# Smart Radio Environments

Gabriele Gradoni
School of Mathematical Sciences
Department of Electrical and Electronic Engineering
University of Nottingham
Nottingham
United Kingdom
<EMAIL_ADDRESS>
Marco Di Renzo
Laboratoire des Signaux et Systémes
CNRS, CentraleSupélec
Université Paris-Saclay
Gif-sur-Yvette
Paris
France
<EMAIL_ADDRESS>

###### Abstract

This Roadmap takes the reader on a journey through the research in
electromagnetic wave propagation control via reconfigurable intelligent
surfaces. Meta-surface modelling and design methods are reviewed along with
physical realisation techniques. Several wireless applications are discussed,
including beam-forming, focusing, imaging, localisation, and sensing, some
rooted in novel architectures for future mobile communications networks
towards 6G.

_K_ eywords RIS $\cdot$ 6G $\cdot$ Metasurface

##  1 Introduction

Future mobile wireless network technologies seek to extend radio signal
coverage and boost communication data-rates of an ever increasing number of
mobile subscribers. It is estimated that the number of mobile terminals
worldwide will almost double the global population by 2025. The recent
development of reconfigurable intelligent surfaces (RISs) provides a way
forward to support the ambition of mobile network technologies in the
transition from 5G to 6G. Inherently, it is believed that properly designed
RISs are amenable to integration within existing 4G/5G network
infrastructures. Several international research projects are currently
underway to substantiate this belief, thus creating a multidisciplinary arena
where physicists, mathematicians, engineers, and computer scientists, can
collaborate to address the most pressing challenges generated by new RIS-based
network architectures. While metamaterials (MTS) constitute the physical
backbone of RISs, their optimization and orchestration as a network
resource/service call for advanced electromagnetic (EM) modelling techniques,
intertwined with modern artificial intelligence (AI) and machine learning (ML)
methods. This roadmap reviews the basic concepts, discusses open problems, and
investigates future solutions, from the perspective of prominent scientists
actively researcheing on RISs.

###  1.1 Roadmap in detail

The roadmap is organised in three sections that mirror the parallel effort of
the electromagnetic and physics communities to develop MTS that have been
recently included in advanced solutions for beyond 5G/6G reconfigurable mobile
networks.

  * •

EM Modelling and Design: _Diaz-Rubio_ and _Tretyakov_ identify gaps between EM
theory and design of MTS. _Caloz_ discusses modelling assisted design for
near-field wave and quantum manipulation, also touching on quantum computing.
_Gradoni_ and _Peng_ show how to find the optimal state of very large RISs via
Ising models and quantum annealing. _Alu_ introduces metagratings for
reflections at extreme angles and highly efficient focusing.

  * •

Physical Realization and EM Environment: _Lerosey_ and _Fink_ look back at the
pioneering achievement of a RIS prototype and unfold the road for the
realization of self-adaptive RISs. _Galdi_ and _Cui_ present challenges in the
modelling of space-time coding digital MTS for an all-EM modulation scheme.
_Frazier_ and _Anlage_ discuss the integration of RISs within complex cavities
and the need of a ML-assisted wavefront shaping. _Salucci_ and _Massa_ extend
wave control to large scale EM environments showing that arbitrary sources can
be designed by inverse methods including smart objects.

  * •

Wireless System Applications: _Cheng_ and _Cui_ survey the use of RISs in
wireless communications and explain the importance of low complexity coding
MTS in 6G wireless newtorks. _Wang_ and _Jin_ tackle the fundamental
challenges in signal detection, channel estimation, and beamforming for RIS
assisted wireless networks via AI methods. _Dardari_ and _Decarli_ elucidate
the need of better EM and channel modelling of RIS assisted systems to support
the realization of holographic radios. _Yurdusevem_ and _Matthaiou_ offer a
perspective on the use of RIS apertures to exploit holography in direction of
arrival estimation and beam synthesis. _Kenney_ and _Gordon_ envisage compact
RIS assisted imaging solutions in the optical domain as an innovative
multimode IoT device. _Georgiou_ and _Nguyen_ examine the role of RISs in the
enhanchement of indoor and underground localization technologies for 6G.
_Martini_ and _Maci_ propose an all-EM wireless sensing solution based on the
manipulation of surface waves propagating through MTS to reduce network
complexity. _Wakatsuchi_ and _Phang_ elaborate on the use of RISs to sense
waveforms and their use in wireless communications.

## Scattering from reconfigurable metasurface panels

### Ana Díaz-Rubio and Sergei A. Tretyakov*

####  1Department of of Electronics and Nanoengineering, Aalto University,
Espoo, Finland

#### *Corresponding author: sergei.tretyakov “at” aalto.fi

##### Emerging challenges

Recently, many researchers have been studying potential improvements of
wireless telecommunication systems with the use of reconfigurable intelligent
metasurfaces. The governing idea is that making some parts of the propagation
environment tunable and adjustable, one can optimize it together with the
optimization of the transmitters and receivers. When experts in communication
theory, signal processing, and machine learning consider wireless propagation
channels in presence of reconfigurable metasurfaces, they need to model
reflected and scattered fields produced by anomalously reflecting
metasurfaces. The wast majority of such studies are based on the assumption
that every “point” at the metasurface plane or every “unit cell” of the
metasurface is characterized by a certain reflection coefficient

$\Gamma(x,y)=|\Gamma(x,y)|e^{j\Phi(x,y)}$ (1)

that can be tuned at will for any coordinate $x,y$ at the metasurface (see,
e.g., [1] and references therein).

Figure 1: Schematic representation of the scattering produced by an anomalous
reflector designed with a linear phase gradient of local reflection
coefficient. The use of model 1 predicts only one reflected beam with a large
error in the estimated amplitude of reflection.

Propagation over the air from the transmitter to every point of the
metasurface and from these points to the receiver are similarly modeled by the
corresponding complex-valued factors, and, finally, the function $\Gamma(x,y)$
is optimized with the goal to increase the signal strength at the receiver
position.

However, the model described by Eq. 1 is valid only in the assumption that the
metasurface is approximately uniform at the wavelength scale (in the theory of
diffraction this model is called _physical optics_ , e.g., [2]). For
anomalously reflecting metasurfaces, this assumption holds only for small
tilts of the reflected wave with respect to the specular-reflection angle. For
modeling of reflections from “unit cells”, this model is not useful, because
single array elements scatter in all directions, and such reflection
coefficient is not meaningful. Moreover, unit-cell response models completely
neglect field coupling between cells, while the design of effective anomalous
reflectors is based on proper engineering of this coupling (e.g., [3, 4, 5]).
On the other hand, the electromagnetic theory of anomalous reflectors
(especially finite-size panels) is not yet properly developed, and the results
often come in a form not directly suitable for inclusion in channel models or
ray-tracing algorithms.

##### Future developments to satisfy these challenges

Attending to the current demands on the development of smart radio
environments, the first challenge is to find appropriate models for
metasurface response to external illuminations and calculating reflected and
scattered fields (including both reflection and transmission) of finite-sized
periodic metasurface. Most of known approaches to calculation of reflection
from finite-sized metasurfaces are based on the local reflection coefficient
model 1 and, as it was discussed in the introduction, do not properly cover
the complexity of the problem. A possible approach to address this challenge
merges some ideas of the physical optics (but using nonlocal expressions for
the induced currents) and the theory of diffraction by gratings (because
periodical structures create multiple diffracted beams) [8]. Figure 1
schematically represents the scattering pattern of an anomalous reflector
designed with a linear phase gradient of the local reflection coefficient when
we consider the energy coupling between the incident wave and different
propagating diffracted modes. Although this model properly accounts for
scattering into all propagating modes, the model assumes homogeneous
distribution of the equivalent currents over the metasurface neglecting the
effect of the discontinuities at the borders. This simplification has an
impact on the accuracy of the method for calculating scattering from small-
sized metasurfaces.

Generally, metasurfaces have been studied assuming homogeneous plane-wave
illumination from a certain direction. This assumption is valid if the source
is far from the metasurface. In real-world application and especially in in-
doors applications, this condition may not always be valid. As it is shown in
Fig. 2, even though metasurfaces are typically periodic systems, the proximity
of the transmitter to the metasurface cause a spatial variation of the
incident field that wrecks the periodicity of system. For this reason,
appropriate models should be applicable to scenarios where the metasurface is
aperiodic and inhomogeneously illuminated. Developing of such models is
challenging because the scattering produced by the metasurface does not follow
the theory of diffraction by gratings.

Figure 2: Illumination of an anomalous reflective metasurface by a receiver
located in close proximity to the metasurface.

It is expected that future communication systems will benefit from the full
potential of inhomogeneous and time-varying metasurfaces with engineered and
adaptive spatial and temporal properties. It is obvious that reconfigurable
metasurfaces whose operation modes can be changed according to the demands of
the environment have an interesting role to play to control the propagation
channel. However, one can envision even more exotic functionalities, from non-
reciprocity to loss compensation, by exploiting the possibilities offered by
time-modulated metasurfaces.

##### Conclusion

We see that there is a deep gap between the electromagnetic theory and design
of reconfigurable metasurfaces that leads to appropriate physical models of
their response and the communication-theory models of propagation channels in
presence of such new devices. Without bridging this gap, many results of
advanced studies of channel optimization will produce estimations of
performance of structures that are not realizable in principle. There are
several publications that try to fill this gap, e.g. [6, 7, 8], but there is
still a long road ahead.

It is also important to note that experimental work on developing
reconfigurable metasurfaces for telecommunications lags behind theoretical
contributions. There has been only a few conceptual demonstrations so far,
mainly for discrete (usually only two states) tuning of unit cells, e.g. [9].
Design of a fully tunable (with some limitations, naturally) metasurface can
be found in [10], but experimental results are not yet available.

Finally, we note that the current efforts are focused on reconfigurable
structures whose functionality is fixed between the acts of reconfiguration.
However, continuous coherent tuning can offer totally new possibilities for
control of wave reflection and transmission, e.g. [11]. We expect that these
possibilities will be exploited in future wireless communication systems.

## Acknowledgment

This work was supported in part by the European Commission through the H2020
ARIADNE project under grant 871464 and the Academy of Finland under grants
330260 and 330957.

## References

  * [1] Q. Wu, S. Zhang, B. Zheng, C. You, and R. Zhang, Intelligent reflecting surface aided wireless communications: A tutorial, IEEE Trans. on Communications (Early Access), 2021.
  * [2] A. Osipov and S. Tretyakov, Modern Electromagnetic Scattering Theory with Applications, Chichester, UK: John Wiley & Sons, 2017.
  * [3] V. S. Asadchy et al., Perfect control of reflection and refraction using spatially dispersive metasurfaces, Phys. Rev. B 94, p. 075142, 2016.
  * [4] N.M. Estakhri and A. Alù, Wave-front transformation with gradient metasurfaces, Phys. Rev. X 6, p. 041008, 2016.
  * [5] X. Wang, A. Díaz-Rubio, and S. A. Tretyakov, Independent control of multiple channels in metasurface devices, Phys. Rev. Applied, vol. 14, p. 024089, 2020.
  * [6] M. Di Renzo, et al., Smart radio environments empowered by reconfigurable intelligent surfaces: How it works, state of research, and the road ahead, IEEE J. Sel. Areas Commun., vol. 38, no. 11, pp. 2450-2525, 2020.
  * [7] G. Gradoni and M. Di Renzo, End-to-End Mutual Coupling Aware Communication Model for Reconfigurable Intelligent Surfaces: An Electromagnetic-Compliant Approach Based on Mutual Impedances, arXiv:2009.02694v2 [cs.IT] 2020.
  * [8] A. Díaz-Rubio and S. A. Tretyakov, Macroscopic modeling of anomalously reflecting metasurfaces: Angular response and far-field scattering, arxiv:2012.03727, 2020.
  * [9] W. Tang, et al., Wireless communications with reconfigurable intelligent surface: Path loss modeling and experimental measurement, IEEE Trans. Wireless Commun., vol. 20, no. 1, pp. 421-439, 2021.
  * [10] A. Pitilakis et al., A multi-functional reconfigurable metasurface: Electromagnetic design accounting for fabrication aspects, IEEE Trans. Antennas Propag. (Early Access), 2021.
  * [11] X. Wang, A. Díaz-Rubio, H. Li, S. A. Tretyakov, and A. Alù, Theory and design of multifunctional space-time metasurfaces, Phys. Rev. Applied, vol. 13, p. 044040, 2020.

