the incident beam towards Bob to improve the channel gain while making no
signal available for Eve to eavesdrop.

Another potential research area for the scenarios in Figs. 25 (B) and (C) is
investigating appropriate algorithms (either centralized or distributed) to
reliably obtain the channel state information (CSI) for the links between the
RIS and the users since this information is needed by the RIS controller to
properly configure its elements. This is crucial in the design of RIS-aided
VLC systems since (i) RIS are low-powered structures that cannot initiate
transmission to facilitate accurate channel estimation, and (ii) a large
number of RIS elements and, as a consequence, a large number of extra channel
coefficients might be required to obtain a reasonable system performance [37].

###  VII-C MIMO and RISs

Typical indoor environments are characterized by the deployment of multiple
light fixtures to ensure uniform illumination. These multiple light fixtures,
when used for the dual purpose of communication and illumination, can be
regarded as an MIMO OWC system [66]. Several studies have considered MIMO OWC
systems, provided channel capacity bounds and proven the significant channel
capacity improvements that MIMO brings in OWC systems [200, 201, 202, 203,
204, 205, 206]. As in any OWC system, transmission in MIMO OWC systems may be
hindered by an obstruction in the link between the transmitter and the
receiver. Hence, the RIS technology could be used to connect users to the
access point. Unlike RIS-based MIMO RF systems [28, 29, 30, 207, 208, 209,
210, 211], and RIS-based cell-free MIMO RF systems [212, 213, 214, 215] which
have been thoroughly studied, there has been minimal research on the
application of RIS technology in MIMO OWC system.

Figure 26: Model of an RIS-assisted MIMO OWC system [32].

Figure 26 depicts a generalized model of an RIS-aided MIMO OWC system [32]. In
this figure, the data to be transmitted is distributed between the $L$ light
sources constituting the transmitter. The transmitted light beams reflect off
of the $QN$ RIS elements and are directed towards the $P$ PDs of the receiver.
Several configurations of RIS-based MIMO OWC systems can be derived from Fig.
26. A few examples of such configurations are shown in Fig. 27. They are all
governed by the general transmission equation

$\textbf{{y}}(t)=\mathbb{H}\textbf{{x}}(t)+\textbf{{z}}(t),$ (7)

where $\textbf{{x}}(t)=[x_{1}(t),\dots,x_{L}(t)]^{T}$
($x_{i}(t)\in\mathbb{R}_{+}$, $i\in\\{1,2,\dots,M\\}$) is the $L$-dimensional
real-valued S-RIS sub-channel input vectors (light intensity of the $i^{th}$
transmitter at time instant $t$).
$\textbf{{y}}(t)=[y_{1}(t),\dots,y_{M}(t)]^{T}$ ($y_{k}(t)\in\mathbb{R}_{+}$)
denotes the $QN$-dimensional real-valued RIS-D sub-channel output vectors, and
$\textbf{{z}}(t)=[z_{1}(t),\dots,z_{QN}(t)]^{T}$ ($z_{k}(t)\in\mathbb{R}_{+}$)
is the $P$-dimensional real-valued Gaussian noise vectors at D. The noise at
the receiver, which is additive, independent, and identically distributed, is
a combination of thermal and shot noises, which are well modeled as Gaussian.
$\textbf{{y}}^{in}_{ris}$ and $\textbf{{y}}^{out}_{ris}$ are respectively
given by
$\textbf{{y}}^{in}_{ris}(t)=\textbf{H}\textbf{{x}}(t)+\textbf{{z}}^{in}_{ris}(t)$
and
$\textbf{{y}}^{out}_{ris}(t)=\Phi\textbf{H}\textbf{{x}}(t)+\textbf{{z}}^{out}_{ris}(t)$,
where $\textbf{{y}}^{in}_{ris}$ and $\textbf{{y}}^{out}_{ris}$ are
$QN$-dimensional real-valued signal measured at the input and output of the
RIS module, respectively, and $\Phi$ denotes the phase shift matrix.
$\textbf{{z}}^{in}_{ris}$ and $\textbf{{z}}^{out}_{ris}$ represent the
$QN$-dimensional real-valued noise vectors before and after the RIS module,
and the transmission from RIS to D is governed by
$\textbf{{y}}(t)=\textbf{G}\textbf{{y}}^{out}_{ris}(t)+\textbf{{z}}(t)$. In
the matrix form, (7), defined in the $\mathbb{R}_{+}^{M\times M}$ space, can
be given by

