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Future wireless networks are expected to evolve toward an intelligent and software reconfigurable paradigm enabling ubiquitous communications between humans and mobile devices. They will also be capable of sensing, controlling, and optimizing the wireless environment to fulfill the visions of low-power, high-throughput, massively-connected, and low-latency communications. A key conceptual enabler that is recently gaining increasing popularity is the HMIMOS that refers to a low-cost transformative wireless planar structure comprised of sub-wavelength metallic or dielectric scattering particles, which is capable of shaping electromagnetic waves according to desired objectives. In this article, we provide an overview of HMIMOS communications including the available hardware architectures for reconfiguring such surfaces, and highlight the opportunities and key challenges in designing HMIMOS-enabled wireless communications.
Holographic MIMO Surfaces for 6G Wireless
Networks: Opportunities, Challenges, and Trends
Chongwen Huang, Sha Hu, George C. Alexandropoulos, Alessio Zappone, Chau Yuen, Rui Zhang, Marco Di
Renzo, and M´
erouane Debbah
Abstract—Future wireless networks are expected to evolve
towards an intelligent and software reconfigurable paradigm
enabling ubiquitous communications between humans and mobile
devices. They will also be capable of sensing, controlling, and
optimizing the wireless environment to fulfill the visions of low-
power, high-throughput, massively-connected, and low-latency
communications. A key conceptual enabler that is recently
gaining increasing popularity is the Holographic Multiple Input
Multiple Output Surface (HMIMOS) that refers to a low-cost
transformative wireless planar structure comprising of sub-
wavelength metallic or dielectric scattering particles, which is
capable of shaping electromagnetic waves according to desired
objectives. In this article, we provide an overview of HMIMOS
communications including the available hardware architectures
for reconfiguring such surfaces, and highlight the opportunities
and key challenges in designing HMIMOS-enabled wireless
Future wireless networks, namely beyond their fifth Gener-
ation (5G) and sixth Generation (6G), are required to support
massive numbers of users with increasingly demanding Spec-
tral Efficiency (SE) and Energy Efficiency (EE) requirements
[1]–[4]. In recent years, research in wireless communications
has witnessed rising interests in massive Multiple Input Mul-
tiple Output (MIMO) systems, where Base Stations (BSs) are
equipped with large antenna arrays, as an innovative way to
address the 5G throughput requirements. However, it is still a
very challenging task to realize massive MIMO BSs with truly
large-scale antenna arrays (i.e., with a few hundreds or more
antennas) mainly due to the high fabrication and operational
costs, as well as the increased power consumption.
Future 6G wireless communication systems are expected
to realize an intelligent and software reconfigurable paradigm,
where all parts of device hardware will adapt to the changes of
the wireless environment [1], [3], [5]. Beamforming-enabled
antenna arrays, cognitive spectrum usage, as well as adaptive
C. Huang and Y. Chau are with the Singapore University of Technology
and Design, 487372, Singapore.
S. Hu is with Huawei Technologies Sweden AB, Sweden.
G. C. Alexandropoulos is with the Department of Informatics and Telecom-
munications, National and Kapodistrian University of Athens, Panepistimiopo-
lis Ilissia, 15784 Athens, Greece.
A. Zappone is with DIEI, University of Cassino and Southern Lazio, Via
G. Di Biasio 43, 03043, Cassino, Italy. He is also with Consorzio Nazionale
Interuniversitario per le Telecomunicazioni (CNIT), V.le G.P. Usberti 181/A,
43124, Parma, Italy.
R. Zhang is with the National University of Singapore, 119077, Singapore.
M. Di Renzo is with Universit´
e Paris-Saclay, CNRS and CentraleSup´
Laboratoire des Signaux et Syst`
emes, Gif-sur-Yvette, France.
M. Debbah is with Universit´
e Paris-Saclay, CNRS and CentraleSup´
Laboratoire LANEAS, Gif-sur-Yvette, France. He is also with the Math-
ematical and Algorithmic Sciences Lab, Paris Research Center, Huawei
Technologies France SASU, 92100 Boulogne-Billancourt, France.
modulation and coding are a few of the transceiver aspects that
are currently tunable in order to optimize the communication
efficiency. However, in this optimization process, the wire-
less environment remains an unmanageable factor; it remains
unaware of the communication process undergoing within it
[1], [3]–[9]. Furthermore, the wireless environment has in
general a harmful effect on the efficiency of wireless links. The
signal attenuation limits the connectivity radius of nodes, while
multipath propagation resulting in fading phenomena is a well-
studied physical factor introducing drastic fluctuations in the
received signal power. The signal deterioration is perhaps
one of the major concerns in millimeter wave and in the
forthcoming TeraHertz (THz) communications [1].
Although massive MIMO, three-Dimensional (3D) beam-
forming, and their hardware efficient hybrid analog and digital
counterparts [10] provide remarkable approaches to counteract
signal attenuation due to wireless propagation via software-
based control of the directivity of transmissions, they impose
mobility and hardware scalability issues. More importantly,
the intelligent manipulation of the ElectroMagnetic (EM)
propagation is only partially feasible since the objects in
the deployment area, other than the transceivers, are uncon-
trollable. As a result, the wireless environment as a whole
remains unaware of the ongoing communications within it, and
the channel model continues to be treated as a probabilistic
process, rather than a nearly deterministic one enabled through
software-controlled techniques.
Following recent breakthroughs on the fabrication of pro-
grammable metamaterials, reconfigurable intelligent surfaces
have the potential to fulfill the challenging vision for 6G
networks, and materialize seamless connections and intelli-
gent software-based control of the environment in wireless
communication systems when deployed on the surfaces of
various objects [5]–[8]. By leveraging this advancement, holo-
graphic MIMO Surfaces (HMIMOS) aim at going beyond
massive MIMO, being based on low cost, size, weight, and
low power consumption hardware architectures that provide
a transformative means for turning the wireless environment
into a programmable smart entity [3], [5], [8], [9], [11], [12].
In this article, we overview the different emerging HMIMOS
architectures and their core functionalities, and discuss their
currently considered communication applications as well as
their future networking challenges.
In this section, we present available hardware architectures,
fabrication methodologies, and operation modes of HMIMOS
systems that render them a flexibly integrable concept for
diverse wireless communication applications.
Figure 1: The two generic steps of holographic training and holographic communication [13].
A. Categorization Based on Power Consumption
1) Active HMIMOS: To realize reconfigurable wireless
environments, HMIMOS can serve as a transmitter, receiver,
or reflector. When the transceiver role is considered, and thus
energy-intensive Radio Frequency (RF) circuits and signal
processing units are embedded in the surface, the term active
HMIMOS is adopted [13], [14]. On another note, active
HMIMOS systems comprise a natural evolution of conven-
tional massive MIMO systems, by packing more and more
software-controlled antenna elements onto a two-Dimensional
(2D) surface of finite size. In [4], where the spacing between
adjacent surface elements reduces when their number increase,
an active HMIMOS is also termed as Large Intelligent Surface
(LIS). A practical implementation of active HMIMOS can be a
compact integration of a large number of tiny antenna elements
with reconfigurable processing networks realizing a continuous
antenna aperture. This structure can be used to transmit and
receive communication signals across the entire surface by
leveraging the hologram principle [13], [14]. Another active
HMIMOS implementation can be based on discrete photonic
antenna arrays that integrate active optical-electrical detectors,
converters, and modulators for performing transmission, recep-
tion, and conversion of optical or RF signals [13].
