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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 1
Intelligent Beam Steering for
Wireless Communication Using
Programmable Metasurfaces
Nouman Ashraf , Taqwa Saeed, Hamidreza Taghvaee , Sergi Abadal , Member, IEEE,
Vasos Vassiliou , Senior Member, IEEE, Christos Liaskos ,
Andreas Pitsillides , Senior Member, IEEE, and Marios Lestas , Member, IEEE
Abstract— Reconfigurable Intelligent Surfaces (RIS) are well
established as a promising solution to the blockage problem
in millimeter-wave (mm-wave) and terahertz (THz) communi-
cations, envisioned to serve demanding networking applications,
such as 6G and vehicular. HyperSurfaces (HSF) is a revolutionary
enabling technology for RIS, complementing Software Defined
Metasurfaces (SDM) with an embedded network of controllers
to enhance intelligence and autonomous operation in wireless
networks. In this work, we consider feedback-based autonomous
reconfiguration of the HSF controller states to establish a reliable
communication channel between a transmitter and a receiver
via programmable reflection on the HSF when Line-of-sight
(LoS) between them is absent. The problem is to regulate the
angle of reflection on the metasurface such that the power at
the receiver is maximized. Extremum Seeking Control (ESC)
is employed with the control signals generated mapped into
appropriate metasurface coding signals which are communicated
to the controllers via the embedded controller network (CN).
This information dissemination process incurs delays which
can compromise the stability of the feedback system and are
Manuscript received 6 May 2021; revised 17 October 2022; accepted
7 January 2023. This work was supported in part by the European
Union through the Horizon 2020: Future Emerging Topics Call
(FETOPEN), under Grant EU736876; in part by the Project VISORSURF
(http://www.visorsurf.eu), Research and Innovation Programme, under
Agreement 739578; and in part by the Government of the Republic
of Cyprus through the Directorate General for European Programmes,
Coordination, and Development. The Associate Editor for this article was
S. H. Bouk. (Corresponding author: Nouman Ashraf.)
Nouman Ashraf is with the School of Electrical and Electronic
Engineering, Technological University Dublin D07 EWV4, Dublin, Ireland
(e-mail: nouman.ashraf@tudublin.ie).
Taqwa Saeed is with the School of Information Technology, Halmstad
University, 30118 Halmstad, Sweden.
Hamidreza Taghvaee is with the Department of Electrical and Electronics
Engineering, George Green Institute for Electromagnetics Research, Univer-
sity of Nottingham, NG7 2RD Nottingham, U.K.
Sergi Abadal is with the Department of Computer Architecture, Universitat
Politècnica de Catalunya, Barcelona, Spain.
Vasos Vassiliou is with the Department of Computer Science, University of
Cyprus, 1678 Nicosia, Cyprus, and also with the Research Centre on Inter-
active Media, Smart Systems and Emerging Technologies (RISE), Nicosia,
Cyprus.
Christos Liaskos is with the Institute of Computer Science, Foundation of
Research and Technology Hellas, 70013 Heraklion, Greece.
Andreas Pitsillides is with the Department of Computer Science, Univer-
sity of Cyprus, 1678 Nicosia, Cyprus, and also with the Department of
Electrical and Electronic Engineering Science, University of Johannesburg,
Gauteng 2006, South Africa.
Marios Lestas is with the Department of Electrical and Computer Engineer-
ing and Informatics, Frederick University, 3080 Nicosia, Cyprus.
Digital Object Identifier 10.1109/TITS.2023.3241214
thus accounted for in the performance evaluation. Extensive
simulation results demonstrate the effectiveness of the proposed
method to maximize the power at the receiver within a reasonable
time even when the latter is mobile. The spatiotemporal nature
of the traffic for different sampling periods is also characterized.
Index Terms— Beam steering, extremum seeking control,
hyperSurface, intelligent reflecting surfaces, metamaterial, pro-
grammable wireless environments.
I. INTRODUCTION
FUTURE generations of wireless communications are
envisioned to provide several Gbps data rates per user
for billions of users simultaneously [1]. The proliferation
of billions of Internet of Things (IoT) devices in various
industries has pushed wireless networks to their limits in
terms of the required capacity to effectively communicate the
vast amounts of data that are generated [2]. Moreover, the
increasing number of IoT devices is changing mobile commu-
nication services from interpersonal communication to smart
interconnection among billions of devices. 6G is expected to
fulfill the requirements of a fully connected world and provide
ubiquitous wireless connectivity for all [3], adopting transfor-
mative solutions, e.g. intelligent surfaces and programmable
wireless environments [4], [5], [6], [7]. Furthermore, it is to
be noted that among these interconnected devices, millions
of vehicles fitted with onboard communication systems and
a range of autonomous capabilities are being increasingly
phased in as part of this network of connected devices [8].
According to the US Department of Transportation and the
Connected-Intelligent Transportation System (C-ITS) initiative
of the European Commission [9], [10], this connectivity would
enable the vehicles to participate in intelligent transportation
systems (ITS) such as See-Through, High-Density Platoon-
ing, Automated Overtake, and so on [11], [12]. However,
high data rates and reliable communications are needed for
next-generation vehicular networks. For example, applications
such as See-through vision and bird’s eye view necessitate
a data rate in excess of 50 Mbps and a delay of 50 ms.
Likewise, automatic overtake necessitates a 10 ms delay and
99.999 percent reliability [13], [14].
Existing communication technologies have limitations on
achievable speeds. For example, Dedicated Short-Range
Communication (DSRC) such as IEEE 802.11p/DSRC and
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2 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Fig. 1. Potential use cases of metasurface assisted wireless communication.
ITS-G5/DSRC, and 3GPP’s Long Term Evolution (LTE) tech-
nologies which were adopted for vehicular communication
purposes have been shown to pose limitations on the achiev-
able speeds (6 Mbps for DSRC and up to 100 Mbps for
LTE-A) [15], and key stakeholders have sought solutions in
the millimeter-wave (mm-wave) [16], [17] and even terahertz
(THz) bands [13].
Frequency allocation in the mm-wave and THz range can
offer tremendous bandwidth as compared to microwave sig-
nals, however, such high-frequency signals suffer from high
path losses and atmospheric absorption thus necessitating
highly directional transmissions. As a result, they rely on
directional antenna technologies [18] to increase the antenna
gain [19]. Moreover, directional beams can serve the purpose
of reducing interference from signals of non-interest [20], [21].
To best serve these functions, transmitter and receiver align-
ment is a critical process [22], however, it is greatly challenged
by node mobility and blockage, typical in vehicle-to-vehicle
(V2V) and vehicle-to-everything (V2X) communications. It is
well established that key challenges in mobile mm-wave
networks are effective beam management under high mobility
and blockage recovery [23], [24].
