estimation results. To enhance the estimation accuracy, data augmentations are
leveraged to enrich the training samples. The numerical results provided by
the authors showed that the proposed machine learning approach achieves a
comparable performance to the least-squares benchmark estimator, with
significantly lower computational complexity.

The authors of [197] addressed a DFRC problem with an aid of a STAR-RIS.
Specifically, the objective of the work was to prevent the unauthorized
disclosure of legitimate user information to a potential eavesdropping sensing
target. The authors seek to maximize the average security rate of the
legitimate user by jointly optimizing the receive filters and transmit
beamforming in the BS, along with the transmitting and reflecting coefficients
of the STAR-RIS, subject to constraints on the minimum echo SNR of the radar
target and the achievable rate of the legitimate user. Due to the complexity
of the problem, finding the globally optimal solution is challenging. To
address this challenge, two deep reinforcement learning algorithms were
presented for the joint design of receive filters, transmit beamforming of BS,
and transmitting and reflecting coefficients of the STAR-RIS.

Tackling the same security aspect of RIS-assisted ISAC but taking a different
approach, the authors of [229] explored secure DFRC transmission via
incorporating artificial noise, as discussed in Section VII-C. To maximize the
secrecy rate, the authors jointly optimized the transmit beamformer, the
artificial noise vector, and the RIS phase shifts. Employing a soft actor-
critic based deep reinforcement learning algorithm, policy diversity was
enhanced through entropy maximization, guaranteeing convergence to the
globally optimal solution.

The study in [230] presented an innovative approach for waveform design in
RIS-assisted DFRC systems. The primary goal of the paper was to simultaneously
maximize the SINR for the radar and minimize the communication MUI energy.
Addressing the intricacies of the challenging waveform design optimization
problem, the authors introduced a decoupled online learning network based
algorithm. The proposed framework comprises three key modules: trainable
network parameters, loss calculation, and back-propagation. The loss
calculation module computes the cost function with fixed network parameters.
Following this, a deep learning optimizer is employed to train the network
parameters, minimizing the loss function and ensuring convergence. Simulation
results provided compelling evidence for the effectiveness of the learning-
based method. The proposed approach achieved a 0.69 dB enhancement in radar
SINR and a remarkable 138.8% improvement in the sum rate when compared to
conventional methods.

A proximal policy optimization based beamforming design was proposed in [231]
for an RIS-assisted DFRC system in the THz band. The focus was on maximizing
the communication capacity subject to ergodic constraints. In tackling this
challenging optimization problem, the authors introduced a proximal policy
optimization algorithm. Since the literature on this topic lacks in-depth
exploration for ISAC systems in the THz band and its relevant considerations,
the proposed algorithm is deemed effective for the joint optimization problem
adopted in the paper.

Challenges, opportunities and open research directions: While machine learning
tools have rapidly emerged in many communication and sensing applications,
they still pose several challenges. One of these challenges is the inherent
bias towards the training data, which can impact system performance under
nonstandard conditions. Although this issue can be mitigated by employing
reinforcement learning approaches, the latter still requires an accurate
description of the environment and a proper reward system. Thus, the
universality of the machine learning approach is an important factor to
consider.

On the other hand, when applied properly, machine learning tools offer a
plethora of advantages, ranging from solving highly challenging optimization
problems to reducing complexity. Moreover, machine learning algorithms can
adaptively optimize the parameters of RIS-aided ISAC systems based on real-
time data, leading to improved performance and efficiency in practice. Machine
learning techniques can be particularly valuable for addressing the impact of
imperfect CSI, a common challenge in ISAC. Learning-based methods can
dynamically adapt to changing channel conditions, enhancing system robustness.
Additionally, they can reduce the need for extensive pilot signaling in
channel estimation, resulting in increased spectral efficiency and improved
communication rates.

##  VII Promising Applications of RIS-Assisted DFRC

In addition to the design aspects of RIS-assisted DFRC covered in the
preceding section, numerous research efforts have concentrated on exploring
the application domains of this technology. In this section, we provide an
overview of existing research works that propose promising applications for
RIS-assisted DFRC.

