In [247] an RIS was utilized alongside a UAV BS to enhance DFRC performance. In this setup, the UAV performed dual roles by transmitting signals to multiple users while simultaneously conducting sensing tasks. The objective was to maximize the weighted sum of the average sum rate for the communication users and the sensing SNR for the radar target while managing the inherent tradeoff between these objective functions. This was achieved by jointly optimizing the RIS phase shifts, the UAV trajectory, UAV beamforming, and user scheduling. To address the optimization problem, the authors introduced an iterative AO algorithm to derive an efficient suboptimal solution. Notably, the resulting UAV trajectory design significantly improved the Pareto-optimal front for the sum rate and sensing SNR, thereby enhancing both communication and sensing functionalities in DFRC.

**Lessons learned:** The use of UAVs in DFRC systems has proven beneficial due to their flexibility in adjusting their locations to enhance overall system performance. In RIS-assisted DFRC configurations, UAVs can serve one of two functions: acting as an aerial RIS or functioning as a DFRC BS. When the RIS is integrated with a UAV BS, significant improvements in the Pareto-optimal front can be achieved, thanks to optimal trajectory design of the UAV, which enhances the functionalities of communication and sensing in DFRC systems.

**Challenges, opportunities and open research directions:**
One major challenge of utilizing a UAV as part of the RIS-assisted DFRC system is the dynamic nature of UAVs, which can complicate the optimization of communication and sensing functions, requiring advanced algorithms. Additionally, ensuring reliable communication links between UAVs, ground users, BSs and RISs may pose difficulties due to varying environmental conditions and potential interference. However, these challenges also create opportunities for innovation, such as the development of new strategies for cooperative sensing and communication, leveraging UAV mobility to improve coverage and performance in complex environments. Open research directions include exploring advanced machine learning techniques for adaptive resource allocation, enhancing the robustness of UAV-based networks against jamming and other threats, and investigating the integration of energy-efficient approaches to extend UAV operation time.

## I. Sub-Space Rotation

The authors of [248] 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. 18(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. 18(b), the angle between the sensing and communication sub-spaces is less than 90°, resulting in a better tradeoff between communications and sensing compared to the case of weak coupling. Finally, in the scenario of strong coupling, illustrated in Fig. 18(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 [248] aimed to leverage the RIS to enhance the coupling between the communication and sensing sub-spaces, thereby reducing the tradeoff 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 [248] illustrates the behavior of the improved Pareto-optimal front based on simulations conducted in [248]. 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.

**Lessons learned:** Achieving optimal performance for both sensing and communication simultaneously is inherently challenging for the cases where there is weak coupling between communications and sensing sub-spaces. This limitation emphasizes the need for innovative strategies to manage the tradeoffs associated with conflicting objectives in communication and sensing. Leveraging RISs can significantly improve the coupling between these sub-spaces, leading to enhanced performance for both functions. The ability to reach points on the Pareto-optimal front that were previously unattainable in weakly coupled scenarios underscores the effectiveness of using RISs to maximize the coupling between the communication and sensing sub-spaces.

**Challenges, opportunities and open research directions:**
A significant challenge lies in optimizing the tradeoffs between communication and sensing performance, particularly in weakly coupled scenarios where the two functions conflict. This necessitates the development of sophisticated algorithms and frameworks that can intelligently manage these tradeoffs while ensuring robust performance in dynamic environments. Additionally, there is an opportunity to explore novel architectures and configurations of RISs to further enhance the coupling between communication and sensing sub-spaces.
<figure><img src="image_20.png" /><figcaption>Figure 18: Three levels of coupling between the communications and sensing sub-spaces: (a) weak coupling, (b) moderate coupling, and (c) strong coupling.</figcaption></figure>

## J. Near-Field Operation

Recently, several research works have explored the near-field operation of RIS-assisted DFRC. While near-field beam-focusing can improve both the communication and sensing performance of ISAC by directing the beams toward physical locations characterized by specific angles and distances, the mathematical formulations of the beam-focusing are more complicated.

1) Performance analysis: In [249], the authors aimed to establish the fundamental limits of RIS-aided near-field 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.

2) System optimization: In [250], 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.

**Lessons learned:** Studies have established the fundamental limits of RIS-aided near-field localization and communication, highlighting performance gains from optimized phase control of RIS surfaces, especially in complex environments. Additionally, system optimization efforts have focused on minimizing self-interference and improving tracking accuracy, particularly under position uncertainties, showing significant improvements over conventional far-field DFRC systems. These findings emphasize the importance of careful phase design and optimization for RIS in near-field operation to maximize system performance.

**Challenges, opportunities and open research directions:**

A major challenge of near-field operation is the complexity of the mathematical models required for beam-focusing, as the focus shifts from far-field approximations to more intricate near-field formulations. The coherent superposition of signals at receivers, especially in environments with obstacles or multiple surfaces, requires precise control of the RIS phase response, which complicates the system design and optimization. Moreover, the self-interference in full-duplex systems and the tracking loss due to narrow beams, known as the *deafness problem*, add further computational complexity to optimizing system performance. These challenges are particularly pronounced when dealing with position uncertainties and dynamic environments, making it difficult to maintain robust performance across communication and radar functions.

The ability to focus beams on specific angles and distances enables more precise sensing and communication, opening the door for improved localization accuracy and enhanced system performance in complex environments. This creates opportunities for developing continuous intelligent surfaces and novel optimization algorithms that can adapt to both near- and far-field conditions. Open research directions include further exploration of joint optimization strategies, such as minimizing self-interference and tracking errors over dynamic scenarios, and refining beamforming techniques to account for user mobility and trajectory prediction. Addition-
ally, advancing methods to handle position uncertainties and improving the robustness of RIS designs under varying environmental conditions remain key areas for future research.