$\mathbb{H}=\textbf{G}^{\text{T}}\Phi\textbf{H}.$ (8)

The entries of the matrices G and H strongly depend on the physical
arrangements of the light sources (H) and photo detectors (G) [32].

Figure 27: Scenarios of RIS-assisted MIMO OWC systems [32].

$x(t)$ in (7) must always satisfy the non-negative and peak-intensity
constraint, resulting in the generated light upper bounded by a total average
value [200, 203].

$0\leq
x_{i}(t)\leq\mathbb{X}\>\>\text{and}\>\>\sum_{i=1}^{M}\textbf{P}_{i}\leq
p_{o},$ (9)

where $\mathbb{X}$ represents the peak-input-intensity and $\textbf{P}_{i}$ =
E${}_{x_{i}}[x_{i}]$, with E${}_{x}[\cdot]$ denoting the expectation with
respect to the distribution of $x$. The average optical power constraint can
be written as $\|\textbf{P}\|_{1}$ =
$\|\textbf{P}_{1},\dots,\textbf{P}_{M}\|_{1}\leq p_{o}$, where $\|\cdot\|_{1}$
is the $\ell_{1}$-norm operator, and $p_{o}$ is the transmitter’s total
optical power.

With these two constraints, the channel capacity,
$c(\mathbb{H},p_{o},\mathbb{X})$, of RIS-based MIMO OWC systems can be given
by [200]

$c(\mathbb{H},p_{o},\mathbb{X})=\underset{f(\textbf{{x}})}{\mathrm{max}}I(\textbf{{x}}:\mathbb{H}\textbf{{x}}+\textbf{{z}}).$
(10)

Figure 27 illustrates four main scenarios for indoor RIS-assisted VLC systems.
Figure 27-(A) is a typical RIS-assisted SISO OWC system in which the
transmitter, RIS, and receiver all consist of a single element. A system of
this type is subject to a number of issues, including large geometric losses
at the destination [32]. These losses are mitigated by using a multiple
elements RIS (i.e., MIMO RIS), where the RIS elements converge their beam
towards a single element receiver, as shown in Fig. 27-(B). Figures 27-(C) and
-(D) shows the use of an RIS to mitigate geometric losses in an OWC system
where the transmitter, the RIS, and the receiver have multiple active
components. The capacity bounds of all these channels can be evaluated based
on $p_{o}/\mathbb{X}\geq L/2$ and $p_{o}/\mathbb{X}<L/2$, where $L/2$
represents a break in the channel capacity.

Figure 28: Capacity bounds for an RIS-assisted MIMO OWC system. Example of an
RIS-based MIMO FSO system [32].

As seen from Fig. 27, multiple RIS elements are required in MIMO OWC systems.
Therefore, it is interesting to examine systems in which the RIS consists of
multiple components. This type of system was proposed in [32], where the
authors utilized the $\it{QR}$-decomposition algorithm to determine the
channel capacity of an RIS-based OWC system. This capacity relates to the
disposition and orientation of the light sources, PDs, and RIS elements. In
[32], the authors considered two scenarios: One in which there is overlap
between light beams at the receiver and a second one where there is no
overlap. To maximize the channel capacity in the former, each channel must be
optimized individually. The total maximized channel capacity is equal to the
sum of all individual maximized channels. This is modeled in [32] as