2) Passive HMIMOS: Passive HMIMOS, also known as
Reconfigurable Intelligent Surface (RIS) [3], [5]–[7], [9], or
Intelligent Reflecting Surface (IRS) [8], [15], acts like a pas-
sive metal mirror or ‘wave collector,’ and can be programmed
to change an impinging EM field in a customizable way [3],
[5]. Compared with its active counterpart, a passive HMIMOS
is usually composed of low cost passive elements that do not
require dedicated power sources. Their circuitry and embedded
sensors can be powered with energy harvesting modules, an
approach that has the potential of making them truly energy
neutral. Regardless of their specific implementations, what
makes the passive HMIMOS technology attractive from an
energy consumption standpoint, is their capability to shape ra-
dio waves impinging upon them and forwarding the incoming
signal without employing any power amplifier nor RF chain,
and also without applying sophisticated signal processing.
Moreover, passive HMIMOS can work in full duplex mode
without significant self interference or increased noise level,
and require only low rate control link or backhaul connections.
Finally, passive HMIMOS structures can be easily integrated
into the wireless communication environment, since their
extremely low power consumption and hardware costs allow
them to be deployed into building facades, room and factory
ceilings, laptop cases, or even human clothing [3], [5].
B. Categorization Based on Hardware Structure
1) Contiguous HMIMOS: A contiguous HMIMOS inte-
grates a virtually uncountably infinite number of elements into
a limited surface area in order to form a spatially continuous
transceiver aperture [13], [14]. For a better understanding of
the operation of contiguous surfaces and their communication
models, we commence with a brief description of the physical
operation of the optical holography concept. Holography is
a technique that enables an EM field, which is generally the
result of a signal source scattered off objects, to be recorded
based on the interference principle of the EM wave. The
recorded EM field can be then utilized for reconstructing the
initial field based on the diffraction principle. It should be
noted that wireless communications over a continuous aperture
is inspired by the optical holography, which is sketched in
Fig. 1. In the training phase, the generated training signals
from an RF source are split via a beamsplitter into two waves,
the object and reference waves. The object wave is directed
to the object and some of the reflected wave, which is mixed
together with the reference wave beam that does not impinge
on the object, is fed to the HMIMOS. In the communication
phase, the transmitted signal is transformed into the desired
beam to the target user over the spatially continuous aperture
of the HMIMOS. Since a continuous aperture benefits from the
integration of a theoretical infinite number of antennas which
can be viewed as the asymptotic limit of Massive MIMO, its
potential advantages include achieving higher spatial resolu-
tion, and enabling the creation and detection of EM waves with
Figure 2: The two operation modes of HMIMOS systems along with their implementation and hardware structures. A schematic
view of the HMIMOS functions of EM field polarization, scattering, focusing, and absorption control is provided.
arbitrary spatial frequency components, without undesired side
2) Discrete HMIMOS: A discrete HMIMOS is usually
composed of many discrete unit cells made of low power
software-tunable metamaterials. The means to electronically
modify the EM properties of the unit cells range from off
the shelves electronic components to using liquid crystals,
microelectromechanical systems or even electromechanical
switches, and other reconfigurable metamaterials. This struc-
ture is substantially different from the conventional MIMO an-
tenna array. One embodiment of a discrete surface is based on
discrete ‘meta-atoms’ with electronically steerable reflection
properties [6]. As mentioned earlier, another type of discrete
surface is the active one based on photonic antenna arrays.
Compared with contiguous HMIMOS, discrete HMIMOS have
some essential differences from the perspectives of implemen-
tation and hardware, as will be described in the sequel.
C. Fabrication Methodologies
There are various fabrication techniques for HMIMOS
including electron beam lithography at optical frequencies,
focused-ion beam milling, interference and nanoimprint lithog-
raphy, as well as direct laser writing or printed circuit board
processes at microwaves. Usually, these fabrication techniques
will be ascribed to produce two typical apertures, continuous
or discrete apertures, as shown in Fig. 2. A fabrication
approach leveraging programmable metamaterials for approx-
imately realizing a continuous microwave aperture [13], [14]
is depicted in Fig. 2(a). This meta-particle structure uses the
varactor loading technique to broaden its frequency response
range, and achieves continuous aperture and controllable re-
flection phase. It is a continuous monolayer metallic structure,
and comprises a large number of meta-particles. Each meta-
particle contains two metallic trapezoid patches, a central
continuous strip, and varactor diodes. By independently and
continuously controlling the bias voltage of the varactors, the
surface impedance of continuous HMIMOS can be dynami-
cally programmed, and thus manipulate the reflection phase,
amplitude states, and the phase distribution over a wide range
of frequency bands [1]. It should be highlighted that this
impedance pattern is a map of the hologram, and can be
calculated directly from the field distribution of the provided
reference wave and reflected object wave, as discussed in
Fig. 1. Exploiting intelligent control algorithms, beamforming
can be accomplished by using the hologram principle.
In contrast to continuous apertures, another instance of
HMIMOS is a realization based on discrete apertures that are
usually realized with software-defined metasurface antennas.
A general logical structure (regardless of its physical charac-
teristics) was proposed in [6], as shown in Fig. 2(b). Its general
unit cell structure contains a metamaterial layer, sensing and
actuation layers, shielding layer, computing layer, as well as
an interface and communications layer with different objec-
tives. Specifically, the meta-material layer is implemented by
graphene materials for delivering the desired EM behavior
through a reconfigurable pattern, while the objective of sensing
and actuation layer is to modify the behavior of the meta-
material layer. The shielding layer is made of a simple metallic
layer for decoupling the EM behavior of the top and bottom
layers to avoid mutual interferences. The computing layer is
used to execute external commands from the interface layer or
sensors. Finally, the interface and communications layer aim at
coordinating the actions of the computing layer and updating
other external wireless entities via the reconfigurable interface.
While the development of HMIMOS is in its infancy, basic
prototyping work on different kinds of this technology is
available already. A discrete HMIMOS was developed by
the start-up company “Greenerwave”, which shows the basic
feasibility and effectiveness of the HMIMOS concept using
discrete metasurface antennas. In contrast, another start-up
company “Pivotalcommware” with the investment of Bill
Gates capital is developing initial commercial products of a
contiguous HMIMOS based on the low-cost and contiguous
metasurfaces, which further verifies the feasibility of the
HMIMOS concept as well as the advancement of holographic
technologies. Continued prototyping development is highly
desired to prove the HMIMOS concept with brand new holo-
graphic beamforming technologies and to discover potentially
new issues that urgently need research.
D. Operation Modes
The following four operation modes for HMIMOS are
usually considered: 1) continuous HMIMOS as an active
transceiver; 2) discrete HMIMOS as a passive reflector; 3)
discrete HMIMOS as an active transceiver; and 4) continuous
HMIMOS as a passive reflector. Given the recent research
interests and due to the space limitation, we elaborate on the
first two representative modes of operation, which are also
sketched in Fig. 2.
1) Continuous HMIMOS as Active Transceivers: Accord-
ing to this mode of operation, a continuous HMIMOS func-
tions as an active transceiver. The RF signal is generated at
its backside and propagates through a steerable distribution
network to the contiguous surface constituted by a large num-
ber of software-defined and electronically steerable elements
that generate multiple beams to the intended users. A distinct
difference between active continuous HMIMOS and passively
reconfigurable HMIMOS is that the beamforming process of
the former is accomplished based on the holographic concept,
which is a new dynamic beamforming technique based on
software-defined antennas with low cost/weight, compact size,
and a low-power hardware architecture.