## K. 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 [251] 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 AO with gradient projection and manifold optimization. Simulation results were then presented, showcasing the feasibility of the proposed positioning algorithm in XR applications.

**Lessons learned:** The integration of RIS into ISAC frameworks holds great potential for XR applications in 6G and beyond networks. Such an integration demonstrates the feasibility of these technologies, suggesting they could be instrumental in realizing efficient human-machine interactions in future networks.

**Challenges, opportunities and open research directions:** Ensuring accurate positioning in dynamic environments particularly for XR, where real-time responsiveness is crucial, is a key challenge. However, the use of RIS-enhanced configurations, combined with algorithms such as MUSIC, offers promising opportunities to improve positioning accuracy and channel capacity in human-machine interactions. Open research directions include refining optimization methods for dynamic XR scenarios, enhancing the robustness of positioning algorithms, and addressing the tradeoffs between complexity and performance in practical deployments.

## L. Movable Antennas

The recently proposed concept of movable antennas refers to a specific type of antenna system designed to adjust its position or orientation to optimize signal reception or transmission [252]. Movable antennas play a significant role in channel customization by enabling dynamic adjustments to optimize signal quality based on the specific needs of a communication channel. In systems where channel conditions vary, movable antennas can be aligned or repositioned to reduce interference, enhance signal strength, and maintain a stable connection. By customizing the channel through antenna movement, users can ensure the best possible performance for different applications, such as data transmission, broadcasting, or secure communications.

Motivated by the fact that both RISs and movable antennas share the common goal of customizing the propagation

environment, the authors of [253] investigated utilizing both to improve DFRC performance. In particular, the authors studied an RIS-assisted DFRC system equipped with a two-dimensional (2D) array of movable antennas. The angles of the targets that need to be estimated are assumed to be in the NLoS area of the BS, so the RIS is used to create a virtual LoS link to boost sensing and communication performance. An optimization problem was then formulated to maximize the radar's minimum beampattern gain while adhering to communication SINR constraints. A method based on SDR, sequential rank-one constraint relaxation, and SCA was then proposed to solve the optimization problem, where the transmit beamforming, RIS phase shifts, and the positions of the movable antennas were designed. The simulation results underscored the benefit of using movable antennas over fixed-position antennas, especially in the high transmit SNR regime, where an improvement of 29% in the radar's minimum beampattern gain was achieved.

**Lessons learned:** Movable antennas enable dynamic channel customization by adjusting their position or orientation to optimize signal quality, particularly in environments where channel conditions vary. When paired with RISs, these systems can improve both radar sensing and communication performance in DFRC systems. Simulation results revealed that this approach significantly outperforms traditional fixed-position antennas, especially at high SNR levels, demonstrating the versatility and effectiveness of movable antennas in advanced wireless communication and sensing applications.

**Challenges, opportunities and open research directions:** The integration of movable antennas and RISs in DFRC systems presents several challenges, opportunities, and open research directions. A key challenge lies in the complex optimization of beamforming, RIS phase shifts, and antenna positioning to achieve real-time performance improvements, particularly in dynamic environments with varying channel conditions. This opens up opportunities for developing more efficient algorithms that can handle these highly non-convex problems while ensuring scalability for large-scale networks. Additionally, the potential of movable antennas to reduce interference and enhance multi-user communication opens new avenues for their application in dense urban and vehicular networks. Open research directions include exploring the impact of movable antennas in three-dimensional (3D) array configurations, investigating their energy efficiency, and addressing the practical limitations of hardware implementations, such as the mechanical constraints and power consumption of continuously adjusting antennas.

# VII. MACHINE LEARNING FOR RIS-ASSISTED ISAC

Machine learning tools have emerged as potential candidates for providing low-complexity solutions for various communication and sensing applications. The application of machine learning techniques in RIS-assisted ISAC has been investigated from several perspectives. This section presents a literature review of machine learning methods for RIS-
<table><caption>Table VII: References on machine learning for RIS-assisted ISAC.</caption><thead><tr><td>Reference</td><td>Year</td><td>RCC or DFRC</td><td>Goal</td><td>Methods</td></tr></thead><tbody><tr><td>[254]</td><td>2023</td><td>RCC</td><td>Resource allocation to maximize the communication mutual information subject to constraints on the radar signal power and the interference energy at the radar receiver</td><td>Meta-reinforcement learning - Markov decision process</td></tr><tr><td>[255]</td><td>2022</td><td>DFRC</td><td>Joint waveform design to maximize the SINR for the radar and minimize the communication MUI energy</td><td>Decoupled online learning network - Deep neural network</td></tr><tr><td>[256]</td><td>2022</td><td>DFRC</td><td>Resource allocation to maximize the communication capacity subject to ergodic constraints</td><td>Proximal policy optimization - Reinforcement learning</td></tr><tr><td>[257]</td><td>2024</td><td>DFRC</td><td>Resource allocation to minimize the transmit power with constraints on the communication SINR and the sensing CRB</td><td>Meta-learning gradient descent - Lagrange multipliers - Manifold optimization</td></tr><tr><td>[258]</td><td>2024</td><td>DFRC</td><td>Resource allocation to minimize the squared position error bound subject to communication QoS constraints</td><td>Hybrid deep reinforcement learning - Markov decision process</td></tr><tr><td>[259]</td><td>2024</td><td>DFRC</td><td>Resource allocation to maximize the spectral efficiency of DFRC</td><td>Unsupervised learning - Image-shaped channel samples algorithm</td></tr><tr><td>[128]</td><td>2022</td><td>DFRC</td><td>Channel estimation for DFRC</td><td>Deep neural network</td></tr><tr><td>[129]</td><td>2023</td><td>DFRC</td><td>Channel estimation for DFRC</td><td>Convolutional neural network</td></tr><tr><td>[130]</td><td>2023</td><td>DFRC</td><td>Channel estimation for DFRC</td><td>Deep neural network - Extreme learning machine</td></tr><tr><td>[260]</td><td>2024</td><td>DFRC</td><td>Channel estimation and environment mapping for DFRC</td><td>Complex-valued depth residual convolutional neural network - Parallel factor tensor decomposition</td></tr><tr><td>[161]</td><td>2023</td><td>DFRC</td><td>PLS enhancement for STAR-RIS-assisted ISAC to prevent the unauthorized disclosure of legitimate user information to a potential eavesdropping sensing target</td><td>Deep reinforcement learning</td></tr><tr><td>[261]</td><td>2023</td><td>DFRC</td><td>PLS enhancement for RIS-assisted ISAC via incorporating artificial noise</td><td>Soft actor-critic based deep reinforcement learning - Entropy maximization for policy diversity</td></tr></tbody></table>