## Physics and Modeling of Metasurfaces

### Christophe Caloz1*

####  1 META Research Group, KU Leuven, Belgium

#### *Corresponding author<EMAIL_ADDRESS>
##### Introduction

Metasurfaces (Fig. 3) are electrically thin sheets of subwavelength artificial
particles - or metaparticles - arranged in a periodic lattice and designed to
transform electromagnetic waves111Although we focus here on electromagnetic
metasurfaces, metasurfaces may be of different types, depending on the type of
waves that they manipulate; they can also be acoustic, mechanical, and
gravitational. according to specifications [1]. They may be considered as the
2D counterparts of voluminal metamaterials, with the advantages of lower form
factor, smaller loss and easier fabrication, or as generalizations of
frequency or polarization selective surfaces spatial light modulators, with
the advantage of drastically diversified properties and enhanced capabilities
across the entire spectrum from microwave frequencies to optical wavelengths
via the terahertz regime. Metasurfaces have emerged as a novel paradigm in
science and technology over the past decade. Given their simple configuration,
one may wonder why they did not flourish much earlier. The reason is that
their macroscopic simplicity conceals a formidable complexity and diversity at
the microscopic level of their metaparticles, especially when the structure is
multilayered, as is typically the case for surface impedance matching, and
nonuniform, i.e., involving particles of different sizes and/or shapes.
Mastering such a jungle of parameters necessitated a deep understanding of
artificial materials, which required itself about one century and a half of
intensive research on complex media, artificial dielectrics and modern
metamaterials. The advance that decisively promoted metasurfaces to the rank
of a new paradigm has been the progressive understanding on how to implement
bianisotropy on the two-dimensional platform.

Figure 3:  Artistic representation of a generic metasurface, performing a
diversity of sophisticated electromagnetic wave transformations.

##### Current and Future Challenges

Bianisotropy. Bianisotropy is characterized by the constitutive relations
$D=\varepsilon_{0}({I}+{\chi}_{ee})\cdot E+\frac{1}{c}{\chi}_{em}\cdot H$ and
$B={\chi}_{me}\cdot E+\mu_{0}({I}+{\chi}_{mm})\cdot H$ [1]. It is a type
electromagnetic response that involves 36 complex medium parameters (4 tensors
of dimension 33), some of which are typically interrelated by fundamental
medium properties, such as reciprocity, loss-gain, chirality, gyrotropy and
birefringence. Historically, bianisotropy first appeared, in partial tensorial
form, in media moving with respect to a rest frame [2], and it was generalized
to its full (36-parameter) tensorial response in the 70’s222Bianisotropy is
not the most general form of medium response (which would include increasing-
order spatial vectorial derivatives of $E$ and $H$), but only a particular
case of weak spatial dispersion [4]. However, it generally models
metamaterials adequately because their subwavelength metaparticle feature,
leading to material homogeneity, typically prevents the onset of higher-order
spatial dispersive effects. [3]. However, general bianisotropic materials are
not widely available in nature, and their implementation in artificial form
has remained elusive, due to the prohibitive challenge posed by their
fabrication complexity. Metasurfaces, thanks to their much simpler, two-
dimensional configuration, have allowed, for the first time, practical access
to the fantastic diversity of full bianisotropy, and subsequently led to a
myriad of novel electromagnetic effects and applications [1].
GSTC. Given their deeply subwavelength thickness, metasurfaces do
generally333This constraint has recently been relaxed in several metasurface
designs, but the GSTCs can then still be sometimes used in terms of effective
sheet conditions. not support normal Fabry-Perot resonances, and can therefore
be perfectly modeled by Generalized Sheet Transition Conditions (GSTCs). GSTCs
are generalizations of textbook boundary conditions, which include the
addition of surface  polarization currents that model the response of the
metaparticles and their intercoupling in the overall metasurface structure [1,
5, 6, 7]. They have the general form [1]:

${n}\times\Delta H=\frac{\partial}{\partial
t}(\varepsilon_{0}{\chi}_{ee}E_{av}+\frac{1}{c}{\chi}_{em}H_{av})+{n}\times\nabla
M_{z},$ $\Delta E\times{n}=\mu_{0}\frac{\partial}{\partial
t}({\chi}_{mm}H_{av}+\frac{1}{\eta}{\chi}_{me}E_{av})+\frac{1}{\varepsilon_{0}}{n}\times\nabla
MP_{z},$

In these relations, the $\Delta$ and $av$ terms represent the differences and
averages, respectively, of the electric and magnetic fields at either side of
the metasurface, with ${n}$ being the unit vector normal to it.
Design. In a synthesis problem, where the task is to design a metasurface that
performs a given electromagnetic wave transformation, the field quantities are
known, and one can then solve the above equations for the susceptibility
tensors. In linear time-invariant metasurfaces, the convolution products in
these relations reduce to multiplication products, and these tensors can often
be found in closed form [1]. New full-wave algorithms have been developed to
analyze metasurfaces characterized by the bianisotropic tensor
susceptibilities obtained from this procedure [8]. Once the metasurface
described by these continuous tensorial susceptibility functions has been
synthesized and analyzed, it is discretized in subwavelength periodic cells in
each of which a specific metaparticle (size and shape) is found via a
parametric map between the theoretical scattering response associated with the
susceptibility model and the full-wave simulated scattering response of
specific metaparticle structures [1].
Applications. The unique and systematic capabilities of metasurfaces to
control the phase, the magnitude, and the polarization of waves in terms of 36
bianisotropic parameters, combined with the recent availability of powerful
design techniques based on GSTCs, has already led to uncountable applications
at the time of this writing [1]. They are too numerous to be exhaustively
mentioned here but they include generalized reflection and reflection, all
kinds of other wavefront manipulations, generalized Brewster and Fresnel
refraction, agile polarization processing for gyrotropy, birefringence and
polarimetry, sophisticated spatial and magnetless nonreciprocity, multi-wave
transformation, simultaneous spatial and temporal spectral processing, spatial
nonlinearity boosting, imaging and holography, spin and orbital angular
momentum transformation, angle-independent absorption, wireless communication
smart-panel scattering and processing, camouflaging and cloaking, thermal
radiation management, nanoparticle and solar sail optical force tailoring,
power harvesting, quantum system engineering, analog computing; references to
most of these are found in the review papers [9]-[12].

##### Advances in Science and Technology to Meet Challenges

At this time, the field of metasurfaces faces several theoretical and
technological challenges, as well as related opportunities. I shall try to
identify here some of these challenges and opportunities: Theory: 1) The
brute-force parametric-mapping technique mentioned above for the synthesis of
specific metaparticles from synthesized tensorial susceptibility functions is
slow and tedious; the development of smarter approaches, possibly drawing from
topology and group theories and machine-learning artificial intelligence,
would be highly welcome to provide more efficient design strategies. 2) As
mentioned above, bianisotropy represents a low spatial dispersion model;
exploiting higher-order spatial dispersion could both lead to more accurate
modeling of current metasurfaces and enable metasurfaces with greater
diversity transformations in mesoscopic-regime structures. 3) Metasurface
near-field manipulations have been very little considered to date; their
investigation could enable a novel range of applications, for instance in
microscopy and Casimir-force engineering. 4) Despite the current blossoming of
quantum technologies, few efforts have been dedicated to the control of
quantum systems by metasurfaces, not to mention the development actual quantum
metasurfaces; research efforts in this area will certainly bring novel effects
and devices for quantum sensing, computing and metrology. Technology: 1) While
many metasurfaces can be easily fabricated in the microwave regime, their
implementation at optical wavelengths is still often challenging, particularly
in configurations involving multiple layers and fine features; much effort is
currently being deployed in this area, particularly using all-dielectric and
CMOS platform approaches. 2) Nonreciprocity has been recently realized to be
particularly desirable to come in magnetless format for compatibility with
integrated circuit technology and spatial processing, particularly in
transistor-loaded metaparticle technology [13]; however, serious efforts are
still required to develop efficient nonreciprocal metaparticles in the
microwave regime, and to find a substitute to transistors - or develop an
optical transistor [14] \- in the optical regime444Time-modulated
nonreciprocity does currently offer a better option than transistor
nonreciprocity technology because of the speed limitation of the electronics
required for modulation (while record transistors have recently passed the
symbolic operation limit of 1 THz).. 3) The addition of time modulation to
space modulation towards spacetime-modulated metamaterials represents
particularly vast potential of scientific and technological innovation [15,
16]; although this potential has been recently discussed in metasurface
structures [17], the implementation of this concept is still enormous
challenging, and will require novel technological approaches for the " RF
modulation" of the metasurfaces. 4) Finally, smart metasurfaces, serving for
instance in wireless communication systems for " fog-type" local processing to
reduce the cluttering of servers on the internet of things, will require fully
programmable tunable and active metasurfaces acting as local distributed
processors.

## References

  * [1] K. Achouri, C. Caloz, Electromagnetic Metasurfaces: Theory and Applications, 2021, Wiley.
  * [2] A. Sommerfeld, Electrodynamics, 1964, Academic Press.
  * [3] J. A. Kong, “Theorems of bianisotropic media” _Proc. IEEE_ , 60(9):1036-46, 1972.
  * [4] L. D. Landau  et al. Electrodynamics of Continuous Media, Elsevier, 1984 (2nd ed.).
  * [5] M. M. Idemen, Discontinuities in the Electromagnetic Field, 2011, John Wiley & Sons.
  * [6] C. L. Holloway et al. “An overview of the theory and applications of metasurfaces: the two-dimensional equivalents of metamaterials” _IEEE Antennas Propag. Mag._ , 54(2):10-35, 2012.
  * [7] K. Achouri et al.  “General metasurface synthesis based on susceptibility tensors” _IEEE Trans. Antennas Propag._ , 63(7):2977-2991, 2015.
  * [8] Y. Vahabzadeh et al. “Computational analysis of metasurfaces” _IEEE J., Multiscale Multiphys. Comput. Tech._ 3:37-49, 2018.
  * [9] S. B. Glybovski et al. “Metasurfaces: from microwaves to visible” _Phys. Rep._ , 634:1-72, 2016.
  * [10] V. S. Asadchy et al. “Bianisotropic metasurfaces: physics and applications” _Nanophotonics_ , 7(6), 1069-1094, 2018.
  * [11] K. Achouri, C. Caloz, “Design, concepts, and applications of electromagnetic metasurfaces” _Nanophotonics_ , 7(6): 1095-1116, 2018.
  * [12] W. Tang et al. “Wireless communications with programmable metasurface: New paradigms, opportunities, and challenges on transceiver design” _IEEE Wireless Communications_ , 27(2):180-187, 2020.
  * [13] C. Caloz et al. “Electromagnetic Nonreciprocity” _Phys. Rev. Appl._ , 10(4), 047001:1-26, 2018.
  * [14] J. Hwang et al. “A single-molecule optical transistor”, _Nature_ , 460(7251):76-80, 2009.
  * [15] C. Caloz, Z.-L. Deck-Léger, “Spacetime metamaterials, part I: General concepts” _IEEE Trans. Antennas Propag._ , 68(3), 1569-1582, 2020.
  * [16] C. Caloz, Z.-L. Deck-Léger, “Spacetime metamaterials, part II: Theory and applications” _IEEE Trans. Antennas Propag._ , 68(3), 1583-1598, 2020.
  * [17] N Chamanara et al. “Simultaneous control of the spatial and temporal spectra of light with space-time varying metasurfaces” _IEEE Trans. Antennas Propag._ , 67(4), pp. 2430-2441, 2019.

