineering) from 2012 to
2017. He was a recipient of the Mario Boella Award in 2005, the IEEE Glavieux
Prize Award in 2011, and the Qualcomm Innovation Prize Award in 2012. He
received 20 best paper awards, among which the 2007 IEEE GLOBECOM Best Paper
Award, the Wi-Opt 2009 Best Paper Award, the 2010 Newcom++ Best Paper Award,
the WUN CogCom Best Paper 2012 and 2013 Award, the 2014 WCNC Best Paper Award,
the 2015 ICC Best Paper Award, the 2015 IEEE Communications Society Leonard G.
Abraham Prize, the 2015 IEEE Communications Society Fred W. Ellersick Prize,
the 2016 IEEE Communications Society Best Tutorial Paper Award, the 2016
European Wireless Best Paper Award, the 2017 Eurasip Best Paper Award, the
2018 IEEE Marconi Prize Paper Award, the 2019 IEEE Communications Society
Young Author Best Paper Award and the Valuetools 2007, Valuetools 2008,
CrownCom 2009, Valuetools 2012, SAM 2014, and 2017 IEEE Sweden VT-COM-IT Joint
Chapter best student paper awards. He is an Associate Editor-in-Chief of the
journal Random Matrix: Theory and Applications. He was an Associate Area
Editor and Senior Area Editor of the IEEE Transactions on Signal Processing
from 2011 to 2013 and from 2013 to 2014, respectively. He is the co-founder of
Ximinds and Ulanta.
---|---
|  MOHAMED-SLIM ALOUINI (S’94-M’98- SM’03-F’09) was born in Tunis, Tunisia. He
received the Ph.D. degree in electrical engineering from the California
Institute of Technology (Caltech), Pasadena, CA, USA, in 1998. He has served
as a Faculty Member with the University of Minnesota, Minneapolis, MN, USA,
and with the Texas A&M University at Qatar, Education City, Doha, Qatar,
before joining the King Abdullah University of Science and Technology (KAUST),
Thuwal, Saudi Arabia, as a Professor of electrical engineering, in 2009. His
current research interests include the modeling, design, and performance
analysis of wireless communication systems
---|---
|  Rui Zhang Rui Zhang (S’00-M’07-SM’15-F’17) received the B.Eng. (first-class
Hons.) and M.Eng. degrees from the National University of Singapore,
Singapore, and the Ph.D. degree from the Stanford University, Stanford, CA,
USA, all in electrical engineering. From 2007 to 2010, he worked as a Research
Scientist with the Institute for Infocomm Research, ASTAR, Singapore. Since
2010, he has joined the Department of Electrical and Computer Engineering,
National University of Singapore, where he is now a Dean’s Chair Associate
Professor in the Faculty of Engineering. He has authored over 300 papers. He
has been listed as a Highly Cited Researcher (also known as the World’s Most
Influential Scientific Minds), by Thomson Reuters since 2015. His research
interests include wireless information and power transfer, drone
communication, wireless eavesdropping and spoofing, energy-efficient and
energy-harvesting-enabled wireless communication, multiuser MIMO, cognitive
radio, and optimization methods. He was the recipient of the 6th IEEE
Communications Society Asia-Pacific Region Best Young Researcher Award in
2011, and the Young Researcher Award of National University of Singapore in
2015. He was the co-recipient of the IEEE Marconi Prize Paper Award in
Wireless Communications in 2015, the IEEE Communications Society Asia-Pacific
Region Best Paper Award in 2016, the IEEE Signal Processing Society Best Paper
Award in 2016, the IEEE Communications Society Heinrich Hertz Prize Paper
Award in 2017, the IEEE Signal Processing Society Donald G. Fink Overview
Paper Award in 2017, and the IEEE Technical Committee on Green Communications
& Computing (TCGCC) Best Journal Paper Award in 2017. His coauthored paper
received the IEEE Signal Processing Society Young Author Best Paper Award in
2017. He served for over 30 international conferences as TPC Co-Chair or
Organizing Committee Member, and as the guest editor for 3 special issues in
IEEE Journal of Selected Topics in Signal Processing and IEEE Journal on
Selected Areas in Communications. He was an elected member of the IEEE Signal
Processing Society SPCOM (2012-2017) and SAM (2013-2015) Technical Committees,
and served as the Vice Chair of the IEEE Communications Society Asia-Pacific
Board Technical Affairs Committee (2014-2015). He served as an Editor for the
IEEE Transactions on Wireless Communications (2012-2016), the IEEE Journal on
Selected Areas in Communications: Green Communications and Networking Series
(2015-2016), and the IEEE Transactions on Signal Processing (2013-2017). He is
now an Editor for the IEEE Transactions on Communications and the IEEE
Transactions on Green Communications and Networking. He serves as a member of
the Steering Committee of the IEEE Wireless Communications Letters.
---|---