assisted ISAC, including both RIS-assisted RCC and RIS-assisted DFRC systems. A summary of the works presented in this section is provided in Table VII.

### A. Interference Management in RCC

In an attempt to reduce the complexity associated with solving complex optimization problems for resource allocation in RIS-assisted RCC, [254] investigated the application of deep reinforcement learning to address the resource allocation optimization problem. In their system model, a LoS link was presumed to be available between the radar and the target, and unavailable between the communication BS and the users. Consequently, the BS relied on the RIS to transmit the communication signals. The problem was formulated to maximize the mutual information for the communication system, subject to constraints on the radar signal power and the interference energy at the radar receiver.

Instead of relying on traditional optimization-based methods, the authors opted for a meta-reinforcement learning-based algorithm. In this approach, an agent was guided by a reward-based procedure with the objective of discovering the optimal policy to maximize the cumulative rewards. A Markov decision process was employed to integrate current and past actions and rewards into the policy, facilitating the attainment of an optimal solution.

Through simulations, the authors showcased that the proposed method can obtain the optimal solution six times faster than traditional AO-based solutions. The reinforcement learning based approach also surpassed other learning-based

methods, including the twin delayed deep deterministic and deep deterministic policy gradient, in terms of communication mutual information and radar detection probability.

**Lessons learned:** The application of machine learning for RIS-assisted RCC is still in at an early stage. Nevertheless, existing works have demonstrated the effectiveness of deep reinforcement learning in addressing complex optimization problems such as resource allocation in RIS-assisted ISAC systems. Meta-reinforcement learning, used in this context, enables the agent to learn how to learn optimal policies across different tasks or scenarios. This meta-learning capability is advantageous in complex environments where conditions and requirements may vary, allowing the system to adapt more effectively to changing conditions. This approach could pave the way for more adaptive and intelligent communication and radar coexistence systems, capable of optimizing performance dynamically in real-time scenarios.

### Challenges, opportunities and open research directions:

In reinforcement learning models, the agent relies on the environment to acquire the optimal policy, and the effectiveness of these systems is highly influenced by the Markov decision process utilized. Therefore, obtaining accurate system descriptions is essential. Additionally, the agent may make inaccurate decisions when confronted with situations it has not been trained on. Combining learning-based methods with theoretical ones can mitigate against this limitation, aiming for a tradeoff between system complexity and performance.
## B. DFRC Waveform Design

The optimal joint design of DFRC waveforms is challenging due to the absence of metrics that can quantify the optimal balance between communications and sensing. This has led researchers to employ machine learning tools to provide enhanced DFRC waveform designs.

The study in [255] 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 theoretical methods.

**Lessons learned:** Designing optimal joint waveforms for DFRC systems is complex due to the lack of clear metrics balancing communication and sensing. To address this, machine learning tools such as deep learning have been explored. Research in this area highlights the effectiveness of a decoupled online learning network for waveform optimization, showing that this method can significantly improve radar SINR and communication performance. The use of trainable parameters, loss calculation, and back-propagation ensures effective training, leading to enhanced outcomes over traditional methods.

**Challenges, opportunities and open research directions:** Challenges in joint design for DFRC systems include the difficulty in quantifying the optimal tradeoff between radar sensing and communication, as well as the complexity of waveform design in dynamic environments. However, the integration of machine learning, particularly deep learning, offers significant opportunities by enabling adaptive and data-driven waveform optimization. Open research directions include developing more robust and interpretable machine learning models, exploring real-time adaptive systems, and establishing standardized metrics to evaluate the balance between communication and sensing in DFRC systems.

## C. Resource Allocation

Resource is another key challenge in RIS-assisted DFRC systems due to the diverse system models and problem formulations as explained in Section IV-B. Since several traditional optimization algorithms suffer from high complexity and lack of ability to avoid converging to local solutions,

machine learning tools have been proposed in a number of research works as replacements.

A proximal policy optimization based beamforming design was proposed in [256] 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.

The work [257] targeted minimizing transmit power by jointly optimizing both active and passive beamforming at the BS and RIS, respectively, with constraints on the communication SINR and the sensing CRB. Due to the non-convexity of the optimization problem as well as coupling between the optimization variables and the high-dimensional optimization variables, solving the problem using the traditional optimization approaches is prohibitively complex. Drawing on meta-learning techniques, the authors introduced a meta-learning algorithm based on gradient descent to iteratively update the optimization variables and solve the non-convex optimization problem. Further, the constrained optimization problem was transformed into its dual form by employing Lagrange multipliers. Meta-learning neural networks were then used to adjust the optimization variables through local loss functions, with network parameters updated via global loss functions.