## On the Role of Quantum Optimization in Reconfigurable Wireless Environments

### Gabriele Gradoni1 and Zhen Peng2*

####  1School of Mathematical Sciences and Department of Electrical and
Electronic Engineering, University of Nottingham, University Park NG72RD,
United Kingdom

####  2Department of Electrical and Computer Engineering, University of
Illinois at Urbana-Champaign, Illinois, USA

#### *Corresponding author<EMAIL_ADDRESS>
##### Motivation:

The reconfigurable intelligent surface (RIS) is emerging as a key technology
for the next-generation of mobile communication network. The general goal is
to turn the wireless environment into a smart/reconfigurable space that plays
an active role in the wireless communication performance [1]. Going beyond 5G
and entering 6G, it is envisaged that large-scale, distributed RIS devices may
be deployed at the surface of interacting objects, e.,g. wall, windows,
furniture, in the propagation channel. A joint optimization of wireless
endpoints and distributed RISs would lead to a dynamically programmable and
customized wireless environment, with a goal of providing enhanced coverage
with high energy efficiency and supporting ultra-fast and seamless
connectivity. To harness the full potential of RIS-enabled smart radio
environment, we need to rapidly optimize the states of RIS devices with
prescribed objective functions. This constitutes a substantial computational
task both in the physical and network layer of wireless communication, due to
the enormous number of available degrees of freedom (DOFs).

##### Introduction to Quantum Optimization:

Whereas some recent researches pursue the direction of artificial intelligence
and deep learning, we elaborate the role of quantum computing (QC) to provide
a scalable approach that overcomes the computational optimization complexity.
In recent years, the remarkable progress made in QC hardware has defined a
new, Noisy Intermediate-Scale Quantum (NISQ), QC era. By exploiting
fundamental properties of quantum mechanics, these QC systems have reach the
potential to deliver orders of magnitude in the speedup against classical
computing hardware for solving hard problems. Here, we focus on quantum
combinatorial optimization algorithms, which run on NISQ devices to search for
an optimal solution over all the combinatorial states of RIS elements. One
well-known example is the Grover Adaptive Search algorithm [2] with provable
quantum speedup, which has been recently applied to Constrained Polynomial
Binary Optimization (CPBO) problems. Another representative case is the
quantum approximate optimization algorithm (QAOA) [3] utilizing unitary
operators and quantum superposition. It is noted that both above-mentioned
algorithms run on universal gate-model quantum computers. There is another
specialized analog computer, so-called quantum annealer (QA), which belongs to
the adiabatic quantum computing (AQC) regime [4]. In what follows, we review
the current literature on quantum optimisation of smart/reconfigurable
wireless systems and discuss open problems in this area of research, with an
outlook into possible solutions to address the most pressing challenges to
enable large scale 6G network design.

##### State of the art:

Recently, paradigms based on QC have attracted the interest of network
operators as a promising platform to perform flexible optimisation of dense
networks that serve an increasing number of users. In this context, QC is
expected to achieve unprecedented low latency reconfiguration of base
stations, as well as more efficient resource scheduling policies. A review
article on quantum search algorithms in wireless applications can be found in
[5]. In September 2018, a partnership between British Telecommunications and
academic partners in the United Kingdom [6] has set the sea to use QC in
telecommunications. In particular, QA has helped planning the network from
system level to optimise network resources, which typically involve hard (NP
and P hard) optimisation problems. For example, cell user channel allocation
and half-duplex network optimisation are known to be NP hard problems. In
February 2020, Telecom Italia Mobile (TIM) was the first mobile operator to
adopt QC approaches in planning of 4.5G and 5G networks [7]. In particular,
the quadratic unconstrained binary optimization (QUBO) algorithm was
implemented on a D-Wave’s 2000QTM quantum computer to perform real-time
network configuration ten times faster than traditional optimization
algorithms. This paradigm already provides increased quality of service in
Voice over LTE (VoLTE) and is believed to be important in future self-
organising networks (SONs). More recently, the QA was also used in the vector
perturbation precoding for large MIMO systems in downlink [8].

Very recently, researchers have drawn their attention to fuse electromagnetic
(EM) models with quantum optimization, a research that is gradually defining a
new field on quantum optimization of RIS assisted wireless networks. In this
new field, statistical physics is playing an important role in linking the
multi-element, distributed devices, wireless problem to a physical formulation
that can be tackled efficiently with novel QC architectures. As an example, in
statistical mechanics, the Ising model is widely used to describe the spin
state of arrays of quantum particles. In [9], the authors dwell on this
analogy and develop an Ising model for the RISs with prescribed scattering
profile. The spin-like degrees of freedom are offered by the discrete phase
values in tunable RIS elements. The EM wave energy is then used to calculate
the Ising Hamiltonian. Subsequently, the Ising model is compiled into a
physical QA hardware, the D-Wave 2000Q (DW2Q) QA device, through a process of
embedding and de-embedding, shown in Fig. 4. The configuration time for
simultaneous beam- and null-forming of large RISs is achieved within a few
milliseconds, which is much less than the channel coherence time in a dynamic
radio environment.

Figure 4: Embedding RIS Ising model into QA Hardware.

##### Emerging challenges:

In the future, we will face a plethora of challenges pertaining two highly
interdisciplinary research areas. The first area is the holistic wave modeling
and design of joint RIS devices and propagation environments. The interaction
between RIS and random fields needs an in-depth study that captures the
multiple reflections originating within a confined wireless environment. This
study requires the formulation as a boundary value problem (BVP) that can be
treated with statistical methods developed in the areas of wave and quantum
chaos. More specifically, the random RIS scattered field can be obtained from
the BVP via semiclassical analysis methods and random matrix theory.

The second area is the global optimization of large networks of RIS devices
operating in large scale environments, important for the orchestration of
multiple RIS devices. More accurate EM models, including multipath fading and
near field coupling, will need to be mapped into Ising Hamiltonians for
advanced design with multi level phases of the RIS unit cell. The marriage of
electromagnetism and statistical mechanics for the physical layer optimization
represents a promising way forward for reconfigurable environments. Research
in these two areas is of crucial importance in both the planning and the
dynamic reconfiguration of mobile networks. Eventually, the evolution into
SONs with quantum computing capabilities will pave the way to real-time
network reconfiguration assisted by a Quantum Digital Twin (QDT).

##### Future developments:

One research direction is to enhance the fidelity of the quantum-suitable RIS
model. To increase the angular resolution of reflected beam, three and four
bit codebooks for the reflection phase of RIS unit cells will be formulated in
terms of Ising spin variables. Multi-level optimization will be considered for
very large (or multiple) RIS devices via extension of antenna sub-array design
schemes, such that a single large RIS can be applied for multiple access point
(AP) multiuser reflective beamforming. Finally, the mutual coupling between
RIS elements can be approximated by impedance matrices of small dipoles with
tunable loads and captured in a MIMO channel model that can be used for
network design. Another research direction will seek to improve the modeling
fidelity of the propagation environment. The near-field RIS optimization will
be studied to extend the use case scenarios of RISs from far-field to near-
field (Fresnel region and near the Fraunhofer distance). Configuring RIS in
multi-path channel and under random, e.g., reverberating, fields will also be
tackled via stochastic Green function approaches to account for the effect of
multi-path fading onto the global optimisation of RIS cells.

##### Conclusion:

We believe that the physics of complex systems fused with quantum computing
will constitute a game changer in modeling and design of large network of RIS
devices cooperating in order to transform real-life propagation environments
into a resource for future mobile networks, including SONs and cell-free
networks.

## Acknowledgment

The work of GG is supported by the Royal Society Industry Fellowship, grant
number INF/R2/192066), and the European Union’s Horizon 2020 Industrial
leadership project RISE-6G, No. 101017011. The work of ZP is supported by U.S.
National Science Foundation (NSF) CAREER award, $\\#$1750839.

## References

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## Metasurfaces for
Next-Generation
Communication Systems

### Andrea Alu’1*

####  1 Advanced Science Research Center, City University of New York, USA

#### *Corresponding author<EMAIL_ADDRESS>
##### Status

In the quest to maximize data rates and transmission efficiencies in crowded
and complex multi-channel environments, reconfigurable intelligent surfaces
(RIS) have been raising significant interest in recent years in the field of
wireless communications. These concepts have been establishing a powerful
platform to control in real time the connections among multiple users and
compensate for environment changes [1, 2]. In a parallel but for the most part
disjoint effort, the electromagnetics community has been developing a new
class of engineered surfaces, known as metasurfaces, which can control the
local reflection and transmission coefficients point by point at the
subwavelength scale in order to demonstrate unprecedented forms of
manipulation and control of the impinging wavefront [3]-[5]. It is becoming
increasingly evident that these two research areas may largely benefit from
closer interactions and joint explorations. On one hand, current research on
RIS has been mostly focused on higher-level network and protocol aspects,
basing the hardware description on often naïve models, e.g., treating the RIS
as a phased array over which the local reflection coefficient can be
arbitrarily controlled in space and time, and disregarding physical
limitations and engineering challenges at the hardware level. Similarly,
metasurface research has often been criticized for being focused on
demonstrating fundamental physical principles without an eye on applications
and impact on the grand societal challenges of today’s ever-connected world.
In the following, we discuss potential opportunities that may arise from
connecting these two lines of research, highlighting opportunities that
advanced metasurface technology can offer in the context of communications and
network systems, and detailing the challenges that need to be addressed to
establish a new paradigm for intelligent metasurfaces benefiting wireless
networks and communication systems.

##### Current and Future Challenges

In discussing the benefits that metasurfaces may offer in the framework of RIS
for wireless communication systems, we need to highlight important challenges
in their design and implementation. First of all, the subwavelength reactive
elements composing a metasurface are prone to suffer from spatial and temporal
dispersion: since they need to strongly interact with the local
electromagnetic fields, they typically operate close to resonances, and are
therefore bound to comply with fundamental principles imposing severe
limitations on their temporal response, implying a limited bandwidth of
operation [6]. The close interactions of multiple resonators also imply strong
coupling between closely spaced elements, inducing spatial dispersion that
affects the overall response of the metasurface, which cannot be treated as
local. In addition to these general challenges, there are limitations stemming
from the specific functionality at hand. In the canonical problem of beam
steering towards extreme angles, efficiency limitations arise in conventional
metasurface approaches that do not take into careful consideration
bianisotropy and impedance matching [5]. In [7] we introduced the concept of
metagratings, which addresses some of these limitations by enabling beam
steering with unitary efficiency even for extreme angles, and lifts the need
for dense arrays of resonant elements by combining the concepts of gratings
and of complex meta-element designs, hence also alleviating the spatial and
temporal dispersion of these elements. Figure 5 shows a recently realized
metalens based on this approach, showcasing record-high focusing efficiency
and numerical aperture for a microwave metasurface [8].

Figure 5:  A highly efficient reflective metalens for microwave frequencies,
reporting close-to-unity aperture efficiency. (From [8]).

Another fundamental limitation of conventional metasurfaces is dictated by
time-reversal symmetry and reciprocity: the overall scattering matrix of a
conventional RIS obeys strict symmetries when linking different points in its
surroundings. Lifting this restriction enables opportunities to better exploit
the scarce frequency spectrum, for instance using the same frequency channel
to transmit and receive in the context of full-duplex communications [9]. At
the same time, breaking reciprocity requires special materials that are
difficultly compatible with modern integrated circuits. Alternative
opportunities to enable nonreciprocal operations in an integration-friendly
platform need to be pursued, for instance leveraging temporal modulation
schemes [10, 11]. Time variations are also important in the context of
reconfigurability. To date, most metasurface technology is static, but recent
efforts to reconfigure its response using varactors [12], switches, liquid
crystals [13], graphene [14] or phase change materials [15] have been
explored. Important challenges to making them compatible with a highly
efficient response is necessary, possibly through the use of active and
amplifying elements. Finally, the linearity of conventional metasurfaces poses
itself limitations on the overall available responses. Being able to carefully
tailor their nonlinear response [16], we may enable other interesting
opportunities, such as ad-hoc frequency transformations and more complex
responses. At the same time, these efforts need to be accompanied by careful
considerations associated with possible signal distortions and unwanted
channel mixing.