2) Discrete HMIMOS as Passive Reflectors: Another op-
eration mode of HMIMOS is the mirror or ‘wave collector,’
where the HMIMOS is considered to be discrete and passive.
In this case, an HMIMOS constitutes reconfigurable unit cells,
as previously described, which makes their beamforming mode
resembling that of conventional beamforming [10], unlike
continuous transceiver HMIMOS systems. It is worth noting
that most of the existing works (e.g., [5], [7], [8]) focus on
this HMIMOS operation mode which is simpler to implement
and analyze.
Different fabrication methods of HMIMOS systems result
in a variety of functionalities and characteristics, with most
of them being very relevant to the expectations for future 6G
wireless systems (e.g., Tbps peak rates). In this section, we
highlight the HMIMOS functions and key characteristics, and
discuss their diverse wireless communications applications.
A. Functionality Types
Intelligent surfaces can support a wide range of EM in-
teractions, termed hereinafter as functions. Ascribing to their
programmable features and depending on whether they are
realized via structures with discrete or continuous elements,
HMIMOS have four common function types as illustrated in
the bottom part of Fig. 2:
F1: EM Field Polarization, which refers to the reconfig-
urable setting of the oscillation orientation of the wave’s
electric and magnetic fields.
F2: EM Field Scattering, where the surface redirects an
impinging wave with a given direction of arrival towards
a desired or multiple concurrent desired directions.
F3: Pencile-like Focusing, which takes place when an
HMIMOS acts as lens to focus an EM wave to a given
point in the near or far field. The collimation (i.e., the
reverse functionality) also belongs to this general mode
of beamforming operation.
F4: EM Field Absorption, which implements minimal
reflected and/or refracted power of the incoming EM
B. Characteristics
Compared with currently used technologies in wireless
networks, the most distinctive characteristics of the HMIMOS
concept lie in making the environment controllable by pro-
viding the possibility of fully shaping and controlling the
EM response of the environmental objects that are distributed
throughout the network. An HMIMOS structure is usually
intended to operate as a signal source or ‘wave collector’
with reconfigurable characteristics, especially for application
scenarios where it is used as a passive reflector with the
objective of improving the communication performance. The
fundamental properties of HMIMOS systems1and their core
differences with massive MIMO and conventional multi-
antenna relaying systems are summarized as follows:
C1: HMIMOS can be nearly passive. One significant
merit of passive HMIMOS is that they do not require
any internally dedicated energy source to process the
incoming information-carrying EM field.
C2: HMIMOS can realize continuous apertures. Re-
cent research activity focuses on low operational cost
methods for realizing spatially-continuous transmitting
and receiving apertures.
C3: Receiver thermal noise is absent in HMIMOS.
Passive HMIMOS do not require to down-convert the
received waveform for baseband processing. Instead they
implement analog processing directly on the impinging
EM field.
C4: HMIMOS elements are tuned in software. Avail-
able architectures for metasurfaces enable simple repro-
grammability of all settings of their unit elements.
1It should be noted that not all HMIMOS architectures have all listed
attributes. Some of them are inherent to passive HMIMOS, but not to active
ones, and vice versa. However, we discuss HMIMOS properties here in a
broad scope, including all available types up to date.
C5: HMIMOS can have full-band response. Due
to recent advances in the fabrication of metamaterials,
reconfigurable HMIMOS can operate at any operating
frequency, ranging from the acoustic spectrum up to THz
and the light spectra.
C6: Distinctive low latency implementation. HMIMOS
are based on rapidly reprogrammable meta materials,
whereas conventional relaying and massive MIMO sys-
tems rely on antenna array processing.
C. Communication Applications
The unique features of HMIMOS enabling intelligent
and rapidly reconfigurable wireless environments make them
an emerging candidate technology for low-power, high-
throughput, and low-latency 6G wireless networks. We next
discuss representative communication applications of HMI-
MOS for outdoor and indoor environments.
1) Outdoor Applications: Consider a discrete passive HMI-
MOS, as an illustrative example, which comprises a finite
number of unit elements, and is intended for forwarding
suitably phase-shifted versions of the impinging signals to
users located in different outdoor scenarios, such as typical
urban shopping malls and international airports, as illustrated
in the upper part of Fig.3. We assume that HMIMOS are
planar structures of few centimeters thickness and variable
sizes that can be easily deployed onto nearly all environmental
A1: Building connections. HMIMOS can extend the
coverage from outdoor BSs to indoor users, especially
where there is no direct link between the users and BS,
or the link is severely blocked by obstacles.
A2: Energy-efficient beamforming. HMIMOS are ca-
pable of recycling ambient EM waves and focusing them
towards intended users via effective tuning of their unit
elements. In such cases, surfaces are deployed as relays
that forward the information bearing EM field to desired
locations via efficient beamforming that compensates for
the signal attenuation from the BS or suppresses the co-
channel interference from neighboring BSs.
A3: Physical-layer security. HMIMOS can be deployed
for physical layer security in order to cancel out reflec-
tions of the BS signals to eavesdroppers.
A4: Wireless power transfer. HMIMOS can collect
ambient EM waves and direct them to low-power IoT
devices and sensors enabling simultaneous wireless in-
formation and power transfer.
2) Indoor Applications: Indoor wireless communication is
subject to rich multipath propagation due to the presence of
multiple scatters and signal blocking by walls and furniture,
as well as RF pollution due to the high density of electronic
devices in confined spaces. As such, providing ubiquitous high
throughput indoor coverage and localization is a challenging
task. HMIMOS has the potential of being highly beneficial
in indoor environments, capitalizing on its inherent capability
to reconfigure EM waves towards various communication
objectives. An illustrative example is sketched in the lower part
of Fig. 3. In the left corner of this example where an HMIMOS
is absent, the signal experiences pathloss and multipath fading
due to refraction, reflection, and diffusion, which deteriorates
the signal strength to the target user. However, in the right
corner of Fig. 3, signal propagation can be boosted using
HMIMOS coated in the wall so as to assist the signal from
the access point to reach the intended user with the desired
power level.
A5: Enhanced in-building coverage: As previously
discussed, indoor environments can be coated with HMI-
MOS to increase the throughput offered by conventional
Wi-Fi access points.
A6: High accurate indoor positioning: HMIMOS has
increased potential for indoor positioning and localiza-
tion, where the conventional Global Positioning System
(GPS) fails to provide the desired accuracy or cannot
work. Large surfaces offer large, and possibly continuous,
apertures that enable increased spatial resolution.
There has been lately increasing research interest in wireless
communication systems incorporating HMIMOS. In Table I,
we list some of the recent works dealing with different
combinations among the functionalities of HMIMOS, their
characteristics, and communication applications.
In this section, we present some theoretical and practical
challenges in HMIMOS-based communication systems.
A. Fundamental Limits
It is natural to expect that wireless communication systems
incorporating HMIMOS will exhibit different features com-
pared with traditional communications based on conventional
multi-antenna transceivers. Recall that current communica-
tion systems operate over uncontrollable wireless environ-
ments, whereas HMIMOS-based systems will be capable of
reconfiguring the EM propagation. This fact witnesses the
need for new mathematical methodologies to characterize the
physical channels in HMIMOS-based systems and analyze
their ultimate capacity gains [14], as well as for new signal
processing algorithms and networking schemes for realizing
HMIMOS-assisted communication. For example, continuous
HMIMOS is used for the reception and transmission of the
impinging EM field over its continuous aperture using the
hologram concept. Different from the massive MIMO systems,
HMIMOS operation can be described by the Fresnel-Kirchhoff
integral that is based on the Huygens-Fresnel principle [9].