The authors in [258] introduced C-RAN to enable collaboration between multiple BSs to enhance the cooperation benefits for both communication and sensing functions. They formulated an optimization problem to minimize the squared position error bound, which reflected system performance by optimizing the transmit beamformer, RIS phase shifts, and subcarrier assignment. Additionally, to adjust the RIS phase shifts, the authors proposed an RIS-assisted cooperative ISAC protocol. This protocol used collected measurements to refine the agent's location and velocity estimates and reconstruct the environmental map with greater accuracy. The large feasible space posed computational challenges, making traditional gradient-based or exhaustive search methods impractical. To address this, a framework based on Markov decision processes was developed and a hybrid deep reinforcement learning algorithm was presented to efficiently navigate the problem.

To achieve a low-complexity and high-performance beamforming design, the authors of [259] leveraged image-shaped channel samples and introduced an ISAC beamforming neural network model to maximize the spectral efficiency of DFRC. By using unsupervised learning, the loss function integrated important performance metrics, such as communication and sensing channel correlation and sensing channel gain, without requiring labeled data. Simulation results demonstrated that the proposed method offers 25% improvement.
ment in the sensing SNR compared to the sub-space rotation theoretical algorithm of [248] with 75% reduction in the running time.

**Lessons learned:** Traditional optimization algorithms often struggle with high-dimensional and non-convex problems, making it difficult to find optimal solutions without getting stuck in local optima. Machine learning techniques have emerged as powerful alternatives. These methods reduce computational complexity and provide more efficient solutions to beamforming and resource allocation problems. For example, proximal policy optimization has been successfully applied to maximize communication capacity under ergodic constraints in the THz band, offering a promising solution where conventional methods fall short. Additionally, meta-learning has proven effective in tackling joint optimization challenges by iteratively updating optimization variables using gradient descent. This approach efficiently addresses the coupling between variables in complex, high-dimensional spaces. Furthermore, advanced techniques such as hybrid deep reinforcement learning are crucial for solving large-scale decision-making problems in RIS-assisted ISAC systems. In conjunction with C-RAN, hybrid deep reinforcement learning enhances system performance by enabling collaboration between multiple BSs for both communication and sensing purposes. The use of unsupervised learning in ISAC beamforming models has also demonstrated significant improvements in spectral efficiency and sensing accuracy while reducing the need for labeled data.

**Challenges, opportunities and open research directions:** Ensuring robust performance in varying environmental conditions, such as interference and fading, is a key challenge when employing machine learning for resource allocation in RIS-assisted DFRC systems. Furthermore, the integration of advanced machine learning techniques into these systems requires thorough understanding and refinement to ensure reliability and adaptability, particularly in real-world applications. Despite these challenges, one promising avenue is the exploration of hybrid machine learning approaches that combine reinforcement learning and deep learning for more efficient resource management and optimization strategies. There is also potential for further research into unsupervised learning methods that optimize system performance without the need for extensive labeled datasets. Exploring the design of adaptive algorithms that can dynamically respond to changing environments and user requirements will also be crucial.

## D. Channel Estimation

Channel estimation is another challenging task in ISAC due to the lack of capability of the RIS and the passive targets to send or process pilot signals.

The authors of [128]–[130] employed a channel estimation framework for RIS-assisted ISAC based on neural networks and extreme learning machines. While the complexity of solving the channel estimation problem by standard

theoretical approaches is prohibitively high, machine learning tools can significantly reduce the complexity of the estimation procedure. Two types of input-output pairs are proposed for the extreme learning machines: the first pair is generated using the original received DFRC signals, and the second pair is based on the least-squares 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.

An ISAC algorithm capable of performing simultaneous channel estimation, positioning, and environment mapping was introduced in [260]. The authors employed a complex-valued depth residual convolutional neural network assisted channel estimation algorithm to address the limited robustness of traditional algorithms. In addition to this, the authors exploited the sparsity of the mmWave channel through parallel factor tensor decomposition to extract factor matrices that contained crucial channel parameters, including complex path gain, direction-of-arrival, direction-of-departure, and time delay. Using these parameters, an environment mapping was achieved based on the geometric relationships between the position and channel parameters. Simulation results indicated that the proposed algorithm maintains strong ISAC performance even at low transmit SNRs.

**Lessons learned:** Employing machine learning techniques, such as neural networks and extreme learning machines, can substantially reduce the complexity associated with traditional estimation methods. The development of input-output pairs for extreme learning machines, including those derived from the original received DFRC signals and least-squares estimation results, enhances the estimation process. Moreover, data augmentation techniques have proven effective in enriching training samples, thereby improving estimation accuracy. Algorithms that leverage advanced models, like complex-valued depth residual convolutional neural networks and tensor decomposition, can effectively extract crucial channel parameters, facilitating simultaneous channel estimation, positioning, and environment mapping while maintaining robust performance, even at low transmit SNRs.

**Challenges, opportunities and open research directions:** Machine learning for channel estimation in ISAC systems faces several challenges. One significant issue is the limited availability of labeled data for training models, primarily due to the inability of RIS and passive targets to send or process pilot signals effectively. Additionally, ensuring model robustness against noise and varying channel conditions is critical, as machine learning models can be sensitive to these factors. Computational complexity remains a concern, as advanced algorithms may still require considerable computational resources for training and inference, which poses a problem for real-time applications where low latency is essential. There is also the risk of overfitting due to limited training data, which
can lead to poor generalization in new scenarios. Finally, integrating machine learning based techniques into existing ISAC frameworks presents compatibility challenges that need to be addressed for successful deployment.