###  VII-A Full-Duplex Systems

A significant body of research on RIS-assisted ISAC has predominantly focused
on half-duplex configurations. Transmitting and receiving using different
frequency bands (i.e., frequency-division multiplexing (FDM)) poses a
challenge in ISAC as both communication and sensing performance are highly
reliant on the frequency band [5]. Alternatively, transmitting and receiving
at different times, known as time-division multiplexing (TDM), is commonly
considered in ISAC system models, but it still imposes limitations on system
capabilities such as degrading the communication rate. On the other hand,
full-duplex systems, where the transmission, reception and echo processing
happens in the same time-frequency resources, can offer improved system
performance. This has spurred the interest of several researchers to
investigate the performance of full-duplex RIS-assisted ISAC systems [232,
233, 234, 235, 236].

For example, the authors in [234] investigated maximizing the data rates for
the communication users while ensuring high-quality radar sensing in a full-
duplex RIS-assisted ISAC system. Additionally, this paper investigates a
comprehensive signal propagation model that fully considers the impact of the
RIS on both the forward and reflected radar probing signals. By employing the
PDD method along with the MM algorithm, the authors found that the full-duplex
system is capable of increasing the communication sum rate of the system by
100% and 125% compared to the corresponding half-duplex systems with and
without RIS, respectively. More interestingly, the full-duplex RIS-assisted
system is capable of achieving a better sum rate even with a high radar SNR
requirement than its half-duplex counterpart, resulting in excellent
communication and sensing performance.

Challenges, opportunities and open research directions: In general, there are
multiple challenges and considerations for full-duplex RIS-assisted DFRC.
First, full-duplex systems require sophisticated signal processing techniques
to effectively separate the transmitted and received signals. This complexity
increases in the presence of an RIS and the dual functions of DFRC,
necessitating advanced algorithms for self-interference cancellation and
signal enhancement. Practical implementation of full-duplex systems involves
challenges in designing transceiver hardware that can handle simultaneous
transmission and reception without significant degradation in performance. One
accentuated hardware consideration is the increased power consumption
requirement compared to half-duplex systems. This can be a concern, especially
in energy-constrained scenarios or battery-operated devices.

Balancing the challenges with the opportunities is crucial in designing
effective and practical full-duplex RIS-assisted DFRC systems. The
opportunities of full-duplex RIS-assisted DFRC systems include enhanced
spectral efficiency. By enabling simultaneous transmission and reception,
full-duplex systems can achieve higher spectral efficiency, leading to
improved sensing and communication performance. Full-duplex DFRC also enhances
the reliability of DFRC by allowing for real-time feedback and re-transmission
in case of errors, improving the quality of service for communication and
sensing applications. Moreover, channel reciprocity can be exploited in the
full-duplex system to minimize the complexity of channel estimation, target
detection, and resource allocation.

###  VII-B Wideband Systems

Using a CCD, the authors in [152, 184, 237] and [238] focused on employing a
wideband OFDM waveform in RIS-assisted ISAC systems. In [152], the authors
investigated a wideband RIS-assisted ISAC system in a clutter-rich environment
caused by the NLoS signals, an aspect that is often overlooked in RIS-assisted
system research. The BS aims to simultaneously detect multiple targets and
communicate with multiple users with the aid of an RIS. A joint RIS-assisted
ISAC design was proposed to maximize the minimum sensing beampattern gain
while ensuring a certain QoS requirement for each communication user, with the
objective of reducing clutter interference caused by the NLoS signal
components. The results show that the presence of an RIS can increase the
minimum beampattern gain by a factor of six, thanks to the RIS’s capability of
reducing clutter interference.

Challenges, opportunities and open research directions: Wideband operation
introduces increased complexity in signal processing and hardware design.
Handling a broader spectrum of frequencies requires more sophisticated
algorithms and flexible hardware architectures. In wideband scenarios,
accurate channel estimation becomes more challenging due to frequency-
selective fading and the beam-squint effect. When ignored, frequency-selective
fading can also affect the performance of the system, as the reflection
patterns of the RIS elements are frequency-dependent. Furthermore, wideband
operation often demands higher power consumption compared to narrowband
systems. Managing power requirements becomes a critical consideration,
especially in energy-constrained scenarios.