##### Advances in Science and Technology to Meet Challenges

Bringing together the metasurface and RIS communities may tackle these
challenges and focus on solutions pushing forward a new platform for
reconfigurable, highly intelligent metasurface technology that can
revolutionize wireless communication systems. Integrating active elements over
the surface may overcome several of the limitations mentioned in the previous
section associated with passivity, enhancing transmission efficiency and
broadening the bandwidth of operation [17] beyond the limits of passive
technology. Temporal modulations can also be exploited in this context,
leveraging parametric phenomena to amplify signals and go beyond the bandwidth
limitations of passive elements [18]. Efficient and fast reconfigurability is
also an important aspect that needs to be addressed in next-generation
metasurfaces for wireless communication systems, ideally embedding
opportunities for self-reconfiguration through smart sensors that monitor
changes in the environment, e.g., position of the users and channel
modifications, and autonomously corrects for these changes. As mentioned above
and shown in Fig. 6, controlled time variations can also provide a route for
magnet-free nonreciprocal responses, ideally suited to enhance the channel
capacity and enable full-duplex operations [19]. Parametric mixing in time-
varying metasurfaces can also be used to implement frequency transformations,
for instance to compensate for Doppler shifts generated by users moving
relative to the surface [20]. Finally, nonlocalities and spatial dispersion,
which are nuisance in the conventional operation of metasurfaces, may be
engineered using sophisticated designs, enabling the control and manipulation
of the impinging signals, and even to impart mathematical operations on them
[21]. Leveraging recent advances in network theory, it may be possible to
implement ad-hoc signal filtering and processing in space and time of the
impinging signals and process them in the analog domain at the speed of light
as they interact with our intelligent metasurfaces.

Figure 6:  A nonreciprocal metasurface enabling a largely asymmetric
scattering matrix for full-duplex operation. (From [19])

##### Conclusion

We envision a bright future in leveraging and bringing together recent
advances in metasurface technology and in wireless communications and
networks. The strong synergy between these areas holds the promise for largely
enhanced communication systems. It will be important to engage the several
relevant communities, from the physical layer to the network protocols and
algorithms that can enhance the overall system, with the goal of establishing
a new class of highly reprogrammable, intelligent, efficient, metasurfaces
that can facilitate the next-generation of communication systems.

## References

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## Autonomy for Reconfigurable Intelligent Surfaces! A pioneers’ point of view

###  Geoffroy Lerosey1* and Mathias Fink1,2

####  1Greenerwave, 6 rue Jean Calvin, 75005 Paris, France

####  2Institut Langevin, 1 rue Jussieu, 75005 Paris, France

#### *Corresp. author<EMAIL_ADDRESS>
##### Introduction

It all started back in 2012, when Mathias came back from his countryside
house, complaining about his cell phone very poor connectivity there. After
days of discussion, inspired by the recent concept of wavefront shaping in the
field of optics [1, 2, 3], we had the idea. We would design smart surfaces
that can adapt themselves in real time to shape, at will and in a passive way,
the propagation of electromagnetic waves. We would hence make the environments
electromagnetically smart for greener and more reliable wireless
communications. And so we did; Leveraging our knowledge of wave propagation in
complex environments, we realized that a tunable mirror where the phase shift
is discretized with only two phases is an optimal compromise between hardware
complexity and wave control capabilities. We developed our first
electronically tunable metasurfaces in the microwave range in 2012 and
provided the first laboratory proof of concept in 2013, increasing 10fold the
energy received by an antenna in the WIFI frequency band, simply by shaping
existing scattered waves. We also filled a first patent application and
submitted a subsequent set of seminal publications [4, 5, 6].

Figure 7: The first tunable metasurface developed at Institut Langevin at
2.45GHz with from right to left Mathias Fink, Matthieu Dupré, Nadège Kaïna and
Geoffroy Lerosey (2012).

The work received rather moderate attention in the scientific community for
years, except for some teams specialized in metasurfaces or wave physics [8,
9, 10]. Yet we received a fantastic enthusiasm when we showcased our concept
and provided live demonstrations of passive microwave control using tunable
metasurfaces. And this encouraged us to launch a company devoted to
commercializing this new technology in the field of wireless communications:
Greenerwave. We made tremendous technical progress thanks to the creation of
the company and the funding it received, developing industry ready tunable
metasurfaces, designing hardware boards to control them, or devising new
algorithms to shape waves [11]. Yet the company almost went bankrupt, unable
it was to deliver its promises and to push this technology to the market. To
survive it had to pivot to other applications based on the same core concept,
passive wave control using tunable metasufaces, that are mmWave antennas and
radars.

##### Challenges we faced with RIS

Of course, our pioneer proposal was much too early for the market, which
explains part of this situation. Yet there were other reasons why the company
failed to bring reconfigurable intelligent surfaces to the market, of which
the two most important ones lie at the very root of the concept. On the one
hand, making the environment smart for wireless communications in real time
supposes to have robust and quantitative information on the wireless links
that are established, to be used as feedbacks to pilot the algorithms
controlling the metasurfaces (signal to noise ratio, signal to interference
ratio, channel state information…). This supposes to have a real time access
to deep level proprietary information from chipset manufacturers, either at
the device or at the access point level. Greenerwave could never obtain such
access, and it failed to deliver convincing proofs of concept of the
technology because of this technological barrier. On the other hand, a very
severe limitation showed up from the business side, where questions of
integration very rapidly came into play. Indeed, the technology relies on
smart surfaces that need to be distributed and set up in the environment, and
the larger the surfaces, the better the performances. This quickly raised
concerns about how to power these, how to control these, and finally the need
for wires and infrastructures to do so, which clearly limited the interest of
potential early adopters.

##### Future of RIS

Our long history with RIS therefore urges us to claim autonomy for RIS! We
believe that for a massive adoption of the technology, these reconfigurable
intelligent surfaces should be as self-standing as possible. And this should
in our opinion tackle the two problems we encountered. First, of major
importance is the ability of the RIS to configure itself for optimal wireless
links with minimal feedback from the network, if not none. This would allow
any RIS to be compatible with any device and avoid or minimize issues between
networks/operators. From a physics point of view it should be possible to add
extra hardware on a RIS to allow it to estimate the configuration necessary
for optimal beamforming on given users, without any feedback between the users
and the RIS. To do so, inspired from holographic concepts, we think that real
time optimization of the RIS configuration can be done by a simple intensity
measurement on each RIS pixel followed by a smart algorithm, reducing at its
minimum the complexity to add on the RIS. A relaxed approach could be to
separate RIS in two categories; the first one would consist in infrastructure
RIS, close to access points, controlled by the network and used to optimize a
global coverage, while the second one, end-user RIS, would be closer to the
users and controlled by their devices only. Such differentiations would allow
to use the best feedbacks on each side for optimal quality of service. The
second mandatory point for us for RIS to be massively deployed is that they
need as minimal infrastructure to operate as possible. This means first the
possibility to control them wirelessly and in real-time, whether it is from
the network or from the devices. From the base station point of view, such
possibility should be granted by 5G, since RIS could become part of the IoT
nebulae, and be addressed wirelessly with latencies as low as milliseconds.
From the users’ point of view, real time optimization could also be
implemented, but how to do so practically remains an open question. In the
meantime, RIS should be energy self-sufficient, to enable cable free
installation and close to zero maintenance. This requires a very optimal
design in terms of energy consumption, which is relatively easy to realize in
the GHz range, but more challenging in the mmWave domain. Furthermore, RIS
should be able to extract energy from their environment. To do so, solar
panels could be used outdoor, while RF energy harvesting could be envisioned
indoor, with RIS extracting energy from the electromagnetic waves themselves,
akin to RFID.

Figure 8: A 20cm*20cm dual polarized tunable metasurface at 26-30GHz,
developed by Greenerwave for 5G mmWave access point extender (2021).

##### Conclusion

With the enormous interest raised by RIS in the wireless communications
community [12, 13, 14], we are extremely excited to see that our pioneering
proposal and Greenerwave’s original goal may finally become a reality. There
are challenges to tackle to do so but making the environment
electromagnetically smart certainly goes in the sense of history, as it is
already done for sound, heat, and even light management. Autonomy for RIS
would certainly help to go in this direction. Until then, RIS will remain
limited to some specific applications such as passive access point extenders
for mmWave, a topic we are already working on [15].

## Acknowledgment

This work has been partly funded by the European Commission through the H2020
project through the RISE-6G, HEXA-X (Grant Agreement no. 101015956).

## References

  * [1] I. M. Vellekoop and A. P. Mosk, “Focusing coherent light through opaque strongly scattering media”, _Optics Letters_ , Vol. 32, pp. 2309-2311, 2007.
  * [2] S.M. Popoff et al., “Measuring the Transmission Matrix in Optics: An Approach to the Study and Control of Light Propagation in Disordered Media”, _Physical Review Letters_ , Vol. 104, pp. 100601, 2010.
  * [3] A.P. Mosk et al., “Controlling waves in space and time for imaging and focusing in complex media”, _Nature Photonics_ , Vol. 6, pp. 283-292, 2012.
  * [4] N. Kaïna et al., “Shaping complex microwave fields in reverberating media with binary tunable metasurfaces”, _Scientific Reports_ , Vol. 4, pp. 6693, 2014.
  * [5] N. Kaïna et al., “Hybridized resonances to design tunable binary phase metasurface unit cells”, _Optics Express_ , Vol. 22-16, pp. 18881-18888, 2014.
  * [6] M. Dupré et al., “Wave-Field Shaping in Cavities: Waves Trapped in a Box with Controllable Boundaries”, _Physical Review Letters_ , Vol. 115, pp. 017701, 2015.
  * [7] “Smart Wall Focuses Scattered RF Signal”, _EE Times_ , https://www.eetimes.com/smart-wall-focuses-scattered-rf-signal/, 2015.
  * [8] S. Rotter and S. Gigan, “Light fields in complex media: Mesoscopic scattering meets wave control”, _Review of Modern Physics_ , Vol. 89-1, 2017.
  * [9] K. Chen et al., “A Reconfigurable Active Huygens’ Metalens”, _Advanced Materials_ , Vol. 29, 1606422, 2017.
  * [10] M. Boyarsky et al., “Synthetic aperture radar with dynamic metasurface antennas: a conceptual development”, _JOSA A_ , Vol. 34-5, A22-A36, 2017.
  * [11] P. del Hougne et al., “Optimally diverse communication channels in disordered environments with tuned randomness”, _Nature Electronics_ , Vol. 2, 36-41, 2019.
  * [12] M. di Renzo et al., “Smart radio environments empowered by reconfigurable AI meta-surfaces: an idea whose time has come”, _Journal on Wireless Communications and Networks_ , 129, 2019.
  * [13] E. Basar et al., “Wireless Communications Through Reconfigurable Intelligent Surfaces”, _IEEE Access_ , 7, 116753, 2019.
  * [14] E. Björnson et al., “Reconfigurable intelligent surfaces: Three myths and two critical questions”, _IEEE Communications Magazine_ , 58-12, 90-96, 2020.
  * [15] www.greenerwave.com

## Some Perspectives on
Space-Time Coding Digital Metasurfaces

### Vincenzo Galdi1* and Tie Jun Cui2

####  1 Fields & Waves Lab, Department of Engineering, University of Sannio,
Benevento, Italy

####  2 State Key Laboratory of Millimeter Waves, Southeast University,
Nanjing, China

#### *Corresponding author<EMAIL_ADDRESS>
##### Introduction

Space-time coding digital metasurfaces (STC-DMs) are a class of recently
introduced spatio-temporal metastructures which enable simultaneous field
manipulations in both space and frequency domains [1]. As conceptually
illustrated in Fig. 9, in their simplest form, STC-DMs essentially rely on a
digital, programmable metasurface platform [2] for which the electromagnetic
response of each element (yellow square patches) can be reconfigured between
two possible states (e.g., in-phase and out-of-phase reflections, associated
with the 0/1 bits) via a switching element such as a positive-intrinsic-
negative (PIN) diode. Therefore, each possible response can be encoded in a
sequence of bits that can be controlled and re-programmed via a field-
programmable gate array (FPGA). In STC-DMs, this concept is further leveraged
by introducing dynamic-modulation aspects inspired by time-modulated arrays
[3] and phase-switched screens [4], whereby the switching is controlled in
space and time according to a suitably designed 3-D coding matrix (red and
green dots).