Despite these challenges, there are numerous opportunities and open research directions in leveraging machine learning for channel estimation in an ISAC system. Hybrid approaches that combine machine learning with traditional estimation techniques can capitalize on the strengths of both methodologies to improve accuracy while maintaining efficiency. Furthermore, the adoption of unsupervised and semi-supervised learning methods could help mitigate the data scarcity issue by effectively utilizing unlabeled data. Advances in edge computing and distributed learning frameworks present promising opportunities for real-time processing of channel estimation algorithms. Research into adaptive learning models that dynamically adjust to changing conditions can enhance estimation accuracy, while cross-domain learning techniques could further improve model performance. Finally, establishing standardized evaluation metrics and encouraging collaboration between theoretical research and practical implementations will help bridge the gap between model development and real-world applications, ultimately refining machine learning approaches in ISAC systems.

### E. Physical Layer Security

The authors of [161] 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 [261] explored secure DFRC transmission via incorporating artificial noise, as discussed in Section VI-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.

**Lessons learned:** The complexity solving optimization problems concerning PLS while trying to minimize the compromise in the communication and sensing performance

underscores the need for innovative solutions. The application of deep reinforcement learning algorithms has emerged as an effective strategy to address this challenge by enabling adaptive optimization of critical parameters in dynamic environments. Additionally, techniques like soft actor-critic-based deep reinforcement learning, which promotes policy diversity through entropy maximization, can facilitate convergence to globally optimal solutions, demonstrating the significant potential of machine learning approaches to bolster PLS in RIS-assisted ISAC systems.

### Challenges, opportunities and open research directions:

One primary challenge is the complexity of the optimization problems involved, which often require balancing multiple objectives, such as maximizing secrecy rates while ensuring effective communication and sensing capabilities. The high dimensionality of the problem space, combined with the need for real-time processing, can make it difficult for traditional machine learning methods to converge to optimal solutions. Additionally, the quality of training data is crucial; however, in PLS scenarios, labeled datasets that accurately represent eavesdropping scenarios may be limited or difficult to generate. Furthermore, ensuring the robustness of machine learning models against adversarial attacks and changing environmental conditions remains a significant concern, as these factors can adversely affect the reliability of the security enhancements.

Despite these challenges, there are numerous opportunities and open research directions for leveraging machine learning in PLS for RIS-assisted ISAC systems. Research can focus on creating novel algorithms that incorporate uncertainty modeling and adversarial training to enhance the robustness of PLS mechanisms against various threats. Exploring unsupervised and semi-supervised learning techniques can also help address the limitations of labeled data by effectively utilizing available unlabeled data for training. Moreover, the integration of multi-agent systems could facilitate collaborative strategies among multiple RIS units to optimize PLS collectively.

## VIII. HOLOGRAPHIC ISAC

Holographic ISAC refers to the use of RHSs for transmission and/or reception in ISAC. While research 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 RCC and RHS-assisted DFRC, followed by a summary of the implementation efforts of RHS-assisted DFRC along with experimental results. We conclude the section by presenting current works on SIM-assisted ISAC. A summary of the paper discussed in this section is presented in Table VIII.

### A. Holographic Beamforming for RCC

In [262], the authors explored incorporating RHS into the communication transmitter of an RCC system to facilitate holographic beamforming. Specifically, the system model
<table><caption>Table VIII: References on holographic ISAC.</caption><thead><tr><td>Reference</td><td>Year</td><td>RCC or DFRC</td><td>Goal</td><td>Methods</td></tr></thead><tbody><tr><td>[262]</td><td>2024</td><td>RCC</td><td>Beamforming design to maximize the communication weighted sum bits per unit bandwidth subject to a constraint on the radar SINR</td><td>AO - Fractional programming</td></tr><tr><td>[263]</td><td>2022</td><td>DFRC</td><td>Beamforming design to minimize the beampattern mismatch error while setting a threshold on the communication QoS and prototyping</td><td>AO - ZF - SDR</td></tr><tr><td>[264]</td><td>2024</td><td>DFRC</td><td>Beamforming design to maximize the communication and radar SINRs with RHSs employed for both signal transmission and echo reception</td><td>AO - Rayleigh quotient-based method</td></tr><tr><td>[265]</td><td>2024</td><td>DFRC</td><td>Beamforming design in cell-free network to maximize the sensing SINR and system energy efficiency</td><td>Variational autoencoder based scheme - Transformer-based algorithm</td></tr><tr><td>[266]</td><td>2024</td><td>DFRC</td><td>Beamforming design for BD-RHS-assisted ISAC to maximize the sum rate for multiple communication users and minimize the largest eigenvalue of the CRB matrix for multiple targets</td><td>AO - Symmetric unitary projection - SCA</td></tr><tr><td>[267]</td><td>2024</td><td>DFRC</td><td>Beamforming design for RHS-assisted ISAC with RSMA transmission to maximize the radar permeance metrics for detection, tracking and localization targets while adhering to a communication sum rate constraint</td><td>AO - SCA - Difference-of-convex programming</td></tr><tr><td>[268]</td><td>2024</td><td>DFRC</td><td>Beamforming design for RHS-assisted ISAC with RSMA transmission to maximize the secrecy sum rate subject to sesning CRB constraint</td><td>AO - SCA - SOCP</td></tr><tr><td>[269]</td><td>2023</td><td>DFRC</td><td>Beamforming design for RIS-assisted holographic ISAC to maximize the radar SINR subject to communication SNR constraints</td><td>AO - Maximin procedure - difference of convex programming</td></tr><tr><td>[270]</td><td>2024</td><td>DFRC</td><td>Beamforming design for RIS-assisted holographic ISAC to maximize the minimum radar SINR subject to communication SNR constraints</td><td>AO - Maximin procedure - difference of convex programming</td></tr><tr><td>[271]</td><td>2024</td><td>DFRC</td><td>Beamforming design to minimize the CRB of the AoA while maintaining a threshold on the communication SINRs and prototyping</td><td>AO - SDR</td></tr></tbody></table>