On the other hand, various opportunities can be realized from wideband RIS-
assisted DFRC systems. The most obvious one is the enhanced spectrum
utilization. Wideband operation allows for more efficient use of the spectrum,
aligning with the fundamental goal of ISAC. Moreover, the use of wideband
signals in radar applications enhances range and Doppler resolution. This
improvement can lead to enhanced target discrimination and tracking
capabilities, especially in complex and dynamic environments.

###  VII-C Physical Layer Security

Sharing the waveform, beamformers, and spectrum in DFRC can introduce several
security threats. Since the DFRC BS transmits a combined radar and
communications signal, the risk of information leakage to illegitimate users
exists in these systems. The capability of RISs to enhance PLS in
communication systems has been investigated in several studies. Motivated by
this, several research works have investigated the capability of RISs to
enhance PLS in communication systems [58, 239, 240, 241, 242]. While some of
these works were already discussed in Section VI-F for STAR-RISs, we overview
two additional aspects in this subsection.

1) Propagation manipulation: Since RISs can adjust the phase of the incident
signals, they can direct the signal towards specific desired locations. This
implies that RISs can be used as an additional resource to optimize a system
security measure. One of the most common security measures for communications
is the secrecy rate, which is defined as the amount of information that can be
reliably transmitted per channel use that is also inaccessible to potential
attackers. The secrecy rate can be expressed as

$R_{\text{sec}}(\boldsymbol{\Phi})=\max(0,R_{\text{leg}}\big{(}\boldsymbol{\Phi})-R_{\text{eav}}\big{(}\boldsymbol{\Phi})\big{)},$
(29)

where $R_{\text{leg}}\big{(}\boldsymbol{\Phi})$ and
$R_{\text{eav}}\big{(}\boldsymbol{\Phi})$ denote the rates of the legitimate
user and the eavesdropper, respectively [243].

The secrecy rate optimization problem can manifest itself in various forms
depending on the potential eavesdropper’s location. For instance, in [244],
the authors considered a scenario where the target is a potential
eavesdropper, and attempted to minimize the information accessible to the
target by imposing a constraint on the maximum information leakage. The
scenario of a potentially eavesdropping target was also addressed in [245]
with the objective of maximizing the system’s secrecy rate. This was further
studied with partial CSI knowledge at the BS in [188]. Another system model
was explored in [246], assuming the potential eavesdropper to be one of the
communication users. This scenario is more challenging, as the security
measures should be carefully imposed, while at the same time they should not
severely affect the communication performance. To address this problem, the
authors aimed to minimize the maximum eavesdropping SINR while ensuring QoS
requirements for the communication users.

2) Artificial noise: Another approach to enhance the security of DFRC systems
is to incorporate a noise-like signal into the transmit waveform [247, 248,
249, 250]. Thus, the transmit communication signal takes the following form:

$\mathbf{x}_{c}=\mathbf{W}\mathbf{s}+\mathbf{W}_{n}\mathbf{n}_{a},$ (30)

where $\mathbf{W}$ and $\mathbf{W}_{n}$ are the information and the artificial
noise precoders, respectively, $\mathbf{s}$ is the information symbol vector,
and $\mathbf{n}_{a}$ is the artificial noise vector with elements drawn from a
specific distribution, usually Gaussian.

While the artificial noise affects both the legitimate user and the
eavesdropper, the precoder of the noise is co-designed with the information
precoder and the RIS phase shifts to maximize a system security metric while
ensuring a threshold on the communication performance. For example, it was
shown in [247] that the presence of an RIS leads to a seven-fold increase in
the secrecy rate for DFRC systems when the artificial noise approach is
employed.