Several beam management schemes have been proposed in
the literature to address the aforementioned challenges [25],
[26], [27], with notable attempts incorporating techniques such
as beam switching [28], 3D interaction with the spatial channel
profile [29], out-of-band direction inference [30], decoupling
of transmitter and receiver steering [31] and predictability
emanating from the abundance of available data [32], [33].
It must be noted here, that beam steering is a significant
part of the beam management problem, relying on reconfig-
urable antennas. Beam steering techniques beyond beamform-
ing which is common in multiple-input and multiple-output
(MIMO) 5G systems are found in literature [34].
One of the key technologies to address the blockage recov-
ery problem as shown in Fig. 1are Reconfigurable Intel-
ligent Surfaces (RIS) [35], [36], also known as Intelligent
Reflecting Surfaces (IRS) among other names, which can
be realized using a number of technologies as for example
reflect arrays [37], [38], [39] or metasurfaces [40], [41] with
recent designs reported even in the THz band [42], [43].
Beam training in the presence of RIS has been recently
addressed in [44], [45], [46], and [47] for single and multi-
user cases. The approaches therein, however, are generic
without reference to the underlying RIS technology. Beam
management in 802.11ad networks assisted by reconfigurable
reflects arrays has been addressed in [48] leading to a
three-party beam searching protocol involving the RIS. The
beam searching protocol involves the second stage of beam
training providing finer control of the link between the RIS
to the receiver. The approach is accompanied by experimental
verification results and is compatible with existing 802.11ad
transceivers. However, metasurfaces, owing to subwavelength
apertures which allow for finer control of the electromagnetic
waves [49], have distinct advantages relative to competing
technologies and have thus recently attracted significant atten-
tion. Beam management in the presence of metasurface RIS
is an open problem [50] and a number of recent works have
considered online tuning of metasurface parameters to opti-
mize the transmitter-receiver channel via the RIS [51], [52].
These approaches are feedback based and aim toward the
standalone operation of metasurfaces without human inter-
vention. However, the aforementioned approaches are based
on machine learning techniques which might lead to slow
convergence due to model training. Moreover, they do not
account for the implementation details of the metasurface i.e.
the metasurface coding that will yield the desired functionality
and the methodology with which the control signals will be
disseminated to the reconfigurable unit cells. As such, the
effect of delays on the performance of the closed-loop system
is not accounted for. A notable work incorporating feedback,
aiming towards autonomous stand-alone operation is reported
in [53]. In that work, practical aspects of a 2-bit digital pro-
grammable metasurface are accounted for, and experimental
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ASHRAF et al.: INTELLIGENT BEAM STEERING FOR WIRELESS COMMUNICATION 3
Fig. 2. Layers of the HSF system.
results demonstrate the effectiveness of the proposed design.
However, the thrust there-in is different aiming for orientation
control with the desired angles fed directly to the metasurface
controller.
In this work, different from the aforementioned approaches,
we consider feedback-based iterative beam steering, based
on power measurements at the receiver, taking into account
the required coding patterns of a multi-state metasurface
and the methodology with which reconfiguration signals are
sent from the metasurface controller to the appropriate unit
cells. The underlying architecture is based on the recently
proposed hypersurface (HSF) paradigm [4] which integrates an
embedded network of controllers (CN) on the software-defined
metasurface (SDM) whose unit cells can assume multiple
states by suitable choice of the varactor values within the
unit cell [54]. A well-known form of extremum seeking
control (ESC) is adopted to serve as the feedback-based
iterative control algorithm [55] whose effectiveness to track
mobile targets with speeds beyond 100 km/h is demonstrated,
rendering it suitable for V2X scenarios. Our contribution goes
beyond that, to characterize the “workload” incurred within
the metasurface as a result of the iterative control procedure
and the packetized directives sent to each of the unit cells.
This workload procedure allows us to account for the feedback
delays associated with message transmission and can also be
used to characterize the energy consumption of the overall
procedure.
The work presented in this paper is based on our previous
work in [56], however, in this extension, we consider changes
in both the elevation and azimuth angles and in addition
demonstrate the effectiveness of the methodology to track
mobile targets, even in the presence of delays and fading. The
main new contributions of this paper relative to our previous
work are outlined below:
•Simultaneous control of the azimuth and elevation angles
is realized via a pair of independent Extremum Seeking
Controllers (only elevation angle changes were consid-
ered in our previous work).
•The effectiveness of the proposed scheme is demonstrated
in the presence of mobile receivers typical in vehicu-
lar environments with indicative performance bounds in
terms of the achievable speed.
•The discrete-time version of the ESC is presented and the
effect of the sampling period is investigated.
•The load within the CN and the effect of delays are char-
acterized for the newly introduced scenarios as outlined
above.
•It is demonstrated that the convergence time of the
proposed scheme can be significantly improved by
fine-tuning the controller parameters.
•The effectiveness of the proposed scheme is shown in the
presence of shadow fading.
The paper is organized as follows. In Section II, we describe
the considered HSF, in Section III, we formulate the
problem mathematically and present the utilized controller.
In Section IV, we evaluate the performance of the closed loop
system, and finally, in Section V, we offer our conclusions
and future research directions.
II. HYP ERSURFAC E SYSTEM DESCRIPTION
A. Hypersurface Structure
The technology considered in this work for the realization of
the RIS is the Hypersurface which complements the SDM with
an embedded controller network (CN) to provide programma-
bility and autonomous operation. A detailed description of
the HSF is provided in recent literature [57] and a reference
HSF is shown in Fig. 2including multiple layers such as the
gateway layer, embedded control layer, and EM. In short, the
metasurface embodies the EM layer as a collection of unit cells
that modify the impinging wave by demand of the correspond-
ing application programming interface calls. In more detail,
the EM layer concerns the physical realization of the deep
subwavelength unit cells or meta-atoms of the metasurface,
as well as of the tunable actuators that allow controlling the
reflection. Coupling between the elements depends on the
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4 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
metasurface design such that if the size of the elements is
larger than the spacing the coupling is high and vice versa.
The high density of elements allows for the micromanagement
of EM waves at the level of electric and magnetic field
vectors [58]. It is in fact the interaction of an impinging
wave with the unit cells that induces local currents in the
EM layer and, by controlling these currents as secondary
EM sources, one can manipulate the scattered field wavefront.
The characteristics of these secondary currents are essentially
determined by the impedance of each unit cell as seen by
the EM wave. This means that we need to apply individual
and independent changes to each unit cell’s impedance to
effectively engineer the wave. To do so, tunable elements
need to be integrated into each of the unit cells to tune their
resistance R and capacitance C as real and imaginary parts
of the surface impedance, respectively. These elements may
be circuit based, such as varactors and varistors, which have
been adopted in the prototype under development [54], [59].
