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Abstract

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.
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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
communications.
I. INTRODUCTION
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´
elec,
Laboratoire des Signaux et Syst`
emes, Gif-sur-Yvette, France.
M. Debbah is with Universit´
e Paris-Saclay, CNRS and CentraleSup´
elec,
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.
II. HMIMOS DESIGN MOD EL S
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.
2
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
3
I I I I I
C C C C C
C C C C C
S A S A S
S A S A S
S A S A S
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
lobes.
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.
4
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.
III. FUNCTIONALITY, CHARACTERISTICS,AND
COMMUNICATION APPLICATIONS
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
field.
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.
5
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
objects.
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.
IV. DESIGN CHA LL EN GE S AN D OPPORTUNITIES
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].
6
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.
7
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.
V. CASE STUDIES
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.
VI. CONCLUSION
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
8
10-4 10-2 100102104
10-4
10-2
100
102
104
106
Massive MIMO
HMIMOS
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.
ACK NOW LE DG EM EN T
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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BIOGRAPHIES
CHONGWEN HUAN G [M’19] (chongwen huang@sutd.edu.sg) 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] (hu.sha@huawei.com) 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.
GEORGE C. ALEXANDROPOULOS [SM’15] (alexandg@di.uoa.gr)received
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] (alessio.zappone@unicas.it) 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] (yuenchau@sutd.edu.sg) 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] (elezhang@nus.edu.sg) 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] (marco.direnzo@centralesupelec.fr) 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.
M´
EROUANE DEBBA H [F’15] (merouane.debbah@huawei.com) 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.
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