consists of a time division multiplexing UL multi-user communication system enhanced by RHS backscatter, operating alongside radar sensing. In this RCC system, the radar transmits a probing signal to track a distant target, while multiple communication users attempt to capture the electromagnetic waves emitted by the radar. Each user employs an RHS to transmit its own data to the BS. The authors formulated an optimization problem aimed at maximizing the weighted sum spectral efficiency, subject to a radar QoS constraint. The solution to this problem is obtained by employing AO and fractional programming. The inclusion of RHSs on the user side showed significant improvement compared to methods like ZF, with the weighted sum spectral efficiency increasing from 7 bits/ssec/Hz to 35 bits/sec/Hz (a 5-fold increase) when RHSs consisting of 60 elements were used. This improvement was even greater (nearly 8-fold) when a larger RHS with 100 elements was employed.

**Lessons learned:** Incorporating RHS into communication transmitters can significantly enhance system performance, particularly in multi-user UL scenarios coexisting with radar sensing. The use of RHS for holographic beamforming allows users to effectively capture radar-emitted signals and transmit their own data to the BS via backscatter, showcasing the potential of RHS to optimize both communication and sensing tasks. The significant gains in the weighted sum spectral efficiency, particularly when using larger RHS arrays, highlight the scalability and effectiveness of RHS in improving

system throughput, especially when compared to traditional techniques like ZF.

**Challenges, opportunities and open research directions:** One major challenge lies in the complexity of optimizing joint radar and communication performance, particularly in scenarios where CSI is imperfect or unavailable, which may limit the effectiveness of beamforming and resource allocation strategies. Additionally, the scalability of RHS, especially as the number of users and the size of the RHS arrays increase, poses challenges in terms of computational complexity and system overhead. However, these challenges also open up opportunities for the development of more efficient optimization algorithms and machine learning-based solutions to manage the CSI and improve the adaptability of beamforming strategies. Future research could explore dynamic and real-time optimization approaches that account for mobility, varying radar targets, and environmental conditions. Moreover, there is potential to investigate hybrid active-passive systems that combine the strengths of both active antennas and RHS to further enhance performance in joint communication and sensing applications.

## B. Holographic Beamforming for DFRC

Holographic beamforming for DFRC has gained increasing attention lately due to its ability to generate more directed beams towards communication users and radar targets,
thereby enhancing ISAC performance. The research in this area can be categorized into five main categories.

1) Resource allocation: One of the early works on holographic beamforming for DFRC was presented in [263], where the authors 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 to allow holographic beamforming. As shown in Fig. 19 (a), two types of antennas/surfaces are employed: RHSs for signal transmission and a regular antenna array for receiving the targets' echo signals. 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.

Following the digital signal processing at the BS, the signals are sent to the RF chains which in turn modulate them and then 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, the communication users receive the ISAC signals broadcast by the RHS and decode their data streams. At the same time, the BS receives the echo signals reflected by the targets via the antenna array and processes them. In this process, different holographic patterns are produced, each corresponding to a single feed. The analog beamformer is then designed by superposing the output holographic patterns in a weighted manner, a technique known as holographic-pattern-division multiple access (HDMA) [272]. The beamformer is then optimized by choosing the weights of the holographic patterns to minimize the beampattern 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. This optimization problem was applied and tested with a real-world prototype, this generated promising results as will be discussed in Section VIII-C.

While [263] employed an antenna array for echo signal reception, a system model with both transmitting and receiving continuous-aperture RHSs was investigated in [264]. To facilitate a design with continuous pattern, the authors proposed a continuous-to-discrete transformation of the RHS pattern using the Fourier transform, translating the continuous beamforming design into a discrete one. The problem was then formulated by creating a joint objective function aiming

to balance the performance of multi-target sensing with the requirements of multi-user communication. To tackle the non-convex problem with coupled variables, the authors proposed an AO algorithm, where the transmit beamforming design is solved by breaking it down into a series of feasibility sub-problems, and the receive beamforming is handled using a Rayleigh quotient based method. The proposed RHS-based ISAC system model showed a significant improvement in the Pareto-optimal front of the sensing SINR versus the communication SINR when the authors compared RHSs containing 81 elements to discrete antenna arrays with the same aperture length and antenna spacing of half a wavelength. For instance, the authors demonstrated a 44% improvement in the sensing SINR when an RHS is utilized instead of a discrete antenna array at a communication SINR requirement of 0 dB. The improvement in the Pareto-optimal front becomes more pronounced at higher communication SINR requirements. For example, a 210% improvement in the sensing SINR is observed at a communication SINR requirement of 25 dB. This highlights the advantage of RHSs in improving the tradeoffs between communications and sensing, thereby enhancing ISAC performance.

2) Cell-free holographic DFRC: Holographic beamforming improves cell-free MIMO by generating highly directed and adaptive beams, enhancing the precision and efficiency of sensing and communication across a wide area. This leads to better signal quality, reduced interference, and more efficient resource utilization, ultimately improving overall system performance in terms of connectivity, energy efficiency, and user experience.