This allows sophisticated spatial/spectral field manipulations, such as the
harmonic beam steering illustrated in Fig. 9, where an impinging beam is re-
radiated into multiple beams at different frequencies and directions, in a
controllable fashion [1]. In principle, higher-bit programmable metasurfaces
can be exploited to reduce the phase quantization error and therefore enable a
more precise control, and it should be noted that the equivalent phase
distributions at the harmonic frequencies generally exhibit a finer
quantization than that of the coding elements [1]. For instance, a 2-bit
programmable metasurface combined with a time-coding approach was demonstrated
to provide arbitrary multi-bit and even quasi-continuous programmable phases
[5]. More recently, the same principles have been exploited to attain
nonreciprocal reflection effects [6], and to address the independent and
simultaneous syntheses of prescribed scattering patterns at given harmonic
frequencies [7]. Also worth of mention are some alternative platforms relying
on varactor (instead of PIN) diodes, which allow more flexibility in the time-
modulation waveform. This has been shown to enable powerful capabilities in
manipulating the spectral distribution of electromagnetic fields [8], and
independently controlling the harmonic amplitudes and phases [9].

The joint spatial/spectral field-manipulation capabilities enabled by STC-DMs,
and their inherent (re)programmability, can be exploited to directly embed
digital information in microwave carrier signals, without the need of
conventional radio frequency circuits, paving the way for novel software-
defined wireless-communication architectures. For instance, in [8], a direct
modulation scheme based on binary frequency shift keying (BFSK) was
demonstrated by exploiting a pair of discrete frequencies to represent the
bits “0” and “1”. In [10], a quadrature phase shift keying (QPSK) modulation
scheme was implemented, demonstrating real-time video transmission with 1.6
Mbps data rate. Within the overarching framework of “reconfigurable
intelligent surfaces” [11], STC-DM platforms and concepts may find many
potential applications in future wireless communication networks.

Figure 9: Conceptual illustration of a STC-DM platform (reproduced from [1]).

##### Emerging challenges

Within the emerging paradigm of “information metastructures” [12], the
temporal dimensionality granted by STC-DM platforms represents a crucial
addition to enable novel self-adaptive, cognitive and possibly nonreciprocal
functionalities. However, in order to fully exploit the additional degrees of
freedom, significantly more complex design approaches are needed, as the joint
multifrequency syntheses are inherently entangled, and brute-force
optimization approaches may become computationally unaffordable.

From the modeling viewpoint, the simple time-domain approach developed in [1],
based on an adiabatic extension of the frequency-domain physical-optics method
in [2], seems to accurately capture the basic physics and its predictions are
in fair agreement with measurements [1, 6, 7]. However, with a view toward
integrating STC-DM platforms in complex communication systems, more
sophisticated approaches are needed, especially in the modeling of the
switching elements.

Another critical challenge is the implementation of faster switching schemes,
enabling higher modulation frequencies. The PIN diodes utilized for the proof-
of-concept demonstrations at microwave frequencies [1, 6, 7] can reach
switching rates up to hundreds of MHz, which are insufficient for operating at
THz frequencies of potential interest for next-generation wireless
communication systems.

Also of great interest would be to add some polarization-control mechanisms,
in order to enable joint manipulations in the entire (spatial, spectral and
polarization) parameter space.

##### Future developments to satisfy these challenges

On the modeling side, more accurate approaches need to be pursued, possibly
hybridizing full-wave and circuit simulations. From the design viewpoint,
semi-analytical strategies based on more sophisticated temporal coding
sequences have been proposed, which enable independent multi-order harmonic
manipulations [7]; these approaches look potentially promising, although their
spectral efficiency needs to be improved. The application of artificial-
intelligence-powered design approaches is also of great potential interest,
and still largely unexplored for STC-DM platforms.

Concerning faster modulation schemes, emerging platforms based on graphene
[13] and vanadium dioxide [14] seem potentially promising for THz frequencies,
although experimental evidence is still lacking. On the other hand,
polarization control can be in principle attained by means of anisotropic
coding elements [15]. Preliminary results from our ongoing studies indicate
the possibility to attain the simultaneous conversion of polarization and
frequency.

##### Conclusion

In summary, we have attempted a compact overview on the state-of-the art,
challenges and perspectives of STC-DM platforms. Overall, the outlook looks
very promising, with ample room for applications under the broad umbrella of
information metastructures and, more specifically, to reconfigurable
intelligent surfaces for future wireless communication systems. The reader is
referred to [16] (and reference therein) for further details.

## References

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## The Future of Intelligent
Wavefront Shaping for Smart Radio Environments

### Benjamin W. Frazier1* and Steven M. Anlage2

####  1 Applied Physics Laboratory, Johns Hopkins University, Laurel, MD
20723, USA

####  2 Department of Electrical and Computer Engineering, Department of
Physics, Quantum Materials Center, University of Maryland, College Park, MD
20742, USA

#### *Corresponding author<EMAIL_ADDRESS>
##### Introduction

As the electromagnetic spectrum becomes more congested and the environments in
which we need to operate become more complicated, control over the environment
itself becomes necessary to ensure the integrity of wireless communication
channels. Wavefront shaping with programmable metasurfaces allows wave fields
to be manipulated in both time and space, providing a method to interact with
the environment. When coupled with deep learning, intelligent wavefront
shaping serves as a catalyst, enabling smart radio environments and unlocking
applications beyond traditional wireless communication networks. In this
paper, we discuss the outlook of intelligent wavefront shaping for wave
propagation in complex environments and highlight its transformative
potential.

##### Emerging challenges

Within the emerging paradigm of “information metastructures” [1], the temporal
dimensionality granted by STC-Modern radio frequency (RF) imaging and
communications systems operate in complex environments that are susceptible to
multipath reflections which scramble propagating electromagnetic waves.
Transmitted signals in these environments experience random spatio-temporal
fluctuations which degrade or disrupt performance, particularly when combined
with competing RF emissions and a congested electromagnetic spectrum. A
“smart” radio environment (SRE) must be able to handle such conditions,
adapting on-the-fly to optimize a metric for the wireless channel [1, 2]. The
optimization should be performed over the entire propagation path, not only at
the endpoints as with traditional wireless systems.

The vision of an SRE (Fig. 10) is a self-adaptive system that can counter
scrambling of electromagnetic waves from the complex scattering environment,
ensuring operation even under degraded conditions. The ability to program the
environment can be achieved through the use of tunable metasurfaces, which can
manipulate their local surface impedance to enable on-demand beamforming [3];
these devices are inexpensive and widely available at RF wavelengths, making
them ideal for dynamic wavefront shaping applications.

Figure 10:  Conceptual diagram of a smart radio environment (SRE). A modulated
signal input to a complex environment, such as a wireless network, is
scrambled through constructive and destructive interference between the
multiple paths. A reconfigurable metasurface is then leveraged to program the
environment through intelligent wavefront shaping, reducing the interference
and allowing the input signal to be recovered. Deep learning provides the
intelligence, updating the metasurface and responding to changes in the
environment on-the-fly.

It is natural to consider the wireless channel for an SRE as a long-range,
open-world environment that may be a densely packed urban area or a sparsely
populated farm. However, an SRE is also valuable in enclosed environments,
such as a train station, a passenger compartment on an airplane, or even an
office. An SRE therefore has many potential applications, including the
ability to enhance 5G communications, protect against electromagnetic
interference, induce cold spots at specified locations [4], realize a
microwave cloak around an object [5], enable computational imaging [6], and
leverage Wi-Fi signals to allow wireless backscatter communications [7].

Complex microwave cavities can mimic these larger scale enclosures and are
extremely useful as surrogates for prototyping and experimentation [8]. Many
metasurface enabled wavefront shaping experiments have been performed in these
cavities [9, 10, 11, 12], demonstrating fine control over the scattering
parameters. As such, complex microwave cavities will play a crucial role in
the future development of SREs, with intelligent wavefront shaping serving as
an enabling technology.

The metasurface is placed inside the scattering medium, intercepting a
relatively small number of ray trajectories, sometimes with multiple bounces
off the metasurface itself. Therefore, the relationship between metasurface
commands and sensed environmental responses is extremely complex, resulting in
an ill-posed inverse problem; an issue that is exacerbated when considering
multiple distributed surfaces. Central to the vision is intelligence, meaning
the device must sense the environment and then deliberately interact with it.
The explosion of software defined radios in recent years provides a wealth of
powerful and inexpensive hardware to develop sensing prototypes. The
distinction of the architecture as “software defined” means that general
purpose hardware can be reused on many varied applications, significantly
reducing cost.

##### Future developments to satisfy these challenges

Wavefront shaping applications to date have relied on brute force trial and
error or stochastic search algorithms [11, 13]; however, the inherent
complexity makes this an ideal place to leverage deep learning. Deep learning
has enjoyed great success in the design of metasurfaces, but has had less use
in dynamic wavefront shaping applications. To successfully field a deep
learning solution, we need to address concerns with long processing times as
well as size, weight, and power. Traditional deep learning systems require
tremendous amounts of data to train. The acquisition time for sufficient
training data may be longer than the coherence time of the environment [2],
resulting in a trained solution that is no longer accurate. The concept of
reinforcement learning [14] is at the intersection of deep learning and
optimal control, and can assist here. It provides a methodology for adjusting
the SRE to environmental changes on-the-fly, and has shown great potential to
develop optimal control policies in complex and uncertain environments. To
alleviate concerns with overwhelming amounts of data, reinforcement learning
can be coupled with transfer learning, where information about previous
environments is leveraged to accelerate training [15].

Processing for deep learning is typically performed on expensive and power
hungry graphical processing units, so the footprint in terms of both cost and
power may exceed allowable margins. Computational efficiency can be increased
by compressing deep learning models through quantization and pruning [16]. The
growth of edge intelligence for connected devices in the internet of things
(IoT) has produced a demand for smaller deep learning models and cheaper, more
efficient processing, which will lead to a wider availability of viable
processing platforms.

Cabling and interface requirements grow with the number of unit cells provided
by a metasurface. The ability to address individual unit cells and switch
states as needed is critical to achieve a practical SRE. Connectors and large
cable runs form bottlenecks and tend to be the weakest links in a system, so
the capability of addressing unit cells wirelessly without further corrupting
the environment is highly desired for large element counts.

##### Conclusion

Future wireless systems, including 5G and IoT devices, will increasingly rely
on the ability to program the environment as the electromagnetic spectrum
becomes even more congested. Intelligent wavefront shaping using metasurfaces
coupled with deep learning serves as a path towards realizing an SRE, which
will be a revolutionary breakthrough for imaging and communication systems
operating in complex environments.