$c(\mathbb{H}^{N}_{m},p_{o},\mathbb{X})=\sum_{i=1}^{M}\mathrm{max}\left[g(\mathbb{H}_{m},p_{o},\mathbb{X})\right],$
(11)

where the function $g(\mathbb{H}_{m},p_{o},\mathbb{X})$ is the SISO channel
capacity, which can be expressed using the considered transmission power and
peak intensity constraints. When the light beams overlap at the receiver, the
channel capacity may be determined by using the QR-decomposition algorithm.
The $\it{QR}$-decomposition, in contrast to the single value decomposition
extensively used in the analysis of MIMO RF systems, does not generate
negative values and is thus very suitable for the VLC channel. Such analysis
is performed in [32] for OWC channels, with a focus on the situation where
$p_{o}/\mathbb{X}\geq L/2$. The channel capacity of the overall channel can be
expressed as

$c(\mathbb{H}_{m},p_{o},\mathbb{X})=\underset{\mathbb{P}}{\mathrm{max}}\sum_{i=1}^{M}\frac{1}{2}\log\left(1+\frac{2(\textbf{{h}}_{m(i,i)})^{2}\mathbb{X}^{2}}{\pi
2e\sigma^{2}}\right),$ (12)

where $\textbf{{h}}_{m(i,i)}$ are diagonal entries of the overall channel
matrix. Results of this analysis is proposed in Fig. 28. The figure depicts
upper bound (UB) and lower bound (LB) of the system with no overlaping
($Scen_{1}$) and overlaping ($Scen_{2}$), considering the constraint
$p_{o}/\mathbb{X}$ with regard to the common number of light sources and PDs,
and using the $\it{QR}$-decomposition. Open research challenges include
determining the optimal position of the RISs as well as capacity optimization.

###  VII-D NOMA and RISs

Non-orthogonal multiple access (NOMA) is among the key enabling techniques for
massive connectivity in future wireless networks. In comparison to
conventional orthogonal multiple access techniques such as OFDMA and TDMA,
that serve a single user in each orthogonal resource block, NOMA is able to
serve multiple users on the same resource block (which can be in time,
frequency, or code domain) and, as result achieve high spectral efficiency
gains [3]. There have been many studies on the design and performance analysis
of the two categories of NOMA (i.e., code and power domain NOMA) in RF
communication networks (e.g., see [3, 4, 216, 2] and references therein). The
success of NOMA in RF communication networks motivates its adoption for VLC
systems. Specifically, NOMA is suited for VLC systems due to the following
reasons: (i) the modulation bandwidth of a typical VLC transmitter (e.g.,
LEDs) is very limited and that affects the achievable data rates; (ii) NOMA is
efficient in multiplexing a small number of users and in most indoor VLC
systems each AP typically have few number of users in its coverage area; (iii)
accurate CSI, which is crucial in NOMA-based systems (e.g., for superposition
coding), can be obtained in VLC systems since the channel remains constant
most of the time (due to the low mobility of users in VLC systems); (iv) NOMA
performs better in high SNR scenarios [217], and VLC systems are able to
provide very high SNR (when a LoS path exits) due to the short separation
distance between the transmitter and receivers; and (v) VLC systems offer a
number of ways (e.g., optimizing the angles of irradiance and incidence
through the use of angle diversity transmitters and receivers, tuning the FoV
of the transmitter and the receiver, etc.) to enhance the channel gain
differences among users, which is important in NOMA, although not a necessary
requirement.444The performance of power-domain NOMA is highly dependent on
having significant channel gain difference among users. NOMA has been
considered for VLC systems in few works, with [54] being the only survey paper
on power domain NOMA-based VLC systems. In VLC, the use of power domain NOMA
has received more attention as compared to code domain domain for the
following reasons [218]. Firstly, in a similar way that VLC systems typically
serve few users, power domain NOMA is used to multiplex a small number of
users. Secondly, power allocation in power domain NOMA largely depends on the
availability of accurate CSI at the transmitter. In VLC, channel estimation is
significantly less error-prone when compared to RF communication networks
since the VLC channel is time-invariant and only changes when the user moves
to another location. Finally, code domain NOMA are associated with high
complexity multiuser detection algorithms [219].