However, as these reach their operational limits at mmwave
frequencies, despite very recent advancements which can also
render them suitable [60], other tuning technologies may prove
to be more suitable, for example, nematic liquid crystals [61].
An embedded control layer [62] exists behind the EM
layer, which includes the hardware and protocols required
for transferring information between the gateway (GW) and
the tuning elements at each unit cell to modify their state.
The control layer, in its simplest form, can be the direct
interconnection from the GW to all unit cells, which can be
practical for some programmable wireless environment (PWE)
applications [63]. However, as more unit cells are integrated
into an HSF, a more scalable network of embedded controller
chips is required. We refer to this network as CN [64]. In this
case, it is assumed that each controller chip can only apply a
discrete number of states with cardinality Ns, whereby each
state is a combination of RC values. The GW layer operates
on top of the embedded control layer. The GW layer not
only connects the HSF to the external world via standardized
protocols but also converts external software commands into
specific metasurface codes. The metasurface code is the set
of unit cell states or RC values, out of the Nsavailable ones,
by which the metasurface achieves the desired functionality
with low error. The code is composed by the GW through
the distribution of simple individual messages in the CN that
trigger the appropriate changes of state at each unit cell.
B. Metasurface Coding
The code to be applied to a metasurface depends not
only on the functionality but also on functionality-dependent
inputs [65]. Here, we describe the process of metasurface
coding for the particular case of anomalous reflection used
in beam steering. For a metasurface in reflection mode, unit
cells shall be designed for high reflection amplitude and wide
phase range. Anomalous reflection is achieved by applying
a phase gradient to the impinging wavefront [66]. In particular,
phase gradients are applied in the xand ydirections, where x
and ydirections define the plane on which the HSF is located.
Let us denote the reflection phase as 8. Then, the desired
reflection phase gradients in the xand ydirections are
8x=∂8/∂ xand 8y=∂8/∂ y, respectively. By applying
such gradients, the phase of a unit cell at location (m,n), i.e.
the m-th column and n-th row, can be expressed as:
8mn =(∂ 8
∂xm+∂8
∂yn)Du+800,(1)
where Duis the lateral size of a square unit cell, and
800 is the phase of the first unit cell (whose value can be
arbitrary, since the importance lies in the phase gradient).
In order to relate the target reflected angle ({θr, φr}in polar
coordinates: where θrand φrdenote the elevation and azimuth
components respectively) with the angle of incidence ({θi, φi})
and the phase gradients implemented in the HSF (8xand 8y),
we apply the momentum conservation law for wave vectors as:
8x=krsin θrcos ϕr−kisin θicos φi,(2)
8y=krsin θrsin ϕr−kisin θisin ϕi,(3)
where kr=2π
λrand ki=2π
λiare the wave vectors of the
reflected and incident mediums with wavelengths λrand λi,
respectively. In this paper, we consider that λi=λr=λ0
because the host medium for most communication applications
is generally air. Since the deflection angle (the difference
between the incident and reflected beam angles) is dictating
the phase gradient (not the absolute value of the incident
and reflected beam angles), we can derive the formula for
normal incidence (θi=0) and then extend it to arbitrary
cases [67], [68].
8mn =2πDu(msin θrcos ϕr+nsin θrsin ϕr)
λ0
.(4)
Since the number of unit cell states is limited to Ns, the
target phase 8mn is actually mapped to that of the closest
state [69]. In any case, if the incidence or reflected angle
changes, the HSF code will need to change to accommodate
the new required phase gradients, triggering the transmission
of internal commands from the GW to the concerned unit cells.
C. Far Field Evaluation
A model is necessary to determine the stability of the
communication link by comparing signal power at the receiver
and its sensitivity. To obtain the received power, one must
calculate the far field pattern of the metasurface. Since the
size of the unit cells is small compared to the wavelength,
we can assume that the current distribution on the patches is
uniform. In other words, we can model each unit cell as an
independent punctual source and apply Huygens’ principle to
obtain the far field pattern as the sum of the contributions of
all unit cells. This model assumes that the crosstalk between
adjacent unit cells is negligible, which can be enforced with
proper unit cell design and spacing. Further, we model the
scattering pattern of the unit cell with the function cos(θ).
Under full illumination of the metasurface, the scattering field
can be expressed as [68]
E(θ, ϕ) =Kcos θ
M
X
m=1
N
X
n=1
0mn ej[8mn +k0ζmn (θ ,ϕ)],(5)
where Kis a constant defined by the incident amplitude,
0mn is the reflection coefficient (ideally it has to be unity),
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ASHRAF et al.: INTELLIGENT BEAM STEERING FOR WIRELESS COMMUNICATION 5
Fig. 3. Physical layout of the system.
8mn is the reflection phase of unit cell (m,n),Mand Nare
the number of unit cells in a row and a column, k0is the
wave number and ζmn(θ, ϕ) is the relative phase shift of the
unit cell with respect to its coordinates (θ, ϕ), given by
ζmn (θ, ϕ ) =Dusin θ[(m−1
2)cos ϕ+(n−1
2)sin ϕ].(6)
This method has proven to be accurate in evaluating the far
field of a metasurface for beam steering by comparing the
results with full-wave simulations [67], [68], [70] and has been
used extensively in a number of recent studies [46], [47], [67],
[68], [70], [71], [72]. The approximations made have a small
impact on the radiation pattern.
III. BEA M STEERING ALGORITHM
A. Problem Formulation
We consider a scenario comprising of a transmitter,
a receiver, and a Hypersurface acting as the RIS (H-RIS). The
transmitter, due to the absence of LoS communication with
the receiver, attempts to establish a reliable communication
channel via the H-RIS. A beam is directed towards the
H-RIS and then the objective is to iteratively re-configure the
controller states so that the impinging wave is directed towards
the receiver. Despite the fact that the channel reliability can be
assessed by a number of metrics, here we pose the objective of
maximizing the received signal strength. Note that maximizing
the signal strength may not be optimal in terms of channel
quality but consideration of alternative metrics is left for future
work. It is also worth noting that the same methodology
and solution are applicable to the setting of a directional
SDM-based transmitter, where the objective is to direct the
outgoing beam towards the receiver. This may find significant
applications in the mm-Wave and THz bands and in space
applications. Below we introduce the necessary notation to
formalize the described problem depicted graphically in Fig. 3.
The transmitted beam is incident on the H-RIS with the
corresponding vector valued angle of incidence ψi= [θi, φi]
comprising two components relative to the RIS plane, the
elevation angle θiand the azimuth angle φi. Depending on the
configuration of the controller states, the beam is reflected on
the H-RIS with a reflection angle ψr= [θr, φr], where θrand
φrdenote the elevation and azimuth components respectively.
The reflection angle is the control variable or actuation signal
which is dictated by the controller states. The controller states
are updated by packetized directives which are sent by the
input GW, one by one at the beginning of each update period.