## References

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## Unconventional Sources for Smart EM Environments: An Inverse Scattering
Vision

### Marco Salucci1 and Andrea Massa1,2,3*

####  1 _ELEDIA@UniTN (DISI - University of Trento), Trento, Italy_

####  2 _ELEDIA Research Center (ELEDIA@UESTC - University of Electronic
Science and Technology of China), Chengdu, China_

####  2 _ELEDIA Research Center (ELEDIA@TSINGHUA \- Tsinghua University),
Beijing, China_

#### *Corresponding author<EMAIL_ADDRESS>
##### Introduction

Future standards beyond the fifth-generation (_5G_) will substantially change
the way a communication system is nowadays conceived and deployed [1]-[4]. The
progressive shift to millimeter-waves and the need for massive access and
ubiquitous wireless coverage with extreme data throughput will pose
unprecedented challenges in the design of next generation systems [1][4].
Higher capacity and link reliability, lower latency and power consumption as
well as reduced costs and complexity are just few representative examples of
several goals to be addressed in the forthcoming years by academic and
industry researchers with unconventional solutions [5][6]. Clearly, the era of
designing base-stations (_BTS_ s) in ideal propagation conditions and without
obstacles between the antenna and the mobile users is destined to end soon.
Indeed, the complex scattering environment where the communication system is
deployed cannot be no more regarded as an uncontrollable impairment to the
overall quality of service (_QoS_). Accordingly, standard line-of-sight
(_LOS_) key performance indicators (_KPI_ s) such as gain, half-power
beamwidth, and sidelobe level should be discarded in favour of _QoS_ system-
level _KPI_ s. A first step towards this direction is the so-called “capacity-
oriented” paradigm [7], which takes into account the presence of the
environment as another key “stakeholder actor” for determining the overall
_QoS_ at the receiver. Following such a recipe the overall end-to-end capacity
is optimized and, as a result, commonly undesired features such as grating
lobes now enable a profitable exploitation of the multipath propagation
scenario [7]. A step forward is to consider the obstacles between _BTS_ and
users as _enabling factors_ for building future _smart electromagnetic
environments_ (_SEE_ s). According to such a vision, the propagation channel
is not only involved in the design process, but it also assumes a _positive_
role in fulfilling demanding system-level performance requirements [8][9].

##### Emerging challenges

Many innovative ideas have been recently proposed to realize the dream of
_SEE_ s. One promising (yet widely unexplored) solution is to cover the walls
of the buildings with artificially-engineered passive/active metasurfaces to
control the propagation of _EM_ waves through anomalous reflections breaking
conventional Snell’s laws [8][9].

However, there are still many challenges to be addressed such as the study of
(_i_) innovative solutions for the synthesis of “smart _BTS_ s” able to
_sense_ the surrounding environment and _opportunistically_ exploit the
arising scattering phenomena, (_ii_) new approaches for the design of
feasible, implementable, and environment friendly smart skins for _EM_ field
manipulation, and (_iii_) cost-effective materials and manufacturing processes
for a large-scale deployment of candidate technologies including intelligent
reflecting surfaces (_RIS_ s) [9]. Finally, the synthesis of unconventional
sources on the buildings facades unavoidably encounters paramount challenges
including the realization of the desired macro-scale field manipulation
without covering windows, doors, or other “forbidden” regions. New solutions
will be also required to favour a seamless integration with the architecture
of the urban scenario by reducing the overall visual impact, as well.

##### Future trends and developments towards _SEE_ s

According to the authors’ vision, the roadmap to _SEE_ s will benefit from a
suitable exploitation of inverse scattering (_IS_) theory and methodologies to
take advantage of the scattering phenomena in future urban scenarios.

Figure 11: _SEE_ enabled by a “smart _BTS_ ” opportunistically exploiting the
surrounding environment.

According to this paradigm, the _BTS_ can be modeled as a primary source
$\underline{J}_{0}\left(\underline{r}\right)$ radiating an incident field
distribution $\underline{E}_{0}\left(\underline{r}\right)$ that induces on the
obstacles in a given volume $V$ a distributed equivalent current
$\underline{J}_{e}\left(\underline{r}\right)$,
$\underline{r}\in\mathbb{R}^{3}$ being the position vector (Fig. 11). Thus,
the field distribution $\underline{E}\left(\underline{r}\right)$ at the
receiver/mobile user terminal is given by the composition of
$\underline{E}_{0}\left(\underline{r}\right)$ with the scattered field
$\underline{E}_{s}\left(\underline{r}\right)$ radiated by
$\underline{J}_{e}\left(\underline{r}\right)$ (Fig. 11)

$\underline{E}\left(\underline{r}\right)=\underline{E}_{0}\left(\underline{r}\right)+\underline{E}_{s}\left(\underline{r}\right)=\underline{E}_{0}\left(\underline{r}\right)+\int_{V}\underline{J}_{e}\left(\underline{r}^{\prime}\right)\underline{\underline{G}}\left(\underline{r},\,\underline{r}^{\prime}\right)d\underline{r}^{\prime}$
(2)

where $\underline{\underline{G}}\left(\,.\,\right)$ is the Green’s function.
Under such assumptions, it is possible to yield the user-desired
$\underline{E}\left(\underline{r}\right)$ distribution at the receivers by
synthesizing a proper $\underline{J}_{e}\left(\underline{r}\right)$ within
$V$. One solution is to design a “smart _BTS”_ able to reconfigure
$\underline{J}_{0}\left(\underline{r}\right)$ to _opportunistically_ exploit
the known (deterministically or statistically) surrounding environment without
modifying it (Fig. 11). Accordingly, the excitations
$\underline{w}=\left\\{w_{n}\in\mathbb{C};\,n=1,\,...,\,N\right\\}$ of $N$
transmit-receive modules (_TRM_ s) of the _BTS_ become the degrees-of-freedom
(_DOF_ s) of the synthesis problem at hand, while the design is formulated as
a global optimization task aimed at minimizing the mismatch between the
synthesized field (2) and a target distribution
$\underline{E}_{t}\left(\underline{r}\right)$ defined over a given observation
domain $\Omega$ (Fig. 11). Looking at the same “picture” from a different
perspective, the synthesis of $\underline{J}_{e}\left(\underline{r}\right)$
can be carried out by designing suitable coatings on “smart buildings” walls
or introducing artificially-engineered bio-inspired “smart objects” in the
environment. In this case, the _DoF_ s are the constituent materials and/or
micro-level descriptors when printed sub-wavelength patterns are used to
realize unnatural permittivity/permeability distributions (Fig. 12).

It is worth pointing out that one paramount advantage coming from _IS_ theory
is the _non-uniqueness_ of the solution because of the unavoidable presence in
$V$ of non-radiating (_NR_) currents generating a null/non-measurable field
outside their support. Consequently, recent advances in the synthesis of
reflectarray antennas leveraging on the possibility to modify the surface
currents with additional _NR_ components still radiating the desired
$\underline{E}_{s}\left(\underline{r}\right)$ [10] are a viable and effective
strategy to realize _EM_ skins fitting geometric/feasibility constraints
(e.g., yielding $\underline{J}_{e}\left(\underline{r}\right)=\underline{0}$ in
correspondence with doors and windows).

Last but not least, implementing all mentioned ideas will also require
significant efforts to cope with the computational complexity arising from the
need for accurately modeling electrically-large scenarios. Towards this end,
System-by-Design (_SbD_) approaches are currently under development [12], as
well as their integration with efficient ray-tracing-based forward solvers and
customized machine learning/deep learning strategies to break down the time
costs of the design process [11].

Figure 12: _SEE_ enabled by a “smart building” and/or a “smart object” as
feasible sources for _EM_ field manipulation.

##### Conclusion

The envisaged pathway to _SEE_ s foresees unprecedented challenges to be
addressed. In this work, new ideas and methodologies for synthesizing
unconventional feasible/opportunistic sources have been illustrated based on
_IS_ theory and optimization.

## Acknowledgment

This work benefited from the networking activities within the Project "CYBER-
PHYSICAL ELECTROMAGNETIC VISION: Context-Aware Electromagnetic Sensing and
Smart Reaction (EMvisioning)" (Grant no. 2017HZJXSZ) funded by the Italian
Ministry of Education, University, and Research within the PRIN2017 Program
(CUP: E64I19002530001).

## References

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## Metasurface-Based Wireless Communications

### Qiang Cheng and Tie Jun Cui*

#### Institute of Electromagnetic Space and State Key Laboratory of Millimeter
Waves, Southeast University, Nanjing 210096, People’s Republic of China

#### *Corresponding author<EMAIL_ADDRESS>
Figure 13: Prototypes of the Metasurface-based wireless communication systems,
including BFSK (a), QPSK (b), 8PSK (c) and 16QAM (d) systems, where the
generated spectrum or constellation diagram are also presented.

##### Introduction

With the rapid development of wireless communication technologies, explosive
growth of personal communications has been witnessed in the past decade, which
inspired tremendous efforts to expand the network capacity, reduce the energy
consumption, and decrease the system cost. In this paper, we present a brief
review and overview on wireless communication systems based on information
metasurfaces, which have advantages of simple architecture, low cost, and easy
integration. The application of the information metasurfaces for smart
propagation environment is also introduced to overcome the multipath fading
that is widely encountered in the traditional communication scenarios.

Recent advent of metasurfaces provides new routes for wave manipulations
during the wave-matter interactions, and greatly expands the range of
available electromagnetic properties of artificial surfaces beyond the natural
materials. The metasurfaces are usually made of periodic strongly resonant
particles with extraordinary electromagnetic responses. By patterning the
particles in a well-defined manner, one can customize the amplitude, phase,
polarization, and spectrum of the reflected and/or transmitted waves, thereby
offering unprecedented degrees of freedom for device applications such as
perfect absorbers, antennas, polarizers, detectors, and sensors [1].

Nevertheless, the widespread use of metasurfaces in wireless communications is
still plagued by two significant challenges: 1) Dynamic manipulations of the
base-station beams are hard to realize by using the traditional passive
metasurfaces to boost the signal strength for moving receivers since the meta-
atoms and their spatial alignment are fixed; 2) The wave controls are
restricted in the physical domain, and digital signal processing technologies
cannot be applied during the reflection and/or transmission processes on the
metasurfaces.

A feasible solution to overcome these drawbacks is using digital coding and
information metasurfaces, which are characterized by digital coding particles
(e.g. 0 and 1 with 180∘ phase difference for 1-bit coding; 00, 01, 10, and 11
with 90∘ phase difference for 2-bit coding; etc.) [2, 3]. It was demonstrated
that the electromagnetic waves can be manipulated by changing the digital
coding sequences. When the digital coding metasurfaces are integrated with
field programmable gate array, field programmable metasurfaces are generated,
which can be used to control the electromagnetic waves in real time and
reprogrammable way. The digital coding particles provide a link between the
physical world and digital world, allowing the concepts and signal processing
methods in information science to be introduced to the physical metasurfaces,
such as Shannon entropy, convolution theorem, and addition theorem [4]-[6].
These studies set up the foundation of information metasurfaces, which bridge
the physical world and digital world to realize new information systems [7].

By tuning the external biasing voltages of the diodes in each meta-atom, the
digital coding metasurface can change amplitude and phase distributions of
incoming field dynamically. Additionally, periodic modulation is applied to
the surface reflectivity, the baseband information can be directly loaded on
the impinging carrier waves without mixer and DAC required by the
superheterodyne transmitter, leading to the simplification of the system
architecture.

As examples, several prototypes of metasurface-based communication systems are
established to accomplish wireless data transmissions in free space. Various
modulation schemes are realized in the experiments, including BFSK, QPSK, 8PSK
and 16QAM [8]-[12], as shown in Fig. 13. The measured results confirm the
feasibility of direction signal modulations with the time-coding digital
metasurfaces, as long as mapping relationships are established between the
baseband data and reflection amplitudes and/or phases. Therefore, the
metasurface can convert an unmodulated single-tone carrier into a modulated
signal owing to the spatial mixing effect.