Challenges, opportunities and open research directions: PLS in RIS-assisted
DFRC systems presents both challenges and opportunities. On the side of
challenges, the combination of radar and communication signals in DFRC systems
introduces the risk of information leakage. Securing the system against
eavesdropping and unauthorized access becomes more complex due to the
multifunctional nature of the signals. Additionally, the dynamic and often
unpredictable nature of the environment, including user mobility and changing
channel conditions, poses challenges for designing robust security mechanisms.

On the flip side, machine learning algorithms can be employed to enhance PLS
in RIS-assisted DFRC systems. These algorithms can adaptively learn and
optimize security measures based on real-time observations, improving the
system’s ability to detect and respond to security threats. Furthermore, RISs
can be utilized for secure key distribution, enabling the establishment of
cryptographic keys for communication data encryption.

However, realizing these opportunities requires addressing several technical
challenges. Efficient algorithms for secure beamforming, dynamic adaptation of
security measures, and robust key management in the presence of RISs need to
be developed. Moreover, the integration of security mechanisms should not
compromise the primary functions of radar and communications, necessitating a
careful balance between security and performance.

###  VII-D Non-Orthogonal Multiple Access

NOMA can provide better spectral efficiency by allowing users to share the
same time-frequency resources via multiplexing them in the power domain or the
code domain [251, 252]. While several researchers considered code-domain NOMA
as a variant of code-division multiple access (CDMA), power-domain NOMA has
gained increasing popularity in the last decade [253] due to its ability to
provide higher spectral efficiency. In power-domain NOMA, each user is
allocated a power level depending on its channel strength, and a successive
interference cancellation (SIC) technique is applied at the receivers to
control the interference caused by the resource sharing among users [55].

Since ISAC and NOMA share the goal of using the spectrum more efficiently, the
integration of the two technologies has been studied in [254, 255, 256] with
the aid of RISs. More specifically, the authors of these papers considered an
RIS-assisted DFRC system with NOMA applied to achieve better communication
performance. Although the use of NOMA targets mainly improves the
communication performance, a more efficient use of the spectrum for the
purpose of communication allows better spectrum utilization for sensing also.
For example, the authors of [254] reported a 300% improvement in the radar
minimum beampattern gain caused by the use of NOMA for communications.

Rate-splitting multiple access (RSMA) is a generalized version of power-domain
NOMA. In RSMA, the transmitted data is divided into distinct layers. These
layers are then transmitted concurrently over the same time-frequency-code
resources. At the receiving end, each user aims to decode its designated layer
while treating the other layers as interference. This approach is known as
rate splitting and involves dividing the total rate among the users [257,
258]. The fundamental concept behind RSMA is to leverage the interference
among users constructively rather than considering it as a hindrance. This
strategy has the potential to enhance the spectral efficiency and increase the
overall throughput of the system [259].

The use of the RSMA transmission scheme for RIS-assisted DFRC was investigated
in [260] with the goal of maximizing the SNR for target detection. Simulation
results showed that a gain of approximately 10 dB can be attained through the
combined effect of using RISs and RSMA, compared to classical space-division
multiple access (SDMA) systems that do not utilize RISs for transmission. It
is worth mentioning, nevertheless, that this advantage comes as the cost of
increased transmitter and receiver complexity.

###  VII-E Intelligent Transport

The increasing demand for intelligent transport applications, encompassing
vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-
everything (V2X) communications, highlights the significant role of ISAC in
such scenarios [261, 262]. A key distinguishing feature that makes ISAC well-
suited for intelligent transport applications is the predictability of motion.
Unlike general communication scenarios, vehicles, trains, and buses follow
pre-defined routes. This predictability facilitates the development of low-
complexity solutions for both communication and sensing aspects within the
context of intelligent transport applications.

In a notable contribution to RIS-aided ISAC, the work presented in [263]
addresses NLoS communications. In the proposed system model, the BS
communicates with a target vehicle while concurrently estimating its distance
and velocity. In scenarios where the LoS is blocked, the RIS serves the dual
purpose of radar sensing and communication. It achieves this by estimating the
location of the vehicle and concentrating the signal power in the estimated
direction. The method employed for accurate distance and velocity estimation
is the maximum likelihood method. The communication performance is evaluated
based on data rate, considering the estimation accuracy. Similar problems were
also tackled in [264, 265]. Additionally, in [266], the authors explored an
RIS-assisted ISAC framework designed for high-speed railways.