The dissemination is challenging, in terms of deadlock-free
delivery,1by the unique Manhattan type topology of the
controller network [74] which comprises of N=M×L
controllers, where Mand Ldenote the number of controllers
along a single row and a single column respectively. These
challenges have been addressed in [75] and the workload char-
acteristics have been characterized in [69]. Here, for simplicity,
we assume an “agnostic” XY routing mechanism and unlike
previous work, the workload is characterized in the considered
feedback setting where the update times are determined by
the sampling period (in [69] the updates are determined by
the angular step). Such a characterization is necessary, as the
delivery of the reconfiguration packets from the input GW to
all the controller nodes which need to be updated to realize a
particular reflection angle, is associated with a delivery delay,
the main source of the overall feedback delays. Feedback
delays are well known from the control systems literature [76],
[77] to compromise the stability of closed-loop systems such
as the one considered here. Further information on the packet
delivery delays is provided in section IV, however, it is to be
noted that they are time-varying with specific bounds. Further,
given the architectural properties of the metasurface (e.g.the
number of states, the CN size, and the routing algorithm), the
delays only depend on the change of the reflection angle to
be realized and this is the approach adopted in section IV for
their characterization.
The controller states c1,c2, . . . cNare lumped into a vector
c= [c1,c2, . . . cN]with each entry assuming a value from the
set S=s1,s2,s3, . . . sNs. Based on the chosen controller states
and the angle of incidence, a particular reflection angle is real-
ized based on the metasurface coding procedure described in
the previous section. This relationship is expressed via a func-
tion f(.) such that ψr=f(c, ψi). The receiver at any time tis
located at the position l(t), with corresponding coordinates in
3D space. The function l(t)defines its time-varying trajectory.
The receiver is appropriately equipped to measure the received
power P(t). The received power is considered as a function
of the transmitter power Pt, the location of the receiver,
the incidence angle, and the HSF controller states which
yield a far-field pattern as described in the previous section.
This is expressed via the function g(.) such that P(t)=
g(c, ψi,l(t), Pt). Based on the latter, one may consider cto be
the control variable, posing the objective of choosing cdirectly
such that the power is maximized. This can be cast as a multi-
input single-output (MISO) control problem, which, however,
poses challenges due to the multi-dimensionality of the input
space. To overcome this, we account for the function f(.) in
g(.), considering the reflection angle as the control variable
instead of c. This results in the composite function v(.) such
that P(t)=v(ψr, ψi,l(t), Pt), rendering the control variable
(i.e. the reflection angle), two-dimensional as compared to the
multidimensional case, had each controller been considered
1We utilize the term “deadlock-freedom” as used in the seminal work by
Duato [73] all over the paper; that is, message traversals along the CN network
are devoid of cyclic patterns as no circular channel (i.e., link) dependencies
are allowed.
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6 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
independently. This change in the input space also ensures the
concavity of P(t)with respect to the input variable ψr, which
is important in the application of ESC. So the considered
optimization problem becomes:
P:max
ψr∈[−90,90]P(t). (7)
The difficulty in solving the problem above stems from the
following:
•The functions f(.) and g(.) although appropriately spec-
ified using the modeling procedures described in the
previous section are subject to uncertainty due to mod-
eling errors, which are inevitable due to the complex
interactions, and also due to the inaccuracies that may be
incurred during the construction phase of the HSF and
the probability of hardware faults and run-time errors.
This, coupled with their highly nonlinear nature suggests
that the true behavior can severely deviate from the one
predicted by the assumed model.
•External disturbances acting at different points in the
considered system are random in nature.
•The assumed single point of measurement in 3D space
at the location of the receiver (assumed unique and
dimensionless) only allows partial observation of the
system states.
•The angle of incidence may be unknown to the HSF and
the receiver.
•The location of the receiver which may be unknown to
the HSF.
The aforementioned challenges and especially the lack of
robustness with respect to model uncertainties and external
disturbances, hint towards the consideration of a closed loop
implementation rather than an open loop implementation [48].
Open loop implementations may be realized if, for example,
the location of the the receiver is known and the incidence
angle can be estimated via a suitable algorithm. Such an
approach, however, does not account for modeling and estima-
tion errors which can yield a significant bias from the desired
reflection angle. For closed-loop implementation, we assume
the existence of a control channel that allows the receiver
to communicate the received power to the input gateway of
the HSF. The propagation delay is assumed negligible. The
input gateway then processes the received power according
to the control algorithm to be designed and generates the
desired reflection angle ψr. The latter is then transformed
into the controller states c, via the inverse of the func-
tion f(.), assumed to be one-to-one. The desired controller
states are communicated to the corresponding controllers via
the embedded controller network as described earlier. Once
the controller messages are sent, the controller states abide
by these directions, the Far Field pattern is modified and the
procedure is repeated. We thus consider a control law to be
implemented at the input gateway which takes as input the
measured power Pand yields the desired reflection angle.
A general description of the control law [78], in the form of
a nonlinear state space representation is given below:
(˙q=h(q,P)
ψr=v(q,P), (8)
Fig. 4. Feedback-based beam steering control.
where ψr(t)is the desired angle of reflection, P(t)is the
measured signal strength, qis a vector of controller states, ˙q
is a vector of changes in controller states and h(.) and v(.) are
possibly nonlinear functions. The problem is then to design the
functions h(.) and v(.) such that problem Pis solved. A block
diagram of the feedback system, as described above is shown
in Fig. 4(a).
B. Proposed Control Algorithm
ESC is a model-free adaptive control method, deemed suit-
able for the problem under consideration due to the compat-
ibility with the posed received signal maximization objective
and the uncertain, nonlinear nature of the cost function, highly
dependent on the incidence angle which is hard to estimate.
In this scheme, the input signal is perturbed sinusoidally
and the resulting change in the output signal is measured
to determine and follow a local maximum of a measurable
objective function.