Considering the downlink of a single cell indoor VLC system with
${\mathcal{K}}$ users, who are assumed to be static or quasi-static such that
their CSI remains not outdated, the received signal at the $k$-th user
according to the NOMA principle can be given by

$y_{k}=a_{k}{\sqrt{P}}h_{k}s_{k}+\sum_{j=1}^{{\mathcal{K}}-1}a_{j}{\sqrt{P}}h_{k}s_{j}+\sum_{i=k+1}^{\mathcal{K}}a_{i}{\sqrt{P}}h_{k}s_{i}+n_{k},$
(13)

where $a_{k}$ represents the power allocation coefficient $(0<~{}a_{k}<~{}1)$,
$P$ refers to the total electrical power, $h_{k}$ is the channel power gain
which can be calculated as in [20], $s_{k}$ denotes the desired information
signal, and $n_{k}$ refers to the additive white Gaussian noise. In (13), the
first, second, and third terms on the right side of the equation represent the
desired signal, the interference components cancelled by SIC, and the residual
interference following SIC, respectively. Without loss of generality, an
assumption is made that the $\mathcal{K}$ users have been sorted in ascending
order according to their channel qualities

$h_{1}\leq\cdots\leq h_{k}\leq\cdots\leq h_{\mathcal{K}}.$ (14)

According to the NOMA principle, users with better channel conditions are
allocated less power, meaning that $a_{1}\geq\cdots\geq\cdots\geq
a_{\mathcal{K}}$ and since the available transmit power is limited, the power
allocation coefficients must satisfy the constraint

$\sum_{k=1}^{\mathcal{K}}a_{k}^{2}=1.$ (15)

It can be observed from (13) that as the number of user users increases, the
complexity of decoding the signal also increases since a total of
${\mathcal{K}}-1$ users (i.e., with the exception of the 1-st user) must
perform SIC. Moreover, any residual interference resulting from inaccurate
estimation of CSI increases with the number of users. In the sequel, most
recent research on power doamin NOMA-VLC works are first summarized, and then,
the possible ways RIS can be utilized to enhance the performance of such
communication systems are provided.

Figure 29: Application of RISs in NOMA-based VLC systems: (A) NOMA principle
in NOMA-VLC systems; and (B) Potential achievements of RIS-aided NOMA-VLC
systems.

The authors in [220, 221, 5, 222, 223, 224] demonstrated the superior sum rate
and BER gains of NOMA-based VLC systems over OFDMA-based VLC systems. In
[225], the authors proposed a general framework for energy efficiency analysis
of a NOMA-based hybrid VLC and RF wireless system. Focusing on maximizing the
sum-rate and the minimum user rate under transmit power constraints and QoS
requirements, the authors in [226] proposed a semi-closed form optimal power
allocation scheme for a NOMA-based VLC system. The simulation results
demonstrated that the proposed NOMA schemes outperformed OMA schemes. In
[217], a novel gain ratio power allocation scheme was proposed, and the
simulation results showed significant bit error rate (BER) and sum rate
performance gain when compared to the static power allocation scheme. In [227,
228], the authors considered a sum rate maximization problem under peak power
and non-negativity constraints, and proposed a power allocation scheme based
on the Lagrange dual method. In [229], the authors proposed gradient
projection based algorithms for the sum rate and minimum user rate
optimization problems while considering QoS requirements. In [230], the
authors proposed bounds on the achievable rates of NOMA-based VLC networks. In
that same paper, the authors proposed power allocation schemes for the static
and mobile users scenarios. The joint optimization of user pairing, link
selection, and power allocation to maximize the sum-rate of a hybrid VLC and
RF communication system was investigated in [170, 231]. NOMA-based modulation
schemes for single cell and multi-cell VLC systems where users located at the
edge of the cells are served by multiple APs were examined in [232] and [233],
respectively. A user pairing scheme for a 3D VLC-NOMA systems was proposed in
[234]. In [235], a cuckoo search algorithm was proposed to optimize the
transmit power, LED’s semi-angle at half power, the FoV of the lens, and the
gain of the optical filter such that the sum rate performance is maximized. In
[236], the optimization of power allocation in a NOMA-based VLC system using
particle swarm optimization was investigated. Under MIMO settings, a
normalized gain difference power allocation method was proposed in [237] for a
NOMA-based VLC system, and was shown to outperform the gain ratio power
allocation method. An experimental demonstration of a MIMO NOMA-based VLC with
single carrier mode of transmission and frequency domain successive
interference cancellation was performed in [221].