B. HMIMOS Channel Estimation
The estimation of possibly very large MIMO channels in
HMIMOS-based communication systems is another critical
challenge due to the various constraints accompanying the
available HMIMOS hardware architectures. Most of the few
currently available approaches mainly consider large time
periods for training all HMIMOS unit elements via pilots sent
from the BS and received at the user equipment via generic
reflection. Another family of techniques employs compressive
sensing and deep learning via online beam/reflection training
for channel estimation and design of the phase matrix [12].
Figure 3: Wireless communication applications of HMIMOS in outdoor and indoor environments.
However, this mode of operation requires large amount of
training data, and employs fully digital or hybrid analog and
digital transceiver architectures for HMIMOS, which results
in increased hardware complexity and power consumption.
C. Robust Channel-Aware Beamforming
Channel dependent beamforming has been extensively in-
vestigated in massive MIMO systems. However, realizing
environment-aware designs in HMIMOS-based communica-
tion systems is extremely challenging, since the HMIMOS unit
cells that are fabricated from metamaterials impose demanding
tuning constraints. The latest HMIMOS design formulations
(e.g., [5], [8]) include large numbers of reconfigurable param-
eters with non-convex constraints rendering their optimal solu-
tion highly non-trivial. For the case of continuous HMIMOS,
intelligent holographic beamforming is an approach to smartly
target and track individual or small clusters of devices, and
provide them with high-fidelity beams and smart radio man-
agement. However, self-optimizing holographic beamforming
technologies that depend on complex aperture synthesis and
low level modulation are not available yet.
D. Distributed Configuration and Resource Allocation
Consider an HMIMOS-based communication system com-
prising multiple multi-antenna BSs, multiple HMIMOS, and
massive number of users, where each user is equipped with a
single or multiple antennas. The centralized configuration of
HMIMOS will require massive amount of control information
to be communicated to a central controller, which is prohibitive
in terms of both computational overhead and energy consump-
tion. Hence, distributed algorithms for the optimal resource
allocation and beamforming, HMIMOS configurations, and
users’ scheduling need to be developed. Additional optimiza-
tion parameters complicating the network optimization are
anticipated to be the power allocation and spectrum usage, as
well as the users’ assignment to BSs and distributed HMIMOS.
Table I: Some recent research results on HMIMOS-based wireless communication systems.
Related Works Applications Functions Characteristics Main Contributions
[1] A1, A2, A5 F2, F3 C1, C3-C6
Presented an HMIMOS-based approach to combat the distance limi-
tation in millimeter wave and THz systems; simulation results for an
indoor set up corroborated the merits of proposed approach.
Indoor [4] A2, A5, A6 F2, F3 C2-C6 Introduced an indoor signal propagation model and presented informa-
tion theoretical results for active and continuous HMIMOS systems.
[6] A1-A3, A5 F1-F4 C1, C3-C6
Introduced the concept of programmable indoor wireless environments
offering simultaneous communication and security; an indoor model
and a simulation setup for HMIMOS communication were presented.
[7] A1, A2 F2, F3 C1, C3-C6
Designed a 0.4m2and 1.5mm thick planar metasurface consisting
of 102 controllable unit cells operating at 2.45GHz; demonstrated
increased received signal strength when deployed indoor.
[9] A1, A2, A5 F2, F3 C3-C6
Proposed free space pathloss models using the EM and physical prop-
erties of a reconfigurable surface; indoor field experiments validated
the proposed models.
[5] A1, A2 F2, F3 C1, C3-C6
Proposed HMIMOS for outdoor MIMO communications and presented
EE maximization algorithms; studied the fundamental differences
between HMIMOS and conventional multi-antenna relays.
[8] A2, A4 F2, F3 C1, C3-C6
Presented jointly active and passive beamforming algorithms for
HMIMOS-assisted MIMO communication; analyzed the interference
distribution and studied the power scaling law.
Outdoor [11] A2 F2, F3 C1, C3-C6
Derived the optimal HMIMOS phase matrix for the case of available
statistical channel information and presented a tight approximation for
the ergodic capacity.
[12] A1, A2 F1-F4 C1, C3-C6 Studied compressive sensing and deep learning approaches for HMI-
MOS channel estimation and online configuration.
Naturally, the more HMIMOS are incorporated in the network,
the more challenging the algorithmic design will be.
In this section, we study the performance of HMIMOS in
two typical application scenarios: indoor positioning with an
active continuous HMIMOS and outdoor downlink communi-
cation assisted by a passive discrete HMIMOS.
A. Indoor Positioning with an Active Continuous HMIMOS
We assume an active HMIMOS where the distance between
any of each two adjacent unit elements is λ/2, with λbeing
the carrier wavelength. In such a discrete setup, traditional
MIMO, massive MIMO, and HMIMOS are unified, and the
differences lie in the number of antenna elements used, i.e., the
surface area. It was shown in [4] that the number of antennas
in a traditional massive MIMO system for a given surface area
πR2is equal to πR2
/(λ2/4)=πτ z2/(λ2/4)
=20106τ, when
z= 4m,λ= 0.1m, and τ,(R/z)2(the normalized surface
area). A typical massive MIMO array comprising of N=200
antennas results in τ0.01, while an active HMIMOS
typically increases the surface area (so as τ) by 10 20
times [4]. In Fig. 4a, the Cram´
er–Rao Lower Bounds (CLRBs)
of user positioning in the presence of phase uncertainty are
illustrated. As depicted, the CRLB of positioning decreases
linearly with τfor traditional MIMO, while massive MIMO
falls short in reaching the cubic decreasing slope that is
achieved by the active HMIMOS, yielding significant gains
in user positioning.
B. EE Maximization with a Passive Discrete HMIMOS
We consider an outdoor 16-antenna BS simultaneously serv-
ing 16 single-antenna users in the downlink communication
using a discrete passive HMIMOS with 32 unit elements that is
attached to a surrounding building’s facade [5]. The simulation
parameters are shown in Table II [5]. The obtained EE
performance using an approach based on Sequential Fractional
Programming (SFP), as well as a gradient descent approach
tuning the HMIMOS system is shown in Fig.4b as a function
of the maximum BS transmit power Pmax. We have also
numerically evaluated the EE of conventional Amplify-and-
Forward (AF) relaying. It is shown that the HMIMOS-assisted
system achieves a three-fold increase in EE compared to the
AF relaying case when Pmax 32dBm. In this case, the EE
saturates, which reveals that the excess BS transmit power
should not be used because it would decrease EE.
In this article, we investigated the emerging concept of
HMIMOS wireless communication, and in particular the avail-
able HMIMOS hardware architectures, their functionalities
and characteristics, as well as their recent communication
applications. We highlighted their great potential as a key
enabling technology for the physical layer of future 6G wire-
less networks. HMIMOS technology offers fertile advantages
in terms of SE and EE, yielding smart and reconfigurable
wireless environments. HMIMOS technology reduces the cost,
size, and energy consumption of network devices, providing
ubiquitous coverage and intelligent communication in both
indoor and outdoor scenarios. Benefiting from its merits,
HMIMOS can be compactly and easily integrated into a
wide variety of applications. Representative use cases are the
extension of coverage, physical-layer security, wireless power
transfer, and positioning. However, there are still challenges
ahead to achieve the full potential of this emerging technology.