Since the maximization must be done with respect to both
the elevation and azimuth angle, the controller takes the form
of a 2-dimensional matrix. Here we consider decoupled control
action [79], i.e. the matrix is diagonal with each diagonal
entry referring to the elevation and azimuth angle respec-
tively. Coupled control action with off-diagonal elements for
example in [80] is left to be explored in the future. For
clarity of presentation, we present below the controller for
the elevation angle θrwith the algorithm being the same for
the φr. Fig. 4(b) shows a schematic representation of the
employed ESC scheme, where P(t)and θ (t)are the controller
input and output respectively, with the latter identified as the
control variable. As shown in Fig. 4(b), a perturbed signal
θris obtained by adding to the best estimate of the optimal
input signal ˆ
θa dither signal which in this case takes the
form of a sinusoidal αsin(ωt). The updated θ (r)is realized
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ASHRAF et al.: INTELLIGENT BEAM STEERING FOR WIRELESS COMMUNICATION 7
via appropriate HSF reconfiguration yielding a new received
power P(t)which is measured. A high pass filter (HPF) is
used to remove undesired steady state components, with the
filtered signal then demodulated by means of multiplication
with the dither signal αsi n(wt). Unwanted high-frequency
components are induced by this procedure which is removed
by the low pass filter (LPF). Integral action is then employed to
improve the best estimate ˆ
θ. The integration basically imple-
ments a gradient policy of the time-averaged system aiming
to maximize the cost function, typical in adaptive control. The
complete algorithm can be represented in continuous time as
follows:
P(t)=f(θr(t))
θr(t)=ˆ
θ(t)+αsin(ωt)
˙
ˆ
θ(t)=kζ(t)
˙
ζ (t)= −ωlζ(t)+ωl(P(t)−η(t))α sin(ωt)
˙η(t)= −ωhη(t)+ωhP(t),
(9)
where ωis the frequency of the perturbation signal, kis the
gain, ωlrepresents the cut-off frequency of the LPF and ωhis
the cut-off frequency of the HPF. The principle of operation
of the algorithm can be explained as follows [81]: At the time
instants when ˆ
θis on either side of the maximum point θ∗,
the periodic dither signal excites a periodic response of P(t)
whose dc component is removed by the high pass filter. The
remaining periodic response can either be in-phase with the
perturbation signal (if θis smaller than θ∗) or out of phase if θ
is larger than θ∗. In the former case, the dc component of the
product of the two signals, which is extracted using the low
pass filter, is positive and thus, after the integration process,
drives the estimate of θ“uphill” towards the maximum. In the
latter case, the dc component is negative and thus drives
the estimate “downhill” towards the maximum. One can thus
view the modulation/demodulation procedure as a gradient
estimation procedure whose sign is retrieved and integrated.
An alternative derivation, which again supports the reasoning
of the method acting as a gradient estimation algorithm can
be found in [81] and [82].
C. Discrete Time Implementation
In practice, the continuous time algorithm needs to be trans-
formed into its discrete time equivalent due to the suitability
of the latter for implementation on the HSF gateway. The
discrete-time algorithm and the chosen sampling period are of
primal significance for the embedded CN as higher sampling
frequencies lead to higher data traffic and consequently more
energy consumption. The traffic aspect is investigated in the
performance evaluation section. Below, we present the discrete
time ESC algorithm.
P(k)=f(θr(k))
Ph(k)= −h Ph(k−1)+P(k)−P(k−1)
ζ(k)=Ph(k)α sin(ωk)
ˆ
θ(k+1)=ˆ
θ(k)−γ ζ(k)
θr(k+1)=ˆ
θ(k+1)+αsin(ω(k+1))
(10)
where kdenotes the discrete time index, P(k)is the received
signal power at instant k,θr(k)is the angle of reflection at
time kand Ph(k)is the received signal power after high pass
filtering at instant k. The LPF parameter γis chosen such
that γ > 0, the HPF parameter his constrained by 0 <h<
1 and the modulation frequency ωis usually set according to
ω=βπ , with 0 <|β|<1 and βbeing rational.
IV. PERFORMANCE EVALUATIO N
In this section, we evaluate the performance of the proposed
control scheme investigating its ability to guide the reflected
beam towards a possibly mobile receiver so that the received
power is maximized. A unique feature of the system under
consideration is the embedded CN through which packetized
directives are forwarded to the metasurface controllers. The
delivery procedure is associated with forwarding delays and
the effect of these delays on the convergence properties of
the algorithm is assessed. In addition, the workload and the
traffic within the CN are critical to be characterized as the
load is aimed to be kept at a minimum. Towards this end
the Spatio-temporal traffic patterns for different sampling
times are also investigated.
The evaluation is conducted using two simulators, one
developed on Matlab and one developed using the Analogic
simulation platform. The latter models the process of deliv-
ering packetized directives within the HSF embedded CN
taking into account its unique characteristics, for example, its
asynchronous operation. The Matlab simulator comprises three
main components. The controller implementation, the function
mapping the reflection angle generated by the controller to
the controller states, and the module which characterizes the
Far Field pattern as a result of the chosen controller states.
The Far-field pattern dictates the received signal strength.
Throughout the evaluation procedure, we consider received
power values normalized by the maximum value such that 0 ≤
P(t)≤1. Time-varying receiver profiles which incur changes
for both the azimuth and elevation angles are considered.
A. Static Receiver
The base scenario of the initial simulation experiments
assumes zero feedback delays. We consider initial elevation
and azimuth reflection angle equal to 45oand 50orespectively,
with the receiver placed at a location such that the desired
corresponding reflection angles are θ=55oand φ=60o.
Fig. 5depicts the time evolution of the elevation and azimuth
angles of the reflected beam together with the normalized
power of the received signal. The results demonstrate that the
ESC scheme is successful in directing the beam towards the
desired direction, enabling the normalized received power to
rise from approximately 0 to 1. It is to be noted that in our
previous work [56], the convergence time was rather slow,
of the order of a couple of seconds, thus, a major objective
of the current work is to further reduce the convergence time
by appropriately tuning the controller parameters. This tuning
procedure has enabled the ESC algorithm to guide the beam to
the vicinity of the target location within 1 second as indicated
in Fig. 5. In addition, the radiation patterns, in the form
of normalized heat maps for different azimuth and elevation
angles, are depicted graphically in Fig. 6at the start of the
simulation experiment (t=0sec) and after the system reaches
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8 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Fig. 5. Simultaneous control of the elevation and azimuth reflection angles
in the case of a stationary receiver when no feedback delays are present.
Fig. 6. Radiation Pattern.
Fig. 7. Coding of a 24 ×24 metasurface with Ns=4.
the steady state within reasonable proximity at t=2.03 seconds.
It can be observed that most of the intensity is concentrated
around the center point, which shifts from 50oto 60oon
the x-axis and 45oto 55oon the y-axis. Fig. 7shows the
corresponding phase profile for the 24 ×24 metasurface with
number of unit cell states Ns=4 for t=0 and t=2 seconds,
respectively. Each color indicates a different state. This is
useful in appreciating the re-configurations that need to be
realized in order to facilitate the desired beam steering change.