Figure 30: Principles of simultaneous optical wireless data transmission and
energy transfer over RIS-assisted VLC channels.

Many studies have exploited RISs to improve the performance gains in NOMA-
based RF systems (e.g., see [238, 239, 240, 241, 48, 242] and references
therein). However, this survey focuses on the use of RISs in NOMA-based VLC
systems. RISs can be combined with NOMA in VLC systems for attractive
performance gains. As previously mentioned, the NOMA principle requires the
ordering of users’ channel gains in ascending or descending order to enable
them to employ SIC to recover the transmitted message. Thus, the performance
of NOMA-based communication systems is highly dependent on the users’ channel
conditions. Moreover, it has been shown in [243] that NOMA based communication
systems in general can attain superior performance gains than OMA-based
systems only if the channel gains of different users are significantly
distinct. Hence, the main functionality of an RIS in NOMA-based VLC systems is
to modify the channel conditions of the users to yield additional performance
improvements. Figure 29 illustrates a potential application in a two-user RIS-
aided NOMA-based VLC system. From this figure, it is demonstrated how RISs can
further improve NOMA-VLC systems. Firstly, an RIS can be utilized in NOMA
enabled VLC systems to control the channel gain ordering of users as well as
to decide which users achieve higher data rate based on their QoS
requirements. In VLC systems, the channel power gain is dependent on the angle
of incidence, angle of irradiance, the LED’s semi-angle at half power, and the
distance between the AP and the user. All but the distance parameter are
typically fixed. Hence, the closest users to the VLC AP will always have
higher channel power gain and, as a result, the channel ordering of the users
will always follow the location of users as shown in Fig. 29 (A). However, the
use of an RIS allows the VLC network to control the channel gains of the users
and as result modify the ordering of the users’ channel conditions as well as
the potential achievable data rates. Specifically, the RIS elements can be
optimized to enhance the channel conditions of users farther away from the AP
and, as result, change the original order of the users’ channel gain as
illustrated in Fig. 29 (B). Since the users with higher channel gain typically
get higher data rate, the ability to modify the original order of the users’
channel gain also allows the VLC network to sort its users based on their
particular data rate requirements rather than on the uncontrollable random
environment. For instance, users that are far away from the AP and have high
data rate requirements in a conventional NOMA-VLC system might be in outage
since irrespective of the allocated power, the weak users’ rate will always be
limited by interference from the strong user and the unfavorable channel
conditions. However, the appropriate use of an RIS can allow the originally
weak users with high data rate requirements to become strong users such that
their required rates can be met. Secondly, the deployment of RISs in a NOMA-
based VLC system can be used to enable massive connectivity by extending the
coverage area of the VLC AP as depicted in Fig. 22. In conventional NOMA-based
VLC systems and VLC systems in general, it can be difficult to guarantee
uniform coverage in the indoor environment. This is because the VLC channel is
easily affected by blockages, random device orientation as well as the limited
FoV of the receivers. All these factors result in severe performance
degradation. However, RISs can be deployed and optimized to provide coverage
and better channel conditions for users outside the illumination area of the
VLC AP. Finally, RISs can be used to create more distinct channel conditions
for users in a NOMA-based VLC systems. As mentioned in [244, 5], the
performance of NOMA communication systems is very dependent on which users are
paired together and could be enhanced by grouping users with more distinctive
channel conditions. However, the channel conditions of VLC links are highly
dependent on the location of the users. When the users are too close, that can
negatively affect the VLC system performance when NOMA is applied. An RIS can
be used in such a scenario to realize more distinct channel conditions for
users, even when they are closely located.