The authors of [235] and [267] introduced a system model featuring an ISAC BS
communicating with multiple vehicles while simultaneously sensing their
locations. An innovative aspect of the model involves the deployment of RISs
on the rooftops of the vehicles to enhance the sensing performance by
adjusting the phases of the echo signals. To address the challenge of joint
communications and sensing, the authors devised a grid-based parametric model.
They formulated the joint estimation problem as a compressive sensing problem
and employed a message-passing algorithm utilizing a progressive approximation
method. This approach was shown to be effective in reducing computational
complexity. Furthermore, an expectation-maximization algorithm was employed to
fine-tune the grid parameters, alleviating potential model mismatch issues.

To leverage the predictability of motion in vehicular networks, the authors of
[268] and [269] introduced a low-complexity predictive beamforming scheme for
a system where a BS communicates with multiple vehicles equipped with STAR-
RISs. In this system model, STAR-RISs are mounted on the tops of the vehicles
to enhance both the sensing and communication performance for users inside the
vehicles. The BS transmits a communication signal, part of which is reflected
back to the BS via the STAR-RIS for location sensing, while the other part is
transmitted to serve the user inside the vehicle.

The sensing model aims to predict the distance, angle, and velocity of each
vehicle, and the BS constructs the channel matrix based on these estimated
quantities. Two models are employed for this purpose: the state evolution
model and the measurement model. The measurement model is obtained from the
collected echo signals, while the state evolution model estimates the
distance, angle, and velocity of each vehicle based on geometric relationships
[270, 271].

Figure 13: State evolution model for predictive beamforming.

To illustrate, consider a BS attempting to align the beam toward a vehicle
moving along a straight road, as shown in Fig. 13. In the initial stage, the
BS conducts pure radar sensing to determine the distance, angle, and velocity
of the vehicle in the first time slot, denoted as $d_{0}$, $\theta_{0}$, and
$v_{0}$. In the subsequent time slots, the BS, on the basis of employing a
number of geometric relationships, estimates the values of these quantities.
These relationships are derived in [270] and are summarized as follows:

$\begin{cases}\theta_{n}=\theta_{n-1}+d_{n-1}^{-1}v_{n-1}\Delta
T\sin\theta_{n-1}+z_{\theta},\\\ d_{n}=d_{n-1}-v_{n-1}\Delta
T\cos\theta_{n-1}+z_{d},\\\ v_{n}=v_{n-1}+z_{v},\end{cases}$ (31)

where $d_{n}$, $\theta_{n}$ and $v_{n}$ denote the distance, angle and
velocity of the vehicle with respect to the BS at the $n$-th time slot,
respectively, $\Delta T$ is the time difference between two time slots, and
$z_{d}$, $z_{\theta}$ and $z_{v}$ are the combined effect of approximation
errors and estimation noise for the distance, angle and velocity,
respectively. One assumption observed from the velocity estimation in (31) is
that the vehicle is locally moving at a constant speed, which may not be
accurate. To correct the estimation, a Kalman filter is employed to combine
the state evolution model with the echo measurement model and obtain refined
estimates. The channel matrix is then constructed, and the resources are
allocated by solving optimization problems. While the system can operate by
observing the reflected echoes from the vehicles directly, the presence of
STAR-RISs at the top of the vehicles enhances sensing performance, leading to
better-constructed channels and improved communication performance.

###  VII-F Sub-Space Rotation

|  |
---|---|---
(a) | (b) | (c)
Figure 14: Three levels of coupling between the communications and sensing
sub-spaces: (a) weak coupling, (b) moderate coupling, and (c) strong coupling.

The authors of [272] presented a unique and interesting approach to exploit
the use of RISs in DFRC.