In the subsequent simulation study, we investigate the effect
of delays on the closed-loop system performance. Delays can
be identified in both the forward and feedback paths: the
propagation delay when forwarding the power recorded at
the receiver to the GW, the latency of message delivery of the
packetized directives to the controller nodes of the CN, and the
time response of reconfiguring the state altering elements once
the directives have been received. The former and the latter
Fig. 8. Delay introduced by the embedded control network.
are established to be negligible compared to the dissemination
delays within the CN and the focus is thus on characterizing
these dissemination delays and examining their effect on the
convergence and stability of the control algorithm. This char-
acterization is conducted using a custom-developed simulation
tool based on the Analogic simulator which accounts for the
considered non-regular controller topology, the asynchronous
design, and the XY-agnostic routing algorithm. In order to
avoid directly integrating the AnyLogic Simulator with Matlab
to examine the effect of delays, we instead pre-calculate the
information dissemination delays for each possible change
in the reflected azimuth and elevation angle pair (using the
AnyLogic Simulator) as depicted in Fig. 8a and the resulting
relationship is embedded in the Matlab Simulator for the
evaluation of the closed loop system. It must be noted that the
GW disseminates the messages by employing unicast routing
to each controller node in one-by-one fashion. Multi-casting
solutions which can reduce the induced traffic and thus the
delays will be pursued in the future.
For the scenario under consideration, the delay relationship
of Fig. 8a leads to the time profile of Fig. 8b. In order to
account for this time-varying delay in the conducted simu-
lations, the time-dependent delay relationship is represented
as a polynomial function via a polynomial fitting procedure
conducted on Matlab. The time evolution of the elevation
and azimuth angles in the presence of time-varying delays
is depicted in Fig. 9indicating that convergence to the desired
reflection angle is still achieved, with the time evolution
becoming slightly more oscillatory.
Remark 1: It is to be noted that the delay values for the
continuous-time version of the proposed ESC scheme were up
to 15 ms, as presented in our previous paper [56]. However,
in this paper, we implement the discrete-time version of the
scheme which yields delay values less than 2.2 ms as shown in
Fig. 8. This reduction is natural due to the lower frequency of
updating the controller parameters which yields less number
of update directives.
It is well known in the controls literature [77], [83], that the
stability properties of feedback systems can be compromised
in the presence of delays. The next step is thus to investigate
the tolerance. We next investigate the tolerance of the system
with respect to feedback delays i.e. what are the delay values
beyond which the system performance degrades significantly?
The stability properties in the presence of delays are a problem
we plan to address analytically in the near future. In this work,
we investigate the problem using simulations. The transient
and steady-state behavior of the system is examined when the
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ASHRAF et al.: INTELLIGENT BEAM STEERING FOR WIRELESS COMMUNICATION 9
Fig. 9. Simultaneous control of the elevation and azimuth reflection angles
in the case of a stationary receiver with the time-varying feedback delays
of Fig. 8.
Fig. 10. Performance for different feedback delays.
feedback delays assume values equal to 20, 30, and 50ms.
Fig. 10 shows the resulting time response of the received
power and the azimuth angle. It can be observed that when
the feedback delay becomes equal to 50ms, the beam is no
longer steered towards the receiver, which causes the received
signal power to become almost 0. It must be noted that
the 50ms delay is significantly larger than the delay values
recorded in the discrete-time implementation of the control
algorithm (Fig. 8and 9), thus delay-related instabilities are not
expected, unless very high sampling rates are utilized which
approximate the continuous time case. A significant design
parameter of the discrete-time implementation of the algorithm
is the sampling period. Higher sampling periods are known
to have an adversarial effect on the stability margins of the
system, however at the benefit of more rare reconfigurations of
the HSF, less traffic within the controller network, and smaller
feedback delays. In the subsequent analysis, we investigate
these effects of the sampling period Ton the system per-
formance. We consider sampling period values equal to 0.01,
0.02, 0.03, 0.04 and 0.05 sec. The time evolution of φr, and the
corresponding signal power P(t)for each of these sampling
periods are shown in Fig. 11. Increasing sampling periods
cause an increase in the system damping resulting in slower
responses. When Treaches a sufficiently high value (0.05 sec),
the system even fails to converge to the desired azimuth and
Fig. 11. Effect of the sampling period on system performance.
Fig. 12. Spatial distribution of traffic generated by the control algorithm for
different values of the sampling period.
elevation angles, causing the received power to attain values
less than 1. It is to be noted that for Fig. 10 and 11, we have
not presented the time evolution of the elevation angle θrto
save the space.
Remark 2: It is worth mentioning that in our previous work
while keeping φconstant and only varying θ, the proposed
controller was able to converge the θto its desired value
for relatively high sampling time such that T<0.5s.
However, in this work, for simultaneous control of both
elevation and azimuth angle, a relatively small sampling time
is required. This depicts a trade-off between sampling time
and the achieved performance.
However, as highlighted above, higher sampling frequencies
(lower sampling periods) lead to re-configurations of the con-
troller states occurring more often, in turn resulting in higher
traffic loads within the CN. In order to characterize this effect,
we use the custom-developed Analogic simulator to analyze
the traffic patterns generated by the control policy within
the CN. First, we consider the effect of the sampling period
on the spatial distribution of traffic workload. Throughout
the simulation experiment we record the number of times
each controller has been reconfigured and this information
is visualized in the form of a heatmap, the cells of which
correspond to the controllers of the HSF. The “hotter” the
color of the cell, the higher is the number of times it has been
reconfigured. Fig. 12 depicts the spatial distribution recorded
for the basic scenario when both θand φare successfully
guided to the desired values. The first thing to note is that for
small sampling periods, only a small percentage of the cells
are re-configured frequently, with most cells being rarely re-
configured. This can be attributed to the fact that the variations
in the reflecting angle are minor and therefore the required
phase profile modifications are spatially limited thus affecting
a small number of cells. However, as the sampling period
increases, the load spreads spatially as more controllers need to
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10 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Fig. 13. Sending rate of injected requests for different sampling periods.
be updated, however the frequency of the heavily loaded ones
drops. The net load on the HSF is assessed by investigating the
sending rate of packetized directives injected by the gateway,
which is a characteristic of temporal behavior. This is depicted
in Fig. 13, where it can be observed that as the sampling period
becomes larger, the periodic traffic patterns exhibit a decrease
in both their peak amplitude and frequency until the sampling
period reaches 0.05sec, in which case the peak amplitude rises
again with a further decrease in the frequency of the peaks.
B. Mobile Receiver
Until now we have considered a target receiver that is sta-
tionary with respect to the HSF. However, in many applications
including V2V, Vehicle to Infrastructure (V2I), and UAVs,
the target receiver is usually mobile in nature. This potential
mobility of the receiver poses additional challenges to the
beam steering scheme and raises concerns with respect to
the effectiveness of the proposed scheme under such mobility
scenarios. In the rest of the paper, we investigate this effect
and its limitations with reference to two types of mobility
models for the receiver. The first one involves a single mobile
receiver that moves in a straight line parallel to the surface.