This includes, among others, the realistic modeling of meta-
surfaces, the analysis of the fundamental limits of wireless
communications with multiple HMIMOS, the implementation
of intelligent environment-aware adaptation, and the channel
10-4 10-2 100102104
Massive MIMO
Traditional MIMO
(a) (b)
Figure 4: (a) CRLBs of positioning with an active HMIMOS of a radius Rfor the case where a single user is located z= 4 m
away from the center of surface. The wavelength λis 0.1m, and τrepresents the normalized surface area [4]. (b) Average
EE with HMIMOS-assisted communication versus the maximum BS transmit power Pmax in dB.
Table II: Simulation Parameters for the Average EE Performance Results in Fig. 4b.
Parameters Values Parameters Values
HMIMOS central element placement: (100 m, 100 m)Circuit dissipated power at BS: 9dBW
BS central element placement: (0 m, 0m)Circuit dissipated power coefficients at BS and AF relay: 1.2
Small scale fading model: Rayleigh Maximum transmit power at BS and AF relay Pmax:20 dBW
Large scale fading model at distance d:103.53d3.76 Dissipated power at each user: 10 dBm
Transmission bandwidth: 180 KHz Dissipated power at each HMIMOS element: 10 dBm
Algorithmic convergence parameter: = 103Dissipated power at each AF relay transmit-receive antenna: 10 dBm
estimation with nearly passive surfaces. These challenges
provide academic and industrial researchers with a gold mine
of new problems and challenges to tackle.
The work of C. Yuen was partly supported by ASTAR
under its RIE2020 Advanced Manufacturing and Engineering
(AME) Industry Alignment Fund-Pre Positioning (IAF-PP)
(Grant No. A19D6a0053). Any opinions, findings and conclu-
sions or recommendations expressed in this material are those
of the author and do not reflect the views of ASTAR. The
work of A. Zappone has been partly supported by MIUR under
the “PRIN Liquid Edge” contract and by the Italian Ministry
of Education and Research, under the program “Dipartimenti
di Eccellenza 2018-2022”. The research activity of M. Di
Renzo was supported by the European Commission through
the H2020 ARIADNE project under grant 871464.
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CHONGWEN HUAN G [M’19] (chongwen obtained his
B. Sc. degree in 2010 from Nankai University, Binhai College, M.Sc degree
from the University of Electronic Science and Technology of China (UESTC,
Chengdu) in 2013, and Ph.D. degree from Singapore University of Technology
and Design (SUTD, Singapore) in Sep. 2019. From Sep. 2019, he becomes a
post-doctoral researcher in SUTD. His main research interests are focused
on holographic MIMO surface/reconfigurable intelligence surface, 5G/6G
wireless communication, deep learning for 5G/6G Technologies, etc.
SHA HU[M’18] ( was born in Hubei, China in 1985.
He received the Ph.D. in electrical engineering from Lund University, Lund,
Sweden in Jan. 2018; the M.S. and B.S. degrees in pure mathematics from
Wuhan University, China, in Jul. 2008 and 2006, respectively. He joined
Huawei Technologies in 2008 and now works as a modem expert in Huawei
Lund research center. His research interests include applied information
theory, signal processing, MIMO detection and channel shortening, precoder
design, and machine learning in communications. He is the recipient of 2019
IEEE VT/COM/IT Sweden best student journal paper award.
the Engineering Diploma, M.A.Sc. (Hons), and Ph.D. degrees (best thesis
award) from the University of Patras, Greece in 2003, 2005, and 2010,
respectively. He is currently an Assistant Professor with the Department of
Informatics and Telecommunications, National and Kapodistrian University of
Athens, Greece. His research interests span the general areas of algorithmic
design, optimization, and performance analysis for wireless networks with
emphasis on transceiver hardware architectures, millimeter wave communi-
cations, and distributed machine learning approaches. He received the IEEE
Communications Society Best Young Professional in Industry Award 2018,
and currently serves as Editor for the IEEE Transactions on Wireless Commu-
nications, IEEE Communications Letters, and Elsevier Computer Networks.
ALE SSI O ZAP PON E [SM’16] ( received his M.Sc.
and Ph.D. both from the University of Cassino and Southern Lazio (Cassino,
Italy). In 2017 he was the recipient of the H2020 Marie Curie IF BESMART
fellowship for experienced researchers, carried out at the LANEAS group of
CentraleSupelec (Gif-sur-Yvette, France). Since 2019, he is with the Univer-
sity of Cassino and Southern Lazio. His research interests lie in the area of
communication theory and signal processing, with main focus on optimization
techniques for resource allocation and energy efficiency maximization. He
held several research appointments at international institutions. Alessio serves
as senior area editor for the IEEE Signal Processing Letters and has served
as guest editor for the IEEE Journal on Selected Areas on Communications.
CHAU YUEN [SM’13] ( received his Ph.D. degree
from Nanyang Technological University, Singapore, in 2004. He is currently
an associate professor at Singapore University of Technology and Design. He
serves as an Editor for IEEE Transactions on Communications and IEEE
Transactions on Vehicular Technology. He has two U.S. patents and has
published over 300 research papers in international journals or conferences.
His research interests include wireless communications, smart grid, and the
Internet of Things.
RUI ZH ANG [F’17] ( received his Ph.D. degree from
Stanford University in 2007 and is now a Professor in the ECE Department
of NUS. He has been listed as a Highly Cited Researcher by Thomson
Reuters since 2015. His research interests include wireless communications
and wireless power transfer. He is the co-recipient of the IEEE Marconi
Prize Paper Award in Wireless Communications, the IEEE Signal Processing
Society Best Paper Award, the IEEE Communications Society Heinrich Hertz
Prize Paper Award, and so on.
MAR CO DIREN ZO [F’20] ( received the
Laurea (cum laude) and Ph.D. degrees from University of L’Aquila, Italy, in
2003 and 2007, and the HDR degree from University Paris-Sud, France, in
2013. Since 2010, he has been with the French National Center for Scientific
Research (CNRS), where he is a Research Director (CNRS Professor) in
the Laboratory of Signals and Systems of CentraleSupelec, Paris-Saclay
University, France. He serves as the Editor-in-Chief of IEEE Communications
Letters. He is a Distinguished Lecturer of the IEEE Vehicular Technology
Society and IEEE Communications Society. He is a Highly Cited Researcher.
EROUANE DEBBA H [F’15] ( received the
M.Sc. and Ph.D. degrees from the Ecole Normale Sup´
erieure Paris-Saclay,
France. Since 2007, he has been a full professor at CentraleSup´
elec, Gif-
sur-Yvette, France. He is currently a WWRF Fellow and a member of the
academic senate of Paris-Saclay. He has managed 8 EU projects and more
than 24 national and international projects. He has received 20 best paper
awards. His research interests lie in fundamental mathematics, algorithms,
statistics, and information and communication sciences research.
... In order to realize the advantages of low cost and reduced energy consumption in IRS-aided MIMO systems, it is essential to know the integra channel state information (CSI) [11][12][13]. erefore, we denote that proceeding accurate channel estimation with reduced expense in mmWave MIMO systems is of great help to improve system performance confronting with dire challenges. Due to the reflecting elements in the IRS being passive and unable to perform signal processing [14], it is difficult to estimate the BS-IRS channel and the IRS-US channel, and this causes serious trouble in obtaining accurate channel state information. ...