The ISAC signals lie within the space spanned by communication and sensing
sub-spaces. The correlation between these sub-spaces can be quantified by
their intersection angle. Consequently, the performance of sensing and
communication is related to the projection of the ISAC signal onto the
respective sub-space. To illustrate these ideas, the authors defined three
levels of coupling. In the weakly coupled scenario (illustrated in Fig.
14(a)), the two sub-spaces are nearly orthogonal, leading to projections close
to the minimum. Due to the orthogonality between the communications and
sensing sub-spaces, satisfying both requirements simultaneously becomes
challenging. For instance, if a communication user and a radar target are
situated at opposite locations relative to the BS, attempting to design an
optimal beam for one adversely affects the other. In moderately coupled
scenarios, as depicted in Fig. 14(b), the angle between the sensing and
communication sub-spaces is less than $90^{\circ}$, resulting in a better
trade-off between communications and sensing compared to the case of weak
coupling. Finally, in the scenario of strong coupling, illustrated in Fig.
14(c), the two sub-spaces are perfectly aligned, leading to optimal
performances for both sensing and communications. An example of this occurs
when a communication user is also a radar target; in such a scenario, forming
beams towards the direction of the communication user yields optimal
communication and sensing performance.

The authors of [272] aimed to leverage the RIS to enhance the coupling between
the communication and sensing sub-spaces, thereby reducing the trade-off
between the two functions. To evaluate the effectiveness of this approach, the
authors generated Pareto-optimal curves for sensing performance (quantified by
the CRB of the AoA estimation) and the communication performance (quantified
by the achievable rate). Fig. 3(c) in [272] illustrates the behavior of the
improved Pareto-optimal front based on simulations conducted in [272]. These
results demonstrate that the sub-space rotation approach can achieve a point
in the upper-left corner of the Pareto-optimal front, which is unattainable in
the weakly coupled case.

###  VII-G Near-Field Operation

Recently, several research works have explored the near-field operation of
RIS-assisted DFRC.

In [273], the authors aimed to establish the fundamental limits of RIS-aided
localization and explore potential performance improvements in RIS-aided
communication. The study focused on the near-field operation of RIS-aided
ISAC, emphasizing the careful control of the phase response of an RIS to
ensure that the signals scattered by different parts of the surface coherently
superimpose at the receivers. The paper also introduced the concept of
continuous intelligent surfaces and established the performance limits of RIS-
aided communication and RIS-aided localization. Additionally, the study
derived near- and far-field ISAC signal models, addressed the optimal design
of the phase response for RISs in various scenarios (including those with
obstacles and multiple surfaces), and quantified the performance gain in
localization and communication facilitated by RISs in complex wireless
environments.

In [274], the authors concentrated on optimizing a full-duplex DFRC system,
comprising a single communication user and a single radar target. Initially,
the authors derived an exact expression for the CRB, considering the cases of
both LoS and NLoS propagation paths. Subsequently, the authors formulated a
problem to jointly minimize the power of the effective self-interference
signal in the uplink case at the radar receiver while simultaneously
maximizing the achievable rate for the DL communication user, subject to the
exact CRB constraint. The joint optimization problem was transformed into an
equivalent MSE minimization problem, consisting of two terms: the self-
interference power at the radar and the MSE at the communication user.
Simulation results showcased that the proposed scheme significantly
outperforms the conventional far-field DFRC scheme without an RIS.

In another study [274], a two-timescale joint optimization problem was
explored, involving the BS precoder and the RIS reflection coefficient matrix.
The main objective of this research was to minimize tracking error within an
uncertainty region, considering position uncertainties between consecutive RIS
optimization steps in both near- and far-field regions. Unlike the common
practice of maximizing the energy toward the instantaneous communication user
position, the optimization problem in this paper aimed to minimize the inverse
of the received power within the uncertainty area of the communication user.
This optimization procedure included trajectory prediction within a finite
time horizon, covering the period until the subsequent optimization step. This
strategy can address the "deafness problem" associated with tracking loss due
to narrow beams, a challenge in conventional RIS optimization methods.