Building on this concept, the authors of [265] proposed a holographic ISAC framework for cell-free networks. Their method utilized an artificial intelligence based technique for beamforming design. An optimization problem was formulated to maximize the sensing SINR and system energy efficiency. The proposed algorithm decomposed the optimization problem into two sub-problems: a sensing sub-problem and a power allocation sub-problem. A variational autoencoder based scheme was employed to solve the sensing sub-problem, followed by a transformer-based mechanism to allocate power to users based on the sensing information. Simulation results demonstrated that this method outperformed other learning-based techniques, such as long short-term memory (LSTM) and gated recurrent unit algorithms, achieving energy efficiency improvements between 15% and 17%.

3) Beyond-diagonal RHSs (BD-RHSs): The study in [266] investigated the use of BD-RHS as part of the DFRC base station in the mmWave band. The deployment of BD-RHS at the BS adds additional design flexibility provided by the fully connected structure of the RHS matrix. The authors proposed an efficient two-stage algorithm for beamforming design at the transmitter, aiming to jointly maximize the sum rate for multiple communication users while minimizing the largest eigenvalue of the CRB matrix for multiple sensing
<figure><img src="image_21.png" /><figcaption>Figure 19: System models for holographic DFRC where (a) an RHS is employed as a transmitter and an antenna array is employed as a receiver and (b) an RHS is employed as a transmitter and another RHS is employed as a receiver.</figcaption></figure>

targets. Numerical results indicated that the transmitter-side BD-RIS-aided mmWave ISAC outperformed conventional diagonal-RHS-aided systems in both communication and sensing performance.

4) **RSMA:** The interplay between RSMA and RHS-assisted DFRC has been explored in several studies. In [267], the authors exploited RSMA to address interference management between communication and sensing in holographic ISAC. In the system model, three types of targets (which are also users) exist: communication/detection users, communication/tracking users, and communication/localization users. The authors introduced sensing QoS performance metrics for each of these user types and formulated an optimization problem with the sensing QoS criterion as the objective function while adhering to communication QoS constraints for all users. To tackle this challenging optimization problem, an AO procedure was utilized to divide the optimization problem into three sub-problems, one for each optimization variable. The SCA algorithm was applied to address the QoS communication constraints, and a difference-of-convex programming method was used to resolve the rank-one nonconvex constraints.

In [268], a time-division sensing-communication mechanism was designed for holographic DFRC. To address the issue of interference management and enhance system PLS, the authors implemented a RSMA transmission scheme, where the common stream was designed to include both a useful signal and an artificial noise. This approach also took into account imperfect CSI and modeled the eavesdropper's channel in a detailed manner, providing an upper bound on the CSI error.

The authors derived the secrecy outage probability and constructed an optimization problem with the secrecy sum rate as the objective function. The goal was to optimize

the common and private stream beamformers along with the timeslot duration allocated for communication and sensing. To tackle the optimization problem, an AO-based approach was proposed using a second-order cone programming (SOCP) algorithm. Furthermore, the S-procedure, Bernstein's inequality, and SCA methods were employed to address the objective function and non-convex constraints.

5) **RIS-assisted holographic DFRC:** Leveraging the advantages of both RHSs and RISs, the authors of [269] and [270] explored the use of both for DFRC. They considered a wide-band DFRC system with an RHS at the transmitter and an RIS reflector. The authors jointly designed the receive filter, along with the digital, holographic, and passive beamformers, to maximize the worst-case radar SINR while ensuring a communication SINR threshold across all users. To solve the resulting optimization problem, they developed an AO method involving CM and difference of convex constraints. Numerical results showed that adding an RIS to the RHS-assisted DFRC improved the radar SINR by 20% for low communication QoS requirements and by 300% for high ones.

**Lessons learned:** Holographic beamforming has shown significant potential in enhancing the performance of DFRC systems through more directed and adaptive beams. This beamforming technique allows simultaneous communication with multiple users and detection of radar targets, creating a more efficient use of resources compared to systems with conventional arrays. Recent research in the area can be categorized into five key topics. First, resource allocation for RHS-assisted DFRC with RHSs can be used for signal transmission only or employed for signal transmission and echo signal reception. Second, cell-free DFRC systems can benefit from holographic beamforming to mitigate interfer-
ence between different targets and users, which leads to better communication and sensing performance. Third, BD-RHSs can introduce additional design flexibility, leading to better performance in both communication and sensing by employing innovative beamforming algorithms. Fourth, the interplay between RSMA and holographic beamforming for DFRC has been investigated to manage interference between communication and sensing, with optimization frameworks enhancing both the system's power allocation and secrecy. Lastly, RIS-assisted holographic DFRC combines RHSs and RISs to maximize ISAC performance, showing substantial improvements in sensing and communication performance through joint beamforming design and optimization techniques. These five aspects collectively push the boundaries of ISAC system performance.

**Challenges, opportunities and open research directions:** Employing RHSs for DFRC systems presents several challenges. First, hardware complexity is a major issue, as integrating both communication and sensing functionalities into a single platform requires advanced beamforming components that are both cost-effective and scalable. Additionally, real-time beamforming optimization is challenging, as it requires highly precise control over the phase and amplitude of each surface element to ensure simultaneous communication and radar sensing performance. Interference management between communication and radar signals, particularly in dynamic environments, also remains a significant hurdle, as the two functions must coexist without degrading each other's performance.

Despite these challenges, RHSs present significant opportunities for enhancing DFRC systems. The ability to create highly adaptive and directed beams enables improved accuracy in both communication and radar operations, especially in environments where signal interference or multipath effects are common. RHSs also allow for fine-grained control over the beam shape, which can be leveraged for multi-target tracking and multi-user communication, offering significant improvements in system efficiency. Furthermore, the integration of machine learning algorithms with RHS-based DFRC systems opens up possibilities for real-time optimization of beamforming patterns, which could dramatically improve energy efficiency, signal quality, and user experience in next-generation networks.