###  VII-E Simultaneous Lightwave and Power Transfer and RISs

In VLC technologies, the receiver may be self-powered. Thus, the integration
of RISs into the VLC channels should enable energy forwarding (i.e.,
simultaneous lightwave and power transfer (SLPT)). The energy contained in the
incident wave should also be reflected with the same orientation as the light.
Although there has been some work done on simultaneous wireless data and
energy transfer using RIS-assisted RF systems [33, 245, 246, 247], this is not
the case for OWC in general and VLC in particular. As a result, it is evident
that there is still much to be done in the field of RIS-assisted SLPT optical
systems.

An RIS-supported SLPT OWC system can facilitate simultaneous wireless energy
data transfer. This may be achieved by using a PD in conjunction with or
without a TIA. The transmitter of such a system illuminates the environment as
it broadcasts information, and the receiver harvests the energy from the light
as it decodes the information. For example, an RIS-assisted SLPT VLC system
may include three different technologies: lighting, data transmission, and
energy harvesting. An example of such a system is shown in Fig. 30. The
diagram in this figure illustrates the integration of an energy harvesting
module. The transmitter is the same as the usual VLC transmitter, which
includes a signal processing unit, an LED, and an LED driver. To detect light,
three different types of devices can be used, namely PDs, LEDs, and
photocells. In contrast to PDs and LEDs, photocells utilize photovoltaic
processes to accomplish this. These devices are all capable of simultaneously
reconstructing the message waveform and proceeding to data signal forwarding,
along with offering energy harvesting capabilities. It is reported in [248]
that an indoor light can produce approximately 180 $\mu$W of power harvested
using an amorphous crystalline in a system without RIS. LEDs provide photon-
to-current conversion, allowing up to 5V to be reached with a great current,
which might be enough to charge a smart device’s battery.

###  VII-F Summary and Lessons Learned

This section focused on the integration of RISs and other emerging
technologies into VLC. Although such technologies have already been studied
for VLC systems, we strongly believe their potentials have not been fully
harnessed. In this section, we presented the various ways RISs can
revolutionize the application of such new technologies in VLC systems to yield
significant performance improvements. The key lessons are summarized as
follows. Firstly, RISs can be utilized to design re-configurable ADTs that
allow the creation of varying number of smaller cells depending on factors
such as the number and the distribution of users. Secondly, RISs can be
exploited to enhance the channel gain difference of the legitimate user and
the eavesdropper to improve security performance. Thirdly, the various
scenarios in which RISs can be deployed in MIMO networks and the corresponding
channel structures, capacity, and achievable rates were discussed. Moreover,
adopting RISs in NOMA-enabled VLC systems can help realize more distinct
channel conditions and allow the control of the channel gain ordering of
users. RISs also enable coverage extension in NOMA-based VLC systems. Finally,
the integration of RISs with simultaneous lightwave and power transfer was
introduced.

##  VIII Future Research Directions

Section VII presented promising research directions on the integration of RISs
and other technologies in VLC. This section further highlights other potential
research problems in RIS-enabled VLC systems that need to be investigated in
future work.