###  VII-H Extended Reality

In 6G and beyond networks, the applications of human-machine interactions are
expected to flourish, with extended reality (XR) playing a crucial role. This
motivation prompted the authors of [275] to explore an ISAC framework in the
context of XR, with the aid of an RIS. Specifically, a practical positioning
approach utilizing the multiple signal classification (MUSIC) algorithm is
proposed, enhanced by specially designed RIS configurations. Additionally, the
paper addressed the joint optimization of the transmit beamformer and the RIS
phase shifts, aiming to maximize channel capacity under CRB constraints. The
optimization problem was tackled through alternating optimization with
gradient projection and manifold optimization. Simulation results were then
presented, showcasing the feasibility of the proposed positioning algorithm in
XR applications.

##  VIII Holographic ISAC

Holographic ISAC refers to employing RHSs for transmission and/or reception in
ISAC. While the work in this area is still limited, promising results and
prototypes have been presented in the literature. In this section, we first
discuss the beamforming design of RHS-assisted ISAC, followed by summarizing
the implementation efforts along with experimental results.

###  VIII-A Holographic Beamforming

The authors of [276] proposed an ISAC transmission scheme for a BS serving
multiple DL communication users and simultaneously detecting multiple radar
targets. Unlike RIS-assisted ISAC systems, the BS and the RHS are co-located
within the same unit. The RHS is utilzed for signal transmission, while an
antenna array is utilized to receive the targets’ echo signals. In other
words, the BS functions as a terminal that processes the echoes received by an
antenna array and sends the signals to the RHS for transmission [15]. The BS
and the RHS employ digital and analog beamforming, respectively, to transmit
the ISAC signals. This allows the system to sense many targets simultaneously
and provide different data streams to the DL users.

In the transmission phase, distinct communication and radar digital
beamformers are employed to precode $L$ data streams and $K$ radar waveforms
separately. This process results in a total of $2K$ signals: $K$ precoded
communication data streams and $K$ precoded radar waveforms. The superposition
of the $k$-th data stream and the $k$-th radar waveform is then obtained to
reduce the number of transmit signals to $K$. Following the digital signal
processing, $K$ signals are sent to $K$ RF chains, where the $K$ RF chains,
with in turn modulate these signals and deliver them to the feed of the RHS.
At the RHS, the modulated signals are converted to radiation patterns through
the waveguide and metamaterial elements for analog processing, where an analog
beamformer is used to determine the radiation amplitudes of the RHS elements.

In the reception phase, communication and sensing are carried out
simultaneously. Specifically, the BS receives the echo signals reflected by
the targets via the antenna array and processes them to produce the radar
signals. At the same time, the communication users receive the ISAC signals
broadcast by the RHS and decode their data streams.

In this process, $K$ different holographic patterns are produced, each
corresponding to a single feed, and can be constructed for each desired
direction. The analog beamformer is then designed by superpositioning the
output holographic patterns in a weighted manner, a technique known as
holographic-pattern-division multiple access (HDMA) [277]. The beamformer is
optimized by choosing the weights of the holographic patterns to minimize the
beam pattern mismatch error while setting a threshold on the communication
QoS.

In order to tackle the complexity of the optimization problem, the authors
proposed an AO framework where they alternate between optimizing the digital
and analog beamformers. The digital beamforming sub-optimization problem is
tackled using the ZF approach to mitigate inter-user interference for all
communication users. To optimize the weights of the analog beamformer, the
sub-optimization problem is reformulated as a quadratic program, and solved
using the SDR technique.

###  VIII-B RHS-Assisted ISAC Prototype

A prototype of an RHS-Assisted ISAC system was presented by the authors of
[15]. In particular, the authors designed a 1D RHS with a length, width, and
thickness of 15 cm, 3 cm, and 0.17 cm, respectively. The substrate is made up
of four layers and serves as a waveguide. Each of the sixteen feed lines is
linked to a metamaterial element. The other end of the feed lines is connected
to a field programmable gate array (FPGA) output pin, which supplies a bias
voltage to the component.

The ISAC transceiver module serves as an ISAC BS for both transmitting and
receiving ISAC signals. The host computer employs an FPGA to control the
radiation amplitudes of the RHS elements. Signal frequency conversion between
baseband and passband is facilitated by a frequency converter that performs
both down-conversion and up-conversion. For echo reception, a standard horn
antenna (LB-75-20-C-SF) with a frequency range of 10-15 GHz is utilized.