Several open research directions exist for the future development of RHS-based DFRC systems. One key area is the design of efficient optimization algorithms that can handle the complex, non-convex beamforming problems associated with RHSs, such as joint communication and radar signal design. Another research direction is exploring robust beamforming solutions that can account for real-world uncertainties, such as CSI and RHS imperfections. Additionally, further work is needed to develop scalable and cost-effective hardware solutions, as well as explore the use of quantum computing or other advanced technologies to push the limits of beamforming accuracy and system performance.

### C. RHS-Assisted ISAC Prototype

A prototype of an RHS-Assisted ISAC system was presented by the authors of [12]. In particular, the authors designed a one-dimensional (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, which serves as an ISAC BS here 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 [273]. 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 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/sec, underscoring that communication between the BS and the user remains supported even as the BS conducts radar sensing concurrently.

### D. Holographic Beamforming via SIM

SIMs consist of multiple layers of RHSs; therefore, they are more capable of generating sharp holographic beams. Moreover, when used as an entity between the transmitter and the receiver, SIMs are better able to generate directed beams toward the targets and users, as shown in [271]. The authors of this work considered a system model consisting of a BS, an extended target, and multiple communication users. Holographic beamforming is performed using a SIM
<figure><img src="image_22.png" /><figcaption>Figure 20: SIM-assisted DFRC system model presented in [271].</figcaption></figure>

placed between the transmitter and the receiver, as illustrated in Fig. 20.

An optimization problem was formulated in [271] to minimize the CRB of the AoA estimation for extended target by jointly optimizing the communication and sensing beamforming matrices, as well as the end-to-end transmission matrix of the SIM. This optimization must be performed while ensuring the minimum SINR constraints for each of the communication users. To tackle this non-convex optimization problem, the authors proposed an AO-based approach that utilizes the SDR algorithm. Notably, the authors observed almost linear decrease in the CRB as the number of layers increases. For instance, a difference of 8 dB is observed between a SIM with one layer (i.e., a standard RIS) and a SIM with seven layers when the communication SINR threshold is set to 0 dB. Furthermore, SIMs with a higher number of layers are more resistant to performance degradation when the communication SINR requirement increases, compared to those with fewer layers. This is because more layers allow for greater system design flexibility through more degrees of freedom.

**Lessons learned:** Research works highlight the advantage of utilizing SIMs consisting of multiple layers to generate sharp holographic beams to improve ISAC performance. The findings indicated a direct correlation between the number of layers in the SIM and the CRB, with notable differences observed between a single-layer SIM and a seven-layer SIM. Additionally, SIMs with more layers demonstrated enhanced resilience to performance degradation under increasing communication SINR demands, attributed to their increased design flexibility and degrees of freedom.

**Challenges, opportunities and open research directions:**
One significant challenge of employing SIMs for ISAC is the complexity of designing and optimizing the multilayer structures, which requires advanced algorithms capable of handling non-convex optimization problems. Additionally,

channel estimation is more challenging in the case of SIMs due to the presence of multiple layers, necessitating the estimation of a multitude of channels. This can significantly increase overhead and degrade system performance. While this issue can be resolved to some extent by estimating average channels instead of instantaneous ones, it leads to the presence of imperfect CSI that needs to be accounted for in the beamforming design step.

Despite these challenges, there are ample opportunities and open research directions in this domain. For instance, the development of adaptive algorithms that dynamically optimize metasurface configurations in real time could enhance the efficiency and effectiveness of ISAC systems. Additionally, investigating machine learning techniques to predict and adapt to changing environments could revolutionize how ISAC systems operate, allowing for smarter resource allocation and user prioritization. Finally, interdisciplinary research that combines insights from communication theory, sensor networks, and materials science can drive fundamental innovations in stacked intelligent metasurfaces, fostering the development of next-generation ISAC applications in various fields, including autonomous driving and smart cities.

## E. RHS-Assisted ISAC Prototype

The authors of the work discussed in the previous subsection also developed a prototype for the system model shown in Fig. 20. In their experimental setup, the authors fabricated the SIM, in which each layer consists of a radiating layer, a receiving layer, and a ground plane. Moreover, a printed circuit board was utilized to represent the layout of a uniform planar array made up of 16 × 16 unit cells, with a total area of 961 cm². In the experimental setup, a universal software radio peripheral serves as the signal source, transmitting a signal at a frequency of 5.8 GHz with a power budget of 10 dBm. Since the system configuration utilizes a single antenna, no beamforming scheme is applied at the transmitter. Also, the transmission coefficient matrix for each scenario is obtained through an exhaustive search during communication or sensing experiments.

Three configurations are considered in the experiments: 1-layer, 2-layer, and 3-layer SIMs, along with five different coordinates for the transmitters. The measurements revealed an average error of the AoA estimation of 3.6° for a single-layer SIM (i.e., an RIS). This error is reduced to 1.5° (58% lower error) for a 2-layer SIM and 1.4° (61% lower error) for a 3-layer SIM.

## 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 started with a brief background on ISAC and metasurfaces. Then, it summarized existing research on RIS-assisted ISAC, where metasurfaces were employed as separate entities between the transmitters and receivers considering two
integration levels in ISAC: RCC and DFRC. Finally, the article summarized the state of the art in holographic ISAC, which encompasses the utilization of metasurfaces at both the transmitter and receiver sides. Within each category, we presented lessons learned as well as prominent opportunities, providing a holistic perspective on the evolving landscape of RIS-assisted ISAC.

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