... Due to the reflecting elements in the IRS being passive and unable to perform signal processing [14], it is difficult to estimate the BS-IRS channel and the IRS-US channel, and this causes serious trouble in obtaining accurate channel state information. Previous channel estimation methods based on a design of a reflecting matrix by perfect CSI have been proposed in References [11][12][13][14] but still face a lot of difficulties. In Reference [11], it is pointed out that the reflection matrix can be designed with perfect channel state information to complete the channel estimation. ...
... Previous channel estimation methods based on a design of a reflecting matrix by perfect CSI have been proposed in References [11][12][13][14] but still face a lot of difficulties. In Reference [11], it is pointed out that the reflection matrix can be designed with perfect channel state information to complete the channel estimation. In Reference [12], the article proposes a kind of hybrid precoding design for IRS-aided mmWave communication systems to acquire perfect CSI. ...
Full-text available
Integrating large intelligent reflecting surfaces (IRS) into a millimeter-wave (mmWave) massive multi-input-multi-output (MIMO) technique has been a promising approach to enhance the performance of the wireless communication system with the channel state information (CSI). Most existing work assume that ideal channel estimation can be obtained, but the proposed high-dimensional cascaded MIMO channels and passive reflectors pose a great challenge to these methods. To address the abovementioned problems, we proposed a new method for the reduction of training overhead in IRS with a partial ON/OFF model and an optimizing strategy for pilot design approach. The energy consumption of large-scale antenna arrays and the pilot overhead in the training phase of signal transmission are greatly reduced. Besides, we proposed an improved deep residual shrinkage denoising network, which possesses better denoising performance with a soft thresholding model. The channel data can be denoised by deep learning methods, which greatly improve the accuracy of channel estimation. Simulation results demonstrate that the superiority of the proposed network over prior solutions.
... On account of the previously described advantages, RISs are in fact gaining a lot of momentum in massive access scenarios [52,[59][60][61][62][63][64][65][66][67][68][69][70][71][72][73] because of their aforementioned ability to turn the stochastic nature of the wireless environment into a programmable channel. RISs have been recently proposed for a variety of applications including secure communications [67,68], nonorthogonal multiple access [69], over-the-air-computation [70], or energy-efficient cellular networks [71,72]. ...
The exponential increase of wireless user equipments (UEs) and network services associated with current 5G deployments poses several unprecedented design challenges that need to be addressed with the advent of future beyond-5G networks and novel signal processing and transmission schemes. In this regard, massive MIMO is a well-established access technology, which allows to serve many tens of UEs using the same time-frequency resources. However, massive MIMO exhibits scalability issues in massive access scenarios where the UE population is composed of a large number of heterogeneous devices. In this thesis, we propose novel scalable multiple antenna methods for performance enhancement in several scenarios of interest. Specifically, we describe the fundamental role played by statistical channel state information (CSI) that can be leveraged for reduction of both complexity and overhead for CSI acquisition, and for multiuser interference suppression. Moreover, we exploit device-to-device communications to overcome the fundamental bottleneck of conventional multicasting. Lastly, in the context of millimiter wave communications, we explore the benefits of the recently proposed reconfigurable intelligent surfaces (RISs). Thanks to their inherently passive structure, RISs allow to control the propagation environment and effectively counteract propagation losses and substantially increase the network performance.
... capacity improvement, etc. [1], [2]. Obstacles blocking the wireless links between the transmitter and receiver always raise serious concern that hinders maximizing the system performance with optimal quality of service. ...
Full-text available
Due to significant blockage conditions in wireless networks, transmitted signals may considerably degrade before reaching the receiver. The reliability of the transmitted signals, therefore, may be critically problematic due to blockages between the communicating nodes. Thanks to the ability of Reconfigurable Intelligent Surfaces (RISs) to reflect the incident signals with different reflection angles, this may counter the blockage effect by optimally reflecting the transmit signals to receiving nodes, hence, improving the wireless network's performance. With this motivation, this paper formulates a RIS-aided wireless communication problem from a base station (BS) to a mobile user equipment (UE). The BS is equipped with an RGB camera. We use the RGB camera at the BS and the RIS panel to improve the system's performance while considering signal propagating through multiple paths and the Doppler spread for the mobile UE. First, the RGB camera is used to detect the presence of the UE with no blockage. When unsuccessful, the RIS-assisted gain takes over and is then used to detect if the UE is either "present but blocked" or "absent". The problem is determined as a ternary classification problem with the goal of maximizing the probability of UE communication blockage detection. We find the optimal solution for the probability of predicting the blockage status for a given RGB image and RIS-assisted data rate using a deep neural learning model. We employ the residual network 18-layer neural network model to find this optimal probability of blockage prediction. Extensive simulation results reveal that our proposed RIS panel-assisted model enhances the accuracy of maximization of the blockage prediction probability problem by over 38\% compared to the baseline scheme.
... Physical layer security (PLS) enhancement has aroused extensive attentions in both industry and academia thanks to its superb capability of light-weight authentication on received signals against eavesdropping attacks [6]. Since the PLS takes advantage of the natural peculiarities and physical properties of propagation environment to promote the security performance of 5G communication network and secure the data transmission, it has attracted extensive attention in the academia and industry circles [7]. Several dimensions of designing PLS schemes have been identified in the literature, via nonorthogonal multiple access (NOMA) [8], artificial noise (AN), and cooperative interference [9]. ...
Full-text available
In this paper, we investigate an innovative physical layer security (PLS) scheme for an uncertain reconfigurable intelligent surface- (RIS-) assisted communication system under eavesdropping attack. Specifically, in our proposed system, we consider that the uncertain RIS is known to the legitimate user while not to the eavesdropper (Eve). In this manner, the reflective elements of the uncertain RIS are adjusted to strengthen the keen signals for the legitimate user while suppressing the signals for Eve via jamming. We analyze the system by assuming legitimate and wiretap channels, respectively, where the secrecy capacity maximization problem is formulated and its exact closed-form expression is derived. Simulation results verifying the accuracy of our analysis demonstrate the validity and superiority of the uncertain RIS-assisted communication system against its counterparts.
... The term ultra mMIMO is used in [5][6][7] for systems with hundreds or even thousands of antennas operating in mmWave and THz bands. The terms large intelligent surfaces and holographic mMIMO [8][9][10][11] are used when the arrays consist of electrically small and densely packed antennas, so that propagation effects can be studied using integrals over the aperture rather than summations of individual antennas. In this chapter, we will consider the latter situation but focus on scenarios when the array aperture is so large that the users are in the so-called near-field. ...
The number of users that can be spatially multiplexed by a wireless access point depends on the aperture of its antenna array. When the aperture increases and wavelength shrinks, "new" electromagnetic phenomena can be utilized to further enhance network capacity. In this chapter, we describe how extremely large aperture arrays (ELAA) can extend the radiative near-field region to kilometer distances. We demonstrate how this affects the propagation models in line-of-sight (LoS) scenarios and enables finite-depth beamforming. In particular, it becomes possible to simultaneously serve users that are located in the same direction but at different distances.