For simplicity, the target receiver is considered at the same
height as the HSF, such that φi=φr=0. When the
receiver moves parallel to the HSF, its angle of elevation θ
changes persistently with respect to the HSF, and thus the
proposed algorithm needs to reconfigure the parameters of
the HSF continuously in order to ensure maximum received
power. In the first set of experiments, we consider a constant
speed of the receiver set to 5.04 km/h, which corresponds
to a scenario of a person walking. Fig. 14 depicts graphical
representations of the behavior of the beam steering scheme
and the corresponding received signal power. As highlighted
earlier, the target only moves in a horizontal direction parallel
to the surface, thus its azimuth is fixed and only the elevation
angle changes. Fig. 14a depicts this in the form of the target
elevation angle which is initiated at 42 degrees and linearly
decreases with time to gradually reach 36 degrees. The initial
deviation of the actual elevation angle from the one that
will steer the beam towards the receiver is reflected on the
received signal power attaining half its maximum value as
shown by Fig. 14b. However, the proposed beam steering
algorithm is successful in steering the beam towards the target
location and within a very short time (0.25 seconds) the beam
points sufficiently close to the receiver such that the received
signal power achieves values close to its maximum. As the
target continues to move the algorithm achieves fairly good
tracking maintaining high power values. Fig. 14c presents the
Fig. 14. Mobile Receiver moving in parallel to the HSF with a constant
speed of 5.04 km /h.
Fig. 15. Mobile receiver with a speed of 20 km /h.
spatial distribution of the traffic within the HSF controller
network while the beam tracks the walking target. The figure
shows a bias towards the upper right and lower left parts
of the metasurface which bear a significant part of the load.
In addition, Fig. 14d depicts the delay incurred by the CN
corresponding to each time instant during tracking of the
moving target. It can be observed that delays are within a
reasonable range and thus do not affect the performance of
the algorithm.
In the second and third set of experiments, we continue
to consider a linear motion of the receiver, however, the
speed is increased to 20 and 72 km/h respectively. For the
case of 72 km/h we also account for the effects of shadow
fading which is modeled as a log-normal distribution with
the standard deviation chosen to be 8.14dBs according to the
design guidelines of [84]. The results are presented in Fig. 15
and Fig. 16 with the latter showing both the no fading and
fading cases. The first thing to note is that the effects of
shadow fading are not significant on the output responses. This
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ASHRAF et al.: INTELLIGENT BEAM STEERING FOR WIRELESS COMMUNICATION 11
Fig. 16. Mobile receiver with a speed of 72 km /h.
can be attributed to the Low Pass Filter which the ESC scheme
incorporates, which filters out the high-frequency components
induced by fading. Further, it may be noted that the proposed
algorithm is capable of steering the beam towards the target
despite its increased speed. This highlights the suitability of
the proposed method to be used for applications like V2V and
V2I. Moreover, it can be observed from Fig. 15c that with
the increase in speed of the target, more aggressive control
action is required and hence more traffic is generated. Due
to this fact, Fig. 15c shows more active cells as compared
to Fig. 14c. Despite the good tracking, we observe that an
increase in the speed results in higher fluctuations of the
received signal power around the maximum value as shown
by Fig. 15b. By increasing the speed further, as shown in
Fig. 16, the algorithm is still able to track the beam near
the target location, however, there are more variations in
the corresponding received signals power and performance
of the algorithm downgrades. The results demonstrate that,
as expected, the increase in the speed has an adversarial effect
on the tracking performance and this raises the question of
which speed is the system performance at acceptable levels.
We conduct additional simulations to investigate this and we
observe that for a speed of 144 km/h, the algorithm yields poor
performance in terms of convergence time and corresponding
received signal power. Similarly, it has been observed that for
speeds greater than 160 km/h the algorithm fails to converge
within a reasonable time.
Remark 3: This performance bound in terms of the allow-
able speed of the target for the effective operation of the
proposed algorithm has been tested via simulations. However,
in the future, we intend to evaluate the performance via mathe-
matical analysis and improve the performance by incorporating
predictions of the receiver trajectory.
Remark 4: It is worth mentioning that the scenarios leading
to Fig. 14,15 and 16 involve horizontal movement of the
receiver parallel to the HSF at different speeds. At relatively
low speeds (Fig. 14 and 15) the simulation experiment ends
before the receiver exceeds the center of the HSF, thus tracking
a strictly decreasing elevation angle profile. At high speeds
(Fig. 16), the receiver has sufficient time to exceed the center
thus tracking a convex (decreasing, reaching a minimum, and
then decreasing) elevation angle profile.
C. Mobility of the Receiver in 3D
The considered linear movement model is adequate for
evaluation purposes and sufficient to describe a number of
real-life cases i.e. a vehicle moving on a straight line road.
However, more sophisticated models which could describe a
Fig. 17. Mobile target with simultaneous change of elevation and azimuth
angles in 3D space.
richer set of scenarios, for example, UAV movement, involve
mobility in the 3D space which causes both angles of azimuth
and elevation to change. In this case, we need to control both
θrand φrsimultaneously in such a way that the beam tracks
the moving target in 3D space and the corresponding received
signal power is maximized. In the next set of experiments,
we consider target movement which causes both θrand φr
to change in spherical coordinates. The change in θand φis
piece-wise linear, one proportional to the other. We evaluate
the performance of our algorithm in controlling both angles
simultaneously when both targeted values are time-varying.
Fig. 17 shows the corresponding results, where it can be
seen that initially the target was at (θr=52, φr=42)
and the beam was pointing towards (θr=49, φr=39).
Within 2 seconds, the algorithm successfully directs the beam
toward the target location and maximizes the received signal
power. After this time, the target keeps changing its location
in three-dimensional space and the beam successfully tracks
the moving target.
Remark 5: It is to be noted that for the case of both
the azimuth and elevation angles changing, the convergence
speed of the power to its maximum value is relatively slow as
compared to the case of only one angle changing. Moreover,
since both angles are adjusted based on a single measurement,
namely the received signal strength, the proposed algorithm
may fail in achieving the goals in the case when the azimuth
and elevation angles are changing in different directions.
In such a case, the control objectives become contradictory.
To tackle this, a more sophisticated control algorithm involving
observers to estimate the states using the received power
measurement is required and will be the topic of future work.
V. CONCLUSION
In this paper, we have considered the application of
extremum seeking control for the autonomous reconfiguration
of Hypersurface controller states to guide an impinging wave
towards the receiver so that the received power is maximized.
Extended simulations have demonstrated the effectiveness of
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12 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
the method even when the receiver is mobile. Speeds beyond
100 km/h can be tolerated rendering it suitable even for
vehicular or UAV settings. The workload within the CN is also
characterized and it is demonstrated that delays up to 2.2 msec
are incurred due to the traffic which is not sufficient to render
the system unstable. Finally, an increase in the sampling
period reduces the load at the expense of a degradation in
performance. Future work will involve multivariable cascaded
control over multiple tiles and event-based extremum-seeking
controllers which minimize the frequency of reconfigurations
also accounting for the delays through appropriate predictors.