####  VIII-1 Learning approach for blockage prediction in RIS-based VLC
systems

Current studies on RIS-aided VLC systems assume the availability of accurate
CSI of the channel paths (i.e., between the APs, the RISs, and the users) for
all users. The RISs’ controller uses this information to optimize the
configuration of the RISs’ elements. However, acquisition of users’ position
information or the full real time CSI can be computationally expensive, and
any errors in these estimates can severely impact the performance of the RIS-
aided VLC system. This is due to the fact that the RIS can consist of a large
number of elements and, as a result, the length of pilots required can be
significant. The idea of communication systems having the ability to predict
the VLC environment (e.g., LoS link blockages), without knowledge of the CSI,
such that the transmitter and the RIS can anticipate the blockage and act
intelligently is an interesting and innovative concept that requires further
investigation. As mentioned in [249], predicting blockage occurrences requires
sensing the communication environment and processing the data using deep
learning and ray casting techniques to allow for other functionalities such as
users’ and non-users’ positioning. The lack of mathematical models for
blockage predictions has motivated researchers to investigate predictors based
on machine learning models (e.g., see [250] and references therein). Hence,
the development of machine learning aided prediction techniques for RIS-
enabled VLC systems is another promising research direction. Such designs
should consider the unique characteristics of RIS-enabled VLC systems such as
the limited range of the VLC APs, the structure of the RIS devices, and the
features of VLC signals. The concept of meta-learning [251] should also be
leveraged to reduce any training overheard involved.

####  VIII-2 Practical implementation and analysis

The existing studies on RISs-enabled VLC systems reveal theoretical findings
and simulation results but nothing on prototyping, practical implementation
and analysis. Moreover, most studies make the assumption of perfect RIS
hardware which will not generally be the case in practice. Thus, RIS
technology for OWC systems in general is still in its infancy as there have
not been any prototype designs and experimental results reported. The first
step to the practical implementation of RISs in OWC is to develop a proof-of-
concept platform to evaluate and illustrate the performance of RIS-enabled VLC
systems. Such a platform will allow researchers and industry players to
collaborate, evaluate, and compare different RIS optimization algorithms,
performance metrics (e.g., data rate, computational complexity, production
cost, overhead cost, reliability, etc.) and system-level requirements for IMR
and IMA RISs in VLC systems under more practical channel conditions and non-
perfect RIS hardware scenarios that consider the impact of temperature on
reflection/transmission characteristics, phase/yaw/roll responses with errors,
etc. The results and analysis from such a platform would validate theoretical
and simulation findings, and accelerate the development of RISs for OWC in a
low-cost and timely manner.

####  VIII-3 Transmitter-channel-receiver (TCaR)-RIS-assisted VLC systems

Existing studies on RIS-enabled VLC systems have considered deploying the RIS
either inside the receiver or in the channel. However, the RIS module could be
deployed in the transmitter, the receiver, and the channel to realize
transmitter-channel-receiver (TCaR)-RIS assisted VLC systems. Located within
the transmitter, the RIS could be used as a refractive element that assists in
beam generation. Within the receiver, the RIS can enable dynamic FoV and
perform incident light amplification and selective interference rejection.
When deployed in the medium between the transmitter and the receiver (i.e.,
the channel), the RIS can achieve signal coverage and illumination expansion,
and enhance security and signal power transmission. To date there have been no
studies on TCaR-RIS-assisted VLC systems, and thus it will be interesting to
explore how RISs can be deployed at the transmitter, the receiver, and in the
channel to achieve performance improvements. Potential research areas include
investigating key performance metrics to characterize such a novel system
model, proposing channel models, and developing low-complexity optimization
algorithms to efficiently optimize the performances of the RISs deployed in
the various components of a TCaR-RIS-assisted VLC system. Thus, it is an
important and challenging research direction to investigate the performance
trade-offs in TCaR-RIS-assisted VLC systems.