... Like its predecessors, 6G technology will provide substantially higher capacity, ultra-low latency and ubiquitous instant communications, and very high-data-rate connectivity per device [10,156]. The 6G system is expected to exploit higher frequencies than 5G networks, to facilitate the integration of maritime, aerial, and terrestrial communications into a sophisticated network with improved access to cutting-edge technologies such as holographic beamforming [157][158][159][160], fog/edge computing [161], tactile Internet [162], quantum communications [163], intelligent reflecting surface (IRS) [164,165], backscatter communication [166,167], artificial intelligence (AI)/machine learning (ML) [168,169], and 3-dimensional (3D) networking [170,171]. Besides a laudable increase in delivering much more data at faster rates, tackling sustainable wireless communication development issues will pose serious concerns in the 6G era. ...
Full-text available
The traditional multiple input multiple output (MIMO) systems cannot provide very high Spectral Efficiency (SE), Energy Efficiency (EE), and link reliability, which are critical to guaranteeing the desired Quality of Experience (QoE) in 5G and beyond 5G wireless networks. To bridge this gap, ultra-dense cell-free massive MIMO (UD CF-mMIMO) systems are exploited to boost cell-edge performance and provide ultra-low latency in emerging wireless communication systems. This paper attempts to provide critical insights on high EE operation and power control schemes for maximizing the performance of UD CF-mMIMO systems. First, the recent advances in UD CF-mMIMO systems and the associated models are elaborated. The power consumption model, power consumption parts, and energy maximization techniques are discussed extensively. Further, the various power control optimization techniques are discussed comprehensively. Key findings from this study indicate an unprecedented growth in high-rate demands, leading to a significant increase in energy consumption. Additionally, substantial gains in EE require efficient utilization of optimal energy maximization techniques, green design, and dense deployment of massive antenna arrays. Overall, this review provides an elaborate discussion of the research gaps and proposes several research directions, critical challenges, and useful recommendations for future works in wireless communication systems.
... Second, the deployment of reconfigurable intelligent surfaces (RISs), which are largely passive devices that can modify the propagation environment for improving various key performance indicators (KPIs) [10], [11]. Third, the introduction of other MIMO-based technologies, such as cell-free massive MIMO [12], holographic MIMO [13], and extra-large MIMO (XL-MIMO) [14]. Fourth, there are algorithmic advances, such as the use of artificial intelligence (AI) to tackle certain classes of problems that are hard to solve with model-based methods [15], as well as novel localization and mapping methods [16], analysis tools [17], and signal designs [18]. ...
Full-text available
This letter is part of a two-letter tutorial on radio localization and sensing, with a focus on mobile radio systems, i.e., 5G and beyond. Building on Part I, which focused on the fundamentals, here we go deeper into the state-of-the-art advances, as well as 6G, covering enablers and challenges related to modeling, coverage, and accuracy.
In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) secure communication system, where only legitimate user's (Bob's) statistical channel state information (CSI) can be obtained at the transmitter (Alice), while eavesdropper's (Eve's) CSI is unknown. Firstly, the analytical expression of the achievable ergodic rate at Bob is obtained. Then, by exploiting Bob's statistical CSI, we jointly design the transmit covariance matrix at Alice and the phase shift matrix at the RIS to minimize the transmit power of the information signal under the quality-of-service (QoS) constraint of Bob. Finally, we propose an artificial noise (AN)-aided method without Eve's CSI to enhance the security of this system and use the residual power to design the transmit covariance for AN. Simulation results verify the convergence of the proposed method, and also show that there exists a trade-off between the secrecy rate and QoS of Bob.
p>Symbiotic radio (SR) is a novel paradigm in cognitive ambient backscatter communication, which makes the connection of devices without the need to reallocation of the frequency spectrum and complex energy infrastructure and so, can turn a 6G network into a green communication. In the system model of this paper, the base stations (BSs) with the active antennas, symbiotic backscatter devices (SBDs) and symbiotic user equipment (SUE) are considered. The main purpose is to minimize the energy consumption (EC) in the SR network and increase energy efficiency (EE) by taking into account to fulfillment of the minimum required throughput for SBDs. To this end, we introduce a new scheduling system called Timing SR (T-SR) to optimal resource allocation between SBDs. In this system, SBDs harvest energy from ambient signals and then send their information without interfering with each other, even in simultaneous transmission, to SUE by using the carrier of its signal. The main optimization problem has non-convex objective functions and constraints. To solve it, we use the novel mathematical methods called conic quadratic representation (CQR) and sequential quadratic (SQ) techniques. Finally, simulation results demonstrate the superiority of the proposed method compared to other outlined schemes in reducing the EC.</p
Full-text available
Employing large intelligent surfaces (LISs) is a promising solution for improving the coverage and rate of future wireless systems. These surfaces comprise massive numbers of nearly-passive elements that interact with the incident signals, for example by reflecting them, in a smart way that improves the wireless system performance. Prior work focused on the design of the LIS reflection matrices assuming full channel knowledge. Estimating these channels at the LIS, however, is a key challenging problem. With the massive number of LIS elements, channel estimation or reflection beam training will be associated with (i) huge training overhead if all the LIS elements are passive (not connected to a baseband) or with (ii) prohibitive hardware complexity and power consumption if all the elements are connected to the baseband through a fully-digital or hybrid analog/digital architecture. This paper proposes efficient solutions for these problems by leveraging tools from compressive sensing and deep learning. First, a novel LIS architecture based on sparse channel sensors is proposed. In this architecture, all the LIS elements are passive except for a few elements that are active (connected to the baseband). We then develop two solutions that design the LIS reflection matrices with negligible training overhead. In the first approach, we leverage compressive sensing tools to construct the channels at all the LIS elements from the channels seen only at the active elements. In the second approach, we develop a deep-learning based solution where the LIS learns how to interact with the incident signal given the channels at the active elements, which represent the state of the environment and transmitter/receiver locations. We show that the achievable rates of the proposed solutions approach the upper bound, which assumes perfect channel knowledge, with negligible training overhead and with only a few active elements, making them promising for future LIS systems.
Full-text available
Reconfigurable intelligent surfaces (RISs) comprised of tunable unit cells have recently drawn significant attention due to their superior capability in manipulating electromagnetic waves. In particular, RIS-assisted wireless communications have the great potential to achieve significant performance improvement and coverage enhancement in a cost-effective and energy-efficient manner, by properly programming the reflection coefficients of the unit cells of RISs. In this paper, free-space path loss models for RIS-assisted wireless communications are developed for different scenarios by studying the physics and electromagnetic nature of RISs. The proposed models, which are first validated through extensive simulation results, reveal the relationships between the free-space path loss of RIS-assisted wireless communications and the distances from the transmitter/receiver to the RIS, the size of the RIS, the near-field/far-field effects of the RIS, and the radiation patterns of antennas and unit cells. In addition, three fabricated RISs (metasurfaces) are utilized to further corroborate the theoretical findings through experimental measurements conducted in a microwave anechoic chamber. The measurement results match well with the modeling results, thus validating the proposed free-space path loss models for RIS, which may pave the way for further theoretical studies and practical applications in this field.
Full-text available
Large intelligent surface (LIS)-assisted wireless communications have drawn attention worldwide. With the use of low-cost LIS on building walls, signals can be reflected by the LIS and sent out along desired directions by controlling its phases, thereby providing supplementary links for wireless communication systems. In this study, we evaluate the performance of an LIS-assisted large-scale antenna system by formulating a tight approximation of the ergodic capacity and investigate the effect of the phase shifts on the ergodic capacity in different propagation scenarios. In particular, we propose an optimal phase shift design based on the ergodic capacity approximation and statistical channel state information. Furthermore, we derive the requirement on the quantization bits of the LIS to promise an acceptable capacity degradation. Numerical results show that using the proposed phase shift design can achieve the maximum ergodic capacity, and a 2-bit quantizer is sufficient to ensure capacity degradation of no more than 1 bit/s/Hz.