A practical demonstration of the method on a real test bed is
also aimed in the future.
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14 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Nouman Ashraf received the Ph.D. degree in
electrical engineering from Frederick University,
Cyprus, under the Erasmus Mundus Scholarship
Program. He has worked with the Turku University
of Applied Sciences, Finland; the TSSG, Waterford
Institute of Technology, Ireland; and the University
of Cyprus. He is currently working with Technologi-
cal University Dublin, Ireland. His research interests
include the application of control theory for manage-
ment of emerging networks with applications in the
Internet of Things, 5G and beyond communication
networks, electric vehicles, and smart grid.
Taqwa Saeed received the Ph.D. degree from
Frederick University Cyprus in 2020. She is cur-
rently a Post-Doctoral Researcher with Halmstad
University, Sweden. Her work involves the devel-
opment of information dissemination techniques for
emerging networks, including VANETS, molecular
networks, and metasurface networks.
Hamidreza Taghvaee received the M.Sc. degree in
telecommunication engineering from K. N. Toosi
University of Technology, Iran, in 2016, and the
Ph.D. degree in computer architecture from the Poly-
technique University of Catalunya, Spain, in 2021.
He was a Researcher at the VISORSURF and the
WiPLASH FET-OPEN Project, N3Cat. He was a
Visiting Researcher at Aalto University in 2020.
He is currently a Research Fellow with the RISE-
6G FET-OPEN and OBLICUE EPSRC Project, Uni-
versity of Nottingham. His main research interests
include electromagnetic, metasurfaces, and wireless communications.
Sergi Abadal (Member, IEEE) received the B.Sc.
and M.Sc. degrees (Hons.) in telecommunication
engineering and the Ph.D. degree (Hons.) in com-
puter architecture from the Universitat Politècnica
de Catalunya (UPC), Barcelona, Spain, in 2010,
2011, and 2016, respectively. From 2009 to 2010,
he was a Visiting Researcher at the Broadband Wire-
less Networking Laboratory, Georgia Institute of
Technology, Atlanta, GA, USA. He has also been
a Visiting Researcher with the School of Com-
puter Science, University of Illinois at Urbana–
Champaign, in 2015 and 2018. He is currently working as a Distinguished
Researcher with the N3Cat Group, Computer Architecture Department, Uni-
versitat Politècnica de Catalunya (UPC). His current research interests are in
the areas of chip-scale wireless communications, including channel modeling
and protocol design and the application of these techniques for the creation of
novel architectures for next-generation computing systems in the classical and
quantum domains. He is a member of ACM and HiPEAC. During the Ph.D.
degree, he was awarded by INTEL within its Doctoral Student Honor Program.
He was a recipient of a Starting Grant called WINC from the European
Research Council (ERC) and the Project Coordinator of WIPLASH H2020
FETOPEN Project, while in the past, he participated in several other national
and EU projects. He is currently an Area Editor of the Nano Communication
Networks (Elsevier) journal, where has selected as an Editor of the Year
2019. Since 2020, he acts as one of the ambassadors the European Innovation
Council (EIC) through its program of National Champions.
Vasos Vassiliou (Senior Member, IEEE) is cur-
rently an Associate Professor with the Department
of Computer Science, University of Cyprus, and the
Co-Director of the Networks Research Laboratory
(NetRL), UCY (founded by Prof. A. Pitsillides).
Since November 2017, he has been the Group
Leader with the Smart Networked Systems Research
Group, RISE Center of Excellence on Interactive
Media, Smart Systems and Emerging Technologies,
Nicosia, Cyprus. He has also been appointed as the
Senate of the University of Cyprus; and the Board
of Director of CYNET, the National Research and Educational Network,
where he has serving as the Chair, since November 2016. He has more than
75 publications in academic journals, books, and international conferences,
with more than 1300 citations. His research interests include protocol design
and performance aspects of networks (fixed, mobile, and wireless), in partic-
ular mobility management, QoS adaptation and control, resource allocation
techniques, wireless sensor networks, and the Internet of Things.
Christos Liaskos received the Diploma degree in
electrical engineering from the Aristotle Univer-
sity of Thessaloniki (AUTH), Thessaloniki, Greece,
in 2004, the M.Sc. degree in medical informatics
from the Medical School, AUTH, in 2008, and
the Ph.D. degree in computer networking from the
Department of Informatics, AUTH, in 2014. He is
currently an Assistant Professor with the University
of Ioannina, Ioannina, Greece, and an Affiliated
Researcher with the Foundation for Research and
Technology Hellas (FORTH), Heraklion, Greece.
His research interests include computer networks, traffic management, and
wireless channel engineering.
Andreas Pitsillides (Senior Member, IEEE) was
appointed as a Visiting Professor at the School
of Electrical and Information engineering, Univer-
sity of the Witwatersrand (Wits), Johannesburg,
South Africa (2017–2020), and the Department of
Electrical and Electronic Engineering Science, Uni-
versity of Johannesburg, Johannesburg (2014–2017).
He is currently a Professor with the Department
of Computer Science, University of Cyprus, the
Co-Director of the Networks Research Laboratory
(NetRL, http://www.NetRL.cs.ucy.ac.cy), appointed
as a Visiting Professor with the Department of Electrical and Electronic
Engineering Science, University of Johannesburg, Johannesburg (2021–2024).
He has published over 350 refereed articles in flagship journals (e.g. IEEE,
Elsevier, IFAC, Springer), international conferences, book chapters, and
coauthored two books (one edited). His broad research interests include
communication networks, software defined metasurfaces/reconfigurable intel-
ligent surfaces, and their application in programmable wireless environments,
nanonetworks, smart systems, and e-health, and networking security aspects.
He has a particular interest in adapting tools from various fields of applied
mathematics such as adaptive non-linear control theory, computational intel-
ligence, game theory, and complex systems and nature inspired techniques,
to solve problems in communication networks.
Marios Lestas (Member, IEEE) received the B.A.
and M.Eng. degrees in electrical and information
engineering from the University of Cambridge, U.K.,
and the Ph.D. degree in electrical engineering from
the University of Southern California in 2000 and
2006, respectively. He is currently an Associate
Professor with Frederick University, Cyprus. His
research interests include application of control the-
oretic tools and optimization methods toward the
development of practical solutions in a number of
Intelligent Networks and Cyber-Physical Systems,
for example, computer networks, the Internet of Things, transportation net-
works, power networks, bio-nano-networks, and nano-metasurface controller
networks. In the aforementioned networks, he has investigated issues perti-
nent to congestion control, information dissemination, network vulnerability,
demand response, and more recently privacy and security. He has participated
in a number of projects funded by the Research Promotion Foundation and
the EU.
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