##### Emerging challenges

One challenge is that the transmission quality greatly suffers from the non-
standard constellation diagram, as recognized in Fig. 13, because it highly
relies on the dynamic amplitude/phase modulation capability, while the two
items are strongly coupled and hard to independently adjust in an arbitrary
manner. Hence it remains a formidable challenge for the digital coding
metasurface to meet the demand of high-order modulation schemes, such as 256
QAM and 1024 QAM, since the increased constellation points require the
reflection amplitudes and phases with extremely high precision for
demodulating information correctly.

The second challenge is the dispersive nature of the metamaterials, which
makes it hard to cover multiple operation bands, e.g., the sub-6G and
millimeter band simultaneously. In the meantime, the signal modulation on a
number of sub-carriers also remains a big trouble, since independent
manipulation on hundreds or thousands of harmonics needs large quantization
bit number of the reflection/transmission phases for the meta-atoms.

##### Future developments to satisfy these challenges

From the perspective of element design, it is necessary to develop new types
of meta-atoms with wide bandwidth, simple structure and low dispersion. To
reduce the material loss and control complexity of the digital coding
metasurface, the number of diodes should be reduced as much as possible. The
biasing networks need be carefully designed to avoid significant impact on the
overall scattering performance of the metasurface.

Additionally, advanced coding theory for the metasurface is expected to play
more important roles in signal modulation for multiple harmonics to further
improve the communication system capacity. More signal processing algorithms
remain to be investigated to enhance the direct data processing ability
through the metasurface.

Toward communication applications, the digital coding and programmable
metasurfaces are also critical for controlling and reshaping the wireless
channels and environments, reaching the well-known reconfigurable intelligent
surfaces (RIS) in the wireless communication community [13]-[15]. Such studies
will benefit the understanding of the system models and enhance the operation
efficiency as well.

##### Conclusion

We present a brief review and overview of the metasurface-based wireless
communication systems. The mechanism of the direct signal modulations via the
information metasurfaces is summarized, and the advantages and limitations of
such systems are analyzed in details. The challenges and future research
trends are discussed. The metasurface-aided wireless communication brings
additional degrees of freedom for wave manipulations and may find important
applications in 6G wireless networks.

## Acknowledgment

This work is supported by the National Key Research and Development Program of
China (2017YFA0700201, 2017 YFA0700202, and 2017YFA0700203), and the National
Natural Science Foundation of China (61722106, 61731010).

## References

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## AI-enabled RIS-assisted
Wireless Communication

###  Jinghe Wang* and Shi Jin*

#### *National Mobile Communications Research Laboratory, Southeast
University, Nanjing, China

#### *Corresponding author: {wangjh<EMAIL_ADDRESS>
##### Introduction

Following the commercialization of the fifth-generation (5G) networks by 2020,
researchers are devoted to shaping the next-generation communication system,
namely sixth-generation (6G). However, high complexity networks, high cost
hardware, and high energy consumption are becoming crucial issues in 6G.
Therefore, it is imperative to explore promising technologies that can be
innovative and concise, cost-efficient, and resource-saving. With the
revolution in electromagnetic (EM) metamaterials, reconfigurable intelligent
surface (RIS) stands out in recent years due to its large capacity, low cost,
and low energy consumption properties and unique characteristics of shaping
the radio propagation environment.

RIS is a kind of artificial EM surface structure with programmable EM
characteristics, which is developed from metamaterial technology [1]. In
recent years, RIS is designed to achieve the reconfiguration function since
its structure or geometric arrangement of meta-atoms can be reprogrammed by
the external control signals. So far, the bottleneck problem in conventional
wireless communications is the uncontrollable wireless propagation
environment, i.e., signal attenuation usually effects the quality of service
(QoS), mutlipath propagation usually leads to various fading, reflection and
refraction usually cause interference. Based on RIS, those uncontrollable
characteristics are expected to be broken through by tailoring the wireless
propagation environment, which can significantly improve the performance of
the wireless system.

Over the past several years, considerable researches have been presented.
Related research areas of RIS mainly includes two aspects, one is the
theoretical research based on mathematical models and the other is the
functional implementation and performance measurement based on RIS prototypes.
For the first aspect, performance enhancement by RIS phase shift design is one
of the most essential topics. For example, reference [2] firstly proposed
joint active beamforming design at the BS and passive beamforming design at
RISs to solve the power consumption minimization problem. Reference [3]
firstly obtained a closed-form solution of the RIS optimal phase shift design
for the RIS-aided massive multiple input single output (MISO) system when only
statistical channel state information (CSI) is available. Reference [4]
presented two computationally efficient energy efficient maximization
algorithms for the BS transmit power allocation and the RIS reflecting
elements design. As for the second aspect, the RFocus RIS system of MIT [5],
the RIS prototype of Tsinghua University [6], and the ScatterMIMO of UCSD [7]
are prototype verification studies of RIS. Reference [8][9] present the path
loss modeling and measurements for RIS in sub 6G and milimeter-wave frequency
band validated by solid numerical measurement results.

##### AI-enabled RIS-assisted Wireless Communication

Conventional RIS-assisted wireless communication optimization problems are
tackled through various optimization methods based on extracted mathematical
model, which include numerous iterations and a large amount of computation.
Moreover, some modules in RIS-assisted systems can not be described well by
those models. Artificial intelligence (AI), therefore, is of great
significance in RIS-assisted systems for processing data without concrete
mathematical equation. The paradigm of configuring smart radio environment
based on AI and RIS is shown in Figure 14.

The most widely used technology in AI is machine learning. Machine learning is
a kind of algorithm that can automatically analyze and obtain rules and
characteristics from massive raw data.Generally speaking, deep learning,
reinforcement learning and federate learning are the three paradigms which are
extraordinarily effective approaches for designing RISs.

Figure 14: Paradigm of configuring smart radio environment based on AI and
RIS.

##### Deep Learning in Signal Detection and Channel Estimation

The deep learning(DL) approach presents a multi-layer like-brain neural
network, which maps the input mass data to the desired output. It is able to
mine abstract distribute feature representation to effectively tackle
optimization problems. Moreover, these feature patterns can be easily passed
to new data, making new data quickly adapt to the environmental changes, which
indicates the good generalization ability.

For those RIS-assisted wireless communication systems, DL methods are
effective for the signal detection and channel estimation problems. Firstly,
signal detection at receiver is inherently a classification problem.
Conventional detection tasks need the channel estimation, which is challenging
in RIS-based radio environment. Therefore, the DL-based detector in the
receiver in RIS-assisted wireless communications can be proposed to substitute
model-based solutions. Secondly, channel estimation is a regression problem.
It is typical that channel estimation at the receiver is carried out by
sending pilots from the transmitter. In RIS-based wireless communication
systems, channel estimation is practically challenging due to RISs’ passive
characteristic. Thus, the DL-based estimation can be proposed to estimate both
direct BS-UE channel and cascade BS-RIS and RIS-UE channel.

##### Reinforcement Learning in RIS Phase Shift Design

A reinforcement learning (RL) approach utilizes an agent to interact with the
environment and learns how to take actions in the next state. Agent learning
is to determine the optimal strategy to maximize long-term cumulative rewards.
Particularly, deep reinforcement learning (DRL) is the combination of DL and
RL, which integrates the strong understanding ability of DL and the decision-
making ability of RL.

In RIS-assisted wireless systems, the phase shift design of RIS is crucial to
maximize the benefits of deploying RIS for the best system performance. DRL-
based beamforming approach can maximize expected performance metrics by
appropriately designing the RIS phase shift, which is applied as instant
rewards to train the DRL-based algorithm. Therefore, the deep deterministic
policy gradient (DDPG) algorithm can be presented in RIS-assisted wireless
systems for optimally learning deterministic strategies in high dimensional
continuous action space.

##### Federate Learning in Over-the-air Computation

The federate learning (FL) approach is an efficient machine learning algorithm
between multiple participants or multiple compute nodes. It enables mobile
devices to collaboratively learn a shared model without frequent data exchange
between mobile devices and servers. Over-the-air Computation (Aircomp)
provides a new simultaneous access technology for FL to support fast local
model aggregation by taking advantage of the signal superposition
characteristics of multiple access channels.

For FL-based Aircomp, minimizing the aggregation error of mean square error
(MSE) quantization is the key issue to improve the learning performance. In
RIS-assisted FL system, RIS is applied to improve the propagation environment
to reduce model aggregation errors and increase the convergence rate of FL,
since RIS can control the phase shift to obtain the desired channel response,
and the MSE of model aggregation can be reduced with the help of RIS.

##### Challenges and Future developments

Due to tremendous dimensionality, those supervised machine learning schemes
heavily relies on the large training data sets to guarantee the system
performance. However, standardized data sets for training and testing are
finite. Another issue raised when using the model-driven machine learning
model is that the offline training period is always large power and resource
consuming. For most wireless devices, especially the low-complexity terminals
at the edge of the network, the available computing and storage resources are
limited.

Therefore, DRL and FL schemes could be better choices in RIS-assisted wireless
networks. DRL learns from experience rather than learns from mass data. FL
effectively reduces the huge transmission overhead and meets the requirements
of client privacy protection and data security.

##### Conclusion

We briefly elaborate AI approaches for RIS-assisted wireless systems for
essential problems including signal detection, channel estimation, RIS passive
beamforming and Aircomp. Challenges and future developments are attached for
guiding the future AI-enabled RIS-aided wireless networks. Considering AI from
the initial deployment of RIS will provide us more opportunities to take the
full advantages of AI in the performance enhancement of RIS-based smart radio
environments.

## References

  * [1] T. J. Cui, S. Liu, and L. Zhang, “Information metamaterials and metasurfaces,” Journal of Materials Chemistry C, vol. 5, no. 15, pp. 3644–3668, 2017.
  * [2] Q. Wu and R. Zhang, “Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming,” IEEE Trans. Wireless Commun., vol. 18, no. 11, pp. 5394–5409, 2019.
  * [3] Y. Han, W. Tang, S. Jin, C.-K. Wen, and X. Ma, “Large intelligent surface-assisted wireless communication exploiting statistical CSI,” IEEE Trans. Veh. Technol., vol. 68, no. 8, pp. 8238–8242, 2019.
  * [4] C. Huang, A. Zappone, G. C. Alexandropoulos, M. Debbah, and C. Yuen, “Large intelligent surfaces for energy efficiency in wireless communication,”IEEE Trans. Wireless Commun., vol. 18, no. 8, pp. 4157-4170, 2019.
  * [5] V. Arun and H. Balakrishnan, “RFocus: Beamforming using thousands of passive antennas,” in USENIX Symposium on Networked Systems Design and Implementation, Feb. 2020, pp. 1047–1061.
  * [6] L. Dai, B. Wang, M. Wang, X. Yang, J. Tan, S. Bi, S. Xu, F. Yang, Z. Chen, M. Di Renzo, C. Chae, and L. Hanzo, “Reconfigurable intelligent surface-based wireless communication: Antenna design, prototyping and experimental results,” IEEE Access, vol. 8, pp. 45 913– 45 923, Mar. 2020.
  * [7] M. Dunna, C. Zhang, D. Sievenpiper, and D. Bharadia, “ScatterMIMO: Enabling virtual MIMO with smart surfaces,” ACM Annual Int. Conf. Mobile Computing and Networking, 14 pages, Sep. 2020.
  * [8] W. Tang, M. Z. Chen, X. Chen, J. Y. Dai, Y. Han, M. Di Renzo, Y. Zeng, S. Jin, Q. Cheng, and T. J. Cui, “Wireless communications with reconfigurable intelligent surface: Path loss modeling and experimental measurement,” IEEE Trans. Wireless Commun., 2020.
  * [9] W. Tang, X. Chen, M. Z. Chen, J. Y. Dai, Y. Han, M. Di Renzo, S. Jin, Q. Cheng, and T. J. Cui, “Path Loss Modeling and Measurements for Reconfigurable Intelligent Surfaces in the Millimeter-Wave Frequency Band,” arXiv preprint arXiv:2101.08607.