The user module acquires the communication stream by receiving and decoding
the ISAC signal transmitted by the ISAC transceiver module. The receive
antenna captures the ISAC signal, which is then sent to the frequency
converter for down-conversion to the equivalent baseband signal.

The target module simulates radar targets by generating controlled radar echo
signals, as demonstrated in [278]. Activation of the target module occurs when
the receiving antenna captures the ISAC signal broadcasted by the RHS. To
emulate targets at different ranges, variations in the time delays have been
implemented.

The experimental results show that that the RHS exhibits slightly higher gains
towards the directions of interest compared to the phased array. However, the
power consumption of the phased array is 30 times greater than that of the
RHS. This suggests that the RHS is capable of supporting ISAC with similar
performance and significantly lower power consumption compared to the phased
array.

The experimental results also indicate that the estimated range closely aligns
with the actual range, affirming the viability of sensing through holographic
ISAC. Furthermore, the data rate between the BS and the user is measured at 5
Mbit/s, underscoring that communication between the BS and the user remains
supported even as the BS conducts radar sensing concurrently.

###  VIII-C Challenges, Opportunities and Open Research Directions

The main motivation for using RHSs as transmitters or receivers is their
practical validity for ultra-massive MIMO systems. Nonetheless, for large-
scale RHSs, the signal attenuation within the waveguide becomes a considerable
factor that cannot be overlooked, leading to a substantial reduction in
antenna efficiency. Therefore, optimizing the size of the RHS becomes crucial
to strike a balance between radiation efficiency and antenna gain. Moreover,
the envisioned ISAC scenario holds promise for scaling to higher frequencies,
allowing seamless integration of the RHS with mmWave and THz systems to
minimize the system’s profile and weight. Innovative design of metamaterial
structures become imperative to enable a high antenna gain for the RHS in the
mmWave and THz bands, mitigating the substantial path-loss associated with the
mmWave and THz transmissions.

Channel estimation is another challenge in RHS-assisted ISAC, a crucial aspect
for optimizing communication and sensing performance. However, channel
acquisition becomes complicated in the RHS case due to the hybrid structure
and the substantial number of antenna elements in RHSs. One direct strategy is
to exploit the amplitude-controllability of RHSs and estimate the channel for
each element in a time-division manner by activating one element only in each
time slot. Yet, the time and pilot overhead linked to this method increases
with the size of the RHS, implying that the time and pilot cost could be
practically infeasible for a large-sized RHS [278]. Hence, there is a need for
innovative methods that can concurrently estimate channels for multiple
elements to alleviate the signaling overhead burden.

Furthermore, the signals at RHSs experience modulation through the
superposition of holographic patterns before being received by the feeds,
introducing complexity to AoA estimation compared to traditional phased arrays
where signals are directly received by the feeds. To address this complexity,
the maximum likelihood method can be utilized for AoA estimation.
Specifically, for a large-scale RHS, it becomes crucial to employ an efficient
estimation algorithm capable of conducting a multi-dimensional search and
identifying the optimal AoA solution. Nevertheless, the computational
complexity associated with multi-dimensional searches may be prohibitive for
RHSs with large dimensions.

##  IX Conclusion

This survey provided a comprehensive review of the existing literature on ISAC
assisted by metasurfaces, discussing the associated challenges and
opportunities. The article began by offering essential background information
on standalone metasurface-assisted communication and metasurface-assisted
sensing systems, thereby setting the stage for a detailed discussion on the
fundamentals of ISAC. Subsequently, the current studies on RIS-assisted ISAC
were meticulously summarized, emphasizing the two levels of integration:
radio-communications co-existence and dual-function radar-communications. The
article then discussed the current works in the area of holographic RHSs from
theoretical and experimental points of view. Within each category, we
presented the challenges faced and outlined various prominent opportunities,
providing a holistic perspective on the evolving landscape of RIS-assisted
ISAC.

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