####  VIII-4 Advances in optical RISs materials

Although the application of reflect-array antennas in communication related
applications has been well studied and developed [252], the same cannot be
said about optical RISs. The materials (i.e., metasurfaces, mirror arrays,
LCs, and their tuning mechanisms) used in the construction of optical RISs
have not yet been well studied. For instance, the deployment of LC-based RISs
at the receiver side has been demonstrated to provide beam steering and light
amplification to enhance the data rate performance in VLC systems [50].
Moreover, the concept of intelligent omni-surfaces capable of transmitting and
reflecting signals simultaneously in OWC systems has been introduced in [253,
254]. However, several research challenges remain with regards to the RIS
material type, channel modeling, optimum operating conditions (e.g., optimum
refractive index, temperature, required voltage, etc.) and noise modeling in
such novel applications. Furthermore, the absorption, reflection, and
transmission characteristics of the various metasurface materials, mirror
arrays, and LCs are not yet well understood as they depend on optical
properties such as the optical wavelength range, refractive index,
birefrigence, and temperature range. The performance of the various tuning
mechanisms and the expected lifespan (e.g., for the actuators used in the IMA)
need to analyzed. Finally, it is important to factor in the availability of
the various RIS materials and their cost.

####  VIII-5 RIS-enabled hybrid RF/VLC and standalone VLC systems

VLC is typically deployed in the downlink of an OWC system while other
technologies (e.g., RF or IR communications) are used for the uplink, forming
a hybrid RF/VLC system with a decoupled uplink and downlink [255, 256]. In
such a hybrid design, RISs can be deployed for system performance enhancements
when RF-based RIS and VLC-based RIS techniques are utilized in the uplink and
downlink, respectively. Moreover, RISs can be deployed in hybrid systems that
use RF and VLC for the downlink and only RF for the uplink to improve the
system performance. For instance, a single RIS with several elements (assuming
an RIS that can reflect and transmit both RF and VLC signals exists) can be
shared on time-sharing basis or via dynamic clustering to aid uplink and
downlink transmissions in a single user hybrid RF/VLC system. Furthermore,
RISs can be deployed in several other standalone VLC networks as described in
[75], that use VLC for both uplink and downlink paths. However, there have
been no studies on RIS-enabled hybrid RF/VLC systems and RIS-aided standalone
VLC systems that use a single/multiple RIS(s) to simultaneously enhance
uplinks and downlinks. As a result, several technical challenges relating to
the duplexing techniques, RIS control and resource optimization algorithms,
distribution of the RISs, suitable RIS material for RF and VLC signals, as
well as the associated overhead cost and implementation complexity remain to
be tackled.

##  IX Conclusion

This paper has presented a comprehensive review of the design, application,
and performance analysis of RISs in OWC in general and VLC in particular.
Firstly, an overview of metasurfaces and mirrors and how they obtain their
reconfigurability have been presented from the physics point of view. Then,
the various RIS technologies suited for optical communications and their
corresponding deployment scenarios have been explored. Afterward, the
distinction between optical RISs and relays and their typical functionalities,
benefits, and drawbacks have been presented. Furthermore, important issues
such as RIS reflection design for solving LoS blockages, RISs for VLC
transmitter and receiver performance enhancements have been thoroughly
studied. More particularly, the standard RIS technologies for such roles have
been presented, and design guidelines and performance optimization techniques
(e.g., optimization problem structure, optimization variables, and promising
solution techniques) have been provided. Simulation results have demonstrated
how properly selecting an RIS technology for a VLC system and optimizing its
performance can reduce the operational complexity of the RIS and yield
significant data rate performance improvements. Finally, the potential role of
RISs in other emerging wireless technologies, important challenges that should
be expected, interesting open issues, and future research directions have been
provided. It is hoped that this paper will provide helpful guidance for future
research in this emerging and promising area of integrating RISs and VLC
systems. This is crucial in harnessing the full potential of the RIS
technology to enable the design of cost-effective, highly reliable, and
sustainable future wireless networks.

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