## Intelligent Surfaces as an Enabling Technology
for Holographic Radio

### Davide Dardari1* and Nicoló Decarli2

####  1Department of Electric and Information Engineering (DEI)
University of Bologna, Bologna, Italy

####  2National Research Council (CNR), Bologna, Italy

#### *Corresponding author<EMAIL_ADDRESS>
##### Introduction

The requirements deriving from the conception of new applications, such as the
transmission of holographic videos and autonomous driving, have already put in
evidence the limits of the current deployment of the fifth-generation (5G)
wireless networks and the and the need to further stress the performance of
the sixth-generation (6G). Specifically, the extremization of the key
performance indexes like data-rate, users’ density, reliability, latency and
jitter is one of the directions undertaken. To meet such challenging
requirements, technology shifts are necessary in conjunction with the
significant increase of the number of antennas and exploitation of higher
frequencies.

##### Emerging challenges

The higher operating frequency does not come for free. The use of millimeter
wave and terahertz technologies translates into a larger path-loss, which can
be partially compensated by antennas densification and use of large antenna
arrays. Having massive antenna arrays means also higher hardware and
processing complexity and hence higher latency and power consumption that
barely scale with the number of antennas. Moreover, the shift towards large
antennas and high frequency poses new challenges since traditional models
based on the assumption of far-field EM propagation fail. In fact, in
classical operating conditions, i.e., small antennas and relatively low
frequency, plane wave propagation is assumed (far-field propagation).
Conversely, when the antenna becomes electrically large, the operating
condition may fall within the Fresnel region (radiating near-field
propagation). If from one side, the operation in the Fresnel region requires
the consideration of new models capable of accounting for this regime, from
the other side, it opens new unexplored possibilities to enhance the
communication performance through the introduction of new design strategies
and technologies to exploit it.

Figure 15: Holographic communication in the near- and far-field of a large
intelligent surface used as antenna.

The term holographic radio denotes a new paradigm where a wireless system is
capable of fully exploiting the characteristics offered by different EM
propagation regimes and thus approaching the ultimate limits of the wireless
channel [1]. In other words, holographic radio is intended as the possibility
to realize the complete control of the EM wave radiated, reflected and/or
sensed by an antenna, with unprecedented flexibility. At the network level,
the holographic capability is obtained if the environment is pervaded by a
possibly large number of devices capable of sensing/control the _EM-space_
(see Fig. 15).

One recent candidate technology to enable the holographic radio paradigm is
given by metamaterials, which represent the building blocks for realizing
_RIS_ [2]. In fact, metamaterials allow the manipulation of the EM field at an
unprecedented level of detail, thus enabling the design of specific
characteristics in terms of reflection, refraction, absorption, polarization,
focusing and steering, when used as reflecting surfaces or large antennas. In
the last years, the idea of deploying semi-passive reconfigurable reflecting
elements in the environment has attracted considerable attention. Such
solutions are able to create additional communication channels between a
transmitter and a receiver, thus increasing the coverage and the DoF of
wireless communication. Using reflecting surfaces, the wireless channel is not
a static entity anymore, whose knowledge is used to optimize transmitters and
receivers, but it becomes a partially-tunable element [3].

As active antennas, intelligent surfaces can be exploited to increase the
number of the design variables allowing to operate directly at EM level for
processing electromagnetic waves [4]. When such antennas are electrically
large, namely LIS, the exploitation of the near-field propagation can offer
several well-coupled _communication modes_ between a transmitter and a
receiver, even in LOS, thus enhancing the available communication DoF [5]. In
particular, it has been shown that the DoF depends on geometric quantities
(normalized to the wavelength), and that novel ad-hoc models are required to
characterize the radio link in terms of DoF and path-loss [5, 6].
Nevertheless, it is still not clear what are the actual fundamental limits in
more complex scenarios involving multipath, multiple users, relays and
reflecting surfaces, like that shown in Fig. 15, and how to design practical
and low-complexity schemes to achieve such limits.

##### Future developments to satisfy these challenges

Reaching the ultimate limits in wireless communication cannot disregard the
physical limits of EM propagation. The approaching of this limits with
practical systems requires solving several theoretical and technological
challenges, some of them summarized in the following.

_Metasurface technology and modeling:_ Metamaterial technology has seen
significant progress. Some solutions are already available such as dynamic
metasurface antennas, based on waveguide-fed metasurface [7] and multi-beam
antennas. Despite that, a significant effort is still needed to augment the
configurability of intelligent surfaces in different frequency bands as well
as to distill models capable of describing properly the characteristics of the
surface and being sufficiently abstract to be used in system design.

_Non-stationary channel models:_ One peculiarity of electrically large
surfaces is that the communication channel may be no longer stationary along
the surface and the EM propagation may happen in the near-field condition
where the wavefront is spherical. Ad-hoc channel models should be developed
and validated to account for such non-stationarity, including non-stationary
polarization and the effect of multipath caused by near/far-field random
scatterers [8].

_EM-based signal processing:_ In perspective, the flexibility offered by
metamaterials can be exploited also as a mean to shift some functionalities,
which are typically performed in the digital domain, directly to EM level with
the purpose to tackle complexity and power consumption issues, and reduce
significantly the latency, as the processing would be realized at the speed of
light [9, 10].

_Holographic radio space awareness:_ Obtaining an enhanced awareness of the
radio environment is fundamental for network optimization in terms of resource
allocation, interference management, coverage and capacity. The deployment of
intelligent surfaces working at high frequencies allows to construct and keep
updated 3D _EM images_ of the surrounding radio space describing propagation
paths, and radio sources position with high accuracy [11]. For instance,
thanks to the RIS-based reflectors, it will be possible to “look around the
corner" and hence obtain an unprecedented level of awareness about the EM
environment.

_Localization:_ The position of the surrounding devices can be inferred
through the analysis of the phase profile of the received signal in case the
devices are located in the EM near-field region (hologram-based positioning)
[10]. In the EM far-field or non-line-of-sight situations, localization
approaches taking advantage of the artificial multipath generated by RIS
deployed in the environment can be exploited.

_Channel state information:_ The estimation of the CSI is usually one of the
most critical tasks in wireless communication. Moreover, when operating in the
near-field, the channel is even more informative thus increasing the
associated complexity in estimation [3]. On the other hand, when moving to
higher frequencies, obstacles may completely block the signal, and multipath
components become sparse so that communication is mainly enabled by LOS
conditions. As a consequence, the CSI is expected to be highly correlated to
the geometric configuration of antennas, i.e., the relative position and
orientation, so that CSI estimation and localization tasks become intimately
linked and can be tackled jointly.

_Network EM theory of information:_ There is the need for a full understanding
of the fundamental performance limits as well as the development of practical
algorithms for operating with wireless networks composed of multiple users,
base stations, scatterers, and RIS under different configurations.

## References

  * [1] D. Dardari and N. Decarli, “Holographic communication using intelligent surfaces,” _arXiv e-prints_ , Dec. 2020.
  * [2] S. A. Tretyakov, “Metasurfaces for general transformations of electromagnetic fields,” _Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences_ , vol. 373, no. 2049, p. 20140362, 2015 (accessed December 01, 2020). [Online]. Available: https://royalsocietypublishing.org/doi/abs/10.1098/rsta.2014.0362
  * [3] M. D. Renzo, A. Zappone, M. Debbah, M. Alouini, C. Yuen, J. D. Rosny, and S. Tretyakov, “Smart radio environments empowered by reconfigurable intelligent surfaces: How it works, state of research, and road ahead,” vol. 38, no. 11, pp. 2450 – 2525, Jul. 2020.
  * [4] S. Hu, F. Rusek, and O. Edfors, “Beyond massive MIMO: The potential of data transmission with large intelligent surfaces,” vol. 66, no. 10, pp. 2746–2758, May 2018.
  * [5] D. Dardari, “Communicating with large intelligent surfaces: Fundamental limits and models,” vol. 38, no. 11, pp. 2526–2537, Nov. 2020.
  * [6] E. Björnson and L. Sanguinetti, “Power scaling laws and near-field behaviors of massive MIMO and intelligent reflecting surfaces,” _IEEE Open J. of the Communications Society_ , vol. 1, pp. 1306 – 1324, Sep. 2020.
  * [7] N. Shlezinger, G. C. Alexandropoulos, M. F. Imani, Y. C. Eldar, and D. R. Smith, “Dynamic metasurface antennas for 6G extreme massive MIMO communications,” _arXiv e-prints_ , p. arXiv:2006.07838, Jun. 2020.
  * [8] S. Wu, C. Wang, H. Haas, e. M. Aggoune, M. M. Alwakeel, and B. Ai, “A non-stationary wideband channel model for massive MIMO communication systems,” vol. 14, no. 3, pp. 1434–1446, Mar. 2015.
  * [9] A. Silva, F. Monticone, G. Castaldi, V. Galdi, A. Alù, and N. Engheta, “Performing mathematical operations with metamaterials,” _Science_ , vol. 343, no. 6167, pp. 160–163, 2014 (accessed December 01, 2020). [Online]. Available: https://science.sciencemag.org/content/343/6167/160
  * [10] F. Guidi and D. Dardari, “Radio positioning with EM processing of the spherical wavefront,” _IEEE Transactions on Wireless Communications_ , 2021, in publication.
  * [11] H. Sarieddeen, N. Saeed, T. Y. Al-Naffouri, and M. Alouini, “Next generation terahertz communications: A rendezvous of sensing, imaging, and localization,” _IEEE Communications Magazine_ , vol. 58, no. 5, pp. 69–75, 2020.

## Metasurfaces for Channel Characterization and Beam-Synthesis

### Okan Yurduseven1* and Michail Matthaiou1

####  1Centre for Wireless Innovation, Institute of Electronics,
Communications and Information Technology, Queen’s University Belfast,
Belfast, United Kingdom

#### *Corresponding author<EMAIL_ADDRESS>
##### Introduction

Metamaterials are sub-wavelength structures that can synthesize artificial
electromagnetic (EM) responses that are not seen in nature. One of their
particular applications can be given in the context of metasurfaces. A
metasurface is a planar aperture synthesized using an array of metamaterial
elements. Metasurfaces have been shown to be a promising candidate across a
wide range of applications, from cloaking to EM polarization manipulation [1].
Recently, a new paradigm, compressive sensing, facilitated by wave-chaotic
metasurface apertures has gained significant attention [2]. An advantage of
wave-chaotic, compressive metasurface antennas is that they can radiate highly
orthogonal modes controlled by a simple frequency sweep [3] or dynamic
modulation of the metasurface aperture [4]. Using these wave-chaotic modes, it
has been shown that the raster scanning requirement of conventional sensing
schemes can be relaxed. Hence, instead of relying on a multi-pixel, point-by-
point scan, the metasurface layer can be used to encode the scene information
and compress it into a single channel. Such an implementation can
significantly reduce the number of data acquisation channels, and hence,
simplify the hardware architecture.

Intelligent reflecting surfaces (IRS) can be considered a distinct form of
metasurface type apertures. An IRS consists of reflection-based unit cells
that can collectively alter the amplitude and phase response of an incoming EM
wave to synthesize a desired wavefrom of interest on the IRS aperture. One
particular application of IRS type apertures in wireless communication systems
is to achieve beam-synthesis, ensuring that a communication link is
established between an IRS and an end user. As a result, it is evident that an
IRS type of aperture requires a channel characterization to be able to steer
the synthesized radiation pattern.

##### Emerging challenges

An IRS uses a holographic principle to synthesize the desired aperture
wavefront as depicted in Fig. 16.

Figure 16: Depiction of a reflective surface for holographic beam-synthesis,