Full-text available
The adoption of a Reconfigurable Intelligent Surface (RIS) for downlink multi-user communication from a multi-antenna base station is investigated in this paper. We develop energy-efficient designs for both the transmit power allocation and the phase shifts of the surface reflecting elements, subject to individual link budget guarantees for the mobile users. This leads to non-convex design optimization problems for which to tackle we propose two computationally affordable approaches, capitalizing on alternating maximization, gradient descent search, and sequential fractional programming. Specifically, one algorithm employs gradient descent for obtaining the RIS phase coefficients, and fractional programming for optimal transmit power allocation. Instead, the second algorithm employs sequential fractional programming for the optimization of the RIS phase shifts. In addition, a realistic power consumption model for RIS-based systems is presented, and the performance of the proposed methods is analyzed in a realistic outdoor environment. In particular, our results show that the proposed RIS-based resource allocation methods are able to provide up to 300% higher energy efficiency, in comparison with the use of regular multi-antenna amplify-and-forward relaying.
Full-text available
Future wireless networks are expected to constitute a distributed intelligent wireless communications, sensing, and computing platform, which will have the challenging requirement of interconnecting the physical and digital worlds in a seamless and sustainable manner. Currently, two main factors prevent wireless network operators from building such networks: (1) the lack of control of the wireless environment, whose impact on the radio waves cannot be customized, and (2) the current operation of wireless radios, which consume a lot of power because new signals are generated whenever data has to be transmitted. In this paper, we challenge the usual “more data needs more power and emission of radio waves” status quo, and motivate that future wireless networks necessitate a smart radio environment: a transformative wireless concept, where the environmental objects are coated with artificial thin films of electromagnetic and reconfigurable material (that are referred to as reconfigurable intelligent meta-surfaces), which are capable of sensing the environment and of applying customized transformations to the radio waves. Smart radio environments have the potential to provide future wireless networks with uninterrupted wireless connectivity, and with the capability of transmitting data without generating new signals but recycling existing radio waves. We will discuss, in particular, two major types of reconfigurable intelligent meta-surfaces applied to wireless networks. The first type of meta-surfaces will be embedded into, e.g., walls, and will be directly controlled by the wireless network operators via a software controller in order to shape the radio waves for, e.g., improving the network coverage. The second type of meta-surfaces will be embedded into objects, e.g., smart t-shirts with sensors for health monitoring, and will backscatter the radio waves generated by cellular base stations in order to report their sensed data to mobile phones. These functionalities will enable wireless network operators to offer new services without the emission of additional radio waves, but by recycling those already existing for other purposes. This paper overviews the current research efforts on smart radio environments, the enabling technologies to realize them in practice, the need of new communication-theoretic models for their analysis and design, and the long-term and open research issues to be solved towards their massive deployment. In a nutshell, this paper is focused on discussing how the availability of reconfigurable intelligent meta-surfaces will allow wireless network operators to redesign common and well-known network communication paradigms.
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Electromagnetic waves undergo multiple uncontrollable alterations as they propagate within a wireless environment. Free space path loss, signal absorption, as well as reflections, refractions, and diffractions caused by physical objects within the environment highly affect the performance of wireless communications. Currently, such effects are intractable to account for and are treated as probabilistic factors. This article proposes a radically different approach, enabling deterministic, programmable control over the behavior of wireless environments. The key enabler is the so-called HyperSurface tile, a novel class of planar meta-materials that can interact with impinging electromagnetic waves in a controlled manner. The HyperSurface tiles can effectively re-engineer electromagnetic waves, including steering toward any desired direction, full absorption, polarization manipulation, and more. Multiple tiles are employed to coat objects such as walls, furniture, and overall, any objects in indoor and outdoor environments. An external software service calculates and deploys the optimal interaction types per tile to best fit the needs of communicating devices. Evaluation via simulations highlights the potential of the new concept.
IRS is a new and revolutionizing technology that is able to significantly improve the performance of wireless communication networks, by smartly reconfiguring the wireless propagation environment with the use of massive low-cost passive reflecting elements integrated on a planar surface. Specifically, different elements of an IRS can independently reflect the incident signal by controlling its amplitude and/or phase and thereby collaboratively achieve fine-grained 3D passive beamforming for directional signal enhancement or nulling. In this article, we first provide an overview of the IRS technology, including its main applications in wireless communication, competitive advantages over existing technologies, hardware architecture as well as the corresponding new signal model. We then address the key challenges in designing and implementing the new IRS-aided hybrid (with both active and passive components) wireless network, as compared to the traditional network comprising active components only. Finally, numerical results are provided to show the great performance enhancement with the use of IRS in typical wireless networks.
Intelligent reflecting surface (IRS) is a revolutionary and transformative technology for achieving spectrum and energy efficient wireless communication cost-effectively in the future. Specifically, an IRS consists of a large number of low-cost passive elements each being able to reflect the incident signal independently with an adjustable phase shift so as to collaboratively achieve three-dimensional (3D) passive beamforming without the need of any transmit radio-frequency (RF) chains. In this paper, we study an IRS-aided single-cell wireless system where one IRS is deployed to assist in the communications between a multi-antenna access point (AP) and multiple single-antenna users. We formulate and solve new problems to minimize the total transmit power at the AP by jointly optimizing the transmit beamforming by active antenna array at the AP and reflect beamforming by passive phase shifters at the IRS, subject to users’ individual signal-to-interference-plus-noise ratio (SINR) constraints. Moreover, we analyze the asymptotic performance of IRS’s passive beamforming with infinitely large number of reflecting elements and compare it to that of the traditional active beamforming/relaying. Simulation results demonstrate that an IRS-aided MIMO system can achieve the same rate performance as a benchmark massive MIMO system without using IRS, but with significantly reduced active antennas/RF chains. We also draw useful insights into optimally deploying IRS in future wireless systems.
With the fast development of smart terminals and emerging new applications (e.g., real-time and interactive services), wireless data traffic has drastically increased, and current cellular networks (even the forthcoming 5G) cannot completely match the quickly rising technical requirements. To meet the coming challenges, the sixth generation (6G) mobile network is expected to cast the high technical standard of new spectrum and energy-efficient transmission techniques. In this article, we sketch the potential requirements and present an overview of the latest research on the promising techniques evolving to 6G, which have recently attracted considerable attention. Moreover, we outline a number of key technical challenges as well as the potential solutions associated with 6G, including physical-layer transmission techniques, network designs, security approaches, and testbed developments.
In the millimeter-wave (30-300 GHz) and terahertz (0.1-10 THz) frequency bands, the high spreading loss and molecular absorption often limit the signal transmission distance and coverage range. In this article, four directions to tackle the crucial problem of distance limitation are investigated, namely, a distance-aware physical layer design, ultra-massive MIMO communication, reflectarrays, and intelligent surfaces. Additionally, the potential joint design of these solutions is proposed to combine the benefits and further extend the communication distance. Qualitative and quantitative evaluations are provided to illustrate the benefits of the proposed solutions. The feasibility of mmWave and THz band communications up to 100 m in both line-of-sight and nonline- of-sight areas are demonstrated.