Intelligent Reflecting Surface Research Articles

TDOA-AOA Localization Algorithm for 5G Intelligent Reflecting Surfaces

5G positioning technology has become deeply integrated into daily life. However, in wireless signal propagation environments, there may exist non-line-of-sight (NLOS) conditions, which lead to signal blockage and subsequently hinder the provision of positioning services. To address this issue, this paper proposes an intelligent reflecting surface (IRS) NLOS time difference of arrival–angle of arrival (TDOA-AOA) localization (INTAL) algorithm. First, the algorithm constructs a system model for 5G IRS localization, effectively overcoming the challenges of positioning in NLOS paths. Then, by applying the multiple signal classification algorithm to estimate the time delay and angle, and using the Chan algorithm to obtain the user’s estimated coordinates, an optimization problem is formulated to minimize the distance between the estimated and actual coordinates. The tent–snake optimization algorithm is employed to solve this optimization problem, thereby reducing localization errors. Finally, simulations demonstrate that the INTAL algorithm outperforms the snake optimization (SO) algorithm and the gray wolf optimization (GWO) algorithm under the same conditions, reducing the localization error by 56% and 60% on average, respectively. Additionally, when the signal-to-noise ratio is 30 dB, the localization error of the INTAL algorithm is only 0.2968 m, while the errors for the SO and GWO algorithms are 0.6733 m and 0.7398 m, respectively. This further proves the significant improvement of the algorithm in terms of localization accuracy.

A Review on Intelligent Reflecting Surfaces: Challenges and Opportunities Towards Secured Communications

ABSTRACTThis review examines the advanced development and implementation of intelligent reflecting surfaces (IRSs) in smart radio environments (SREs) to enhance secure communications. IRS technology, including intelligent walls and software‐controlled metasurfaces, is designed to manipulate electromagnetic waves, thereby significantly improving wireless communication. The study highlights the importance of dynamic, nearly passive IRS, which combine multiple elements to create surfaces capable of diverse wave manipulations. Cutting‐edge techniques such as nonorthogonal multiple access (NOMA) and machine learning are employed to optimize IRS functions, enhancing secrecy rates and resilience against eavesdropping. Despite their ease of reconfiguration and affordability, IRSs are vulnerable to several security threats, a concern that has not been extensively explored in current research. This review aims to fill this gap by offering a comprehensive analysis of the security challenges associated with IRS‐enabled wireless networks. The effectiveness of IRS is compared to traditional wireless relays, with special attention to the simultaneous transmitting and reflecting intelligent reflecting surface (STAR‐IRS) model that provides 360° coverage. Key findings suggest that IRS can greatly enhance secure communications, improving wireless network performance, reliability, and adaptability. This paper underscores the pivotal role of IRS technology in the future of wireless networks, presenting new opportunities for secure and efficient communication systems.

Optimum Beamforming and Grating-Lobe Mitigation for Intelligent Reflecting Surfaces

Optimum Beamforming and Grating-Lobe Mitigation for Intelligent Reflecting Surfaces

Overview of Tensor-Based Cooperative MIMO Communication Systems—Part 2: Semi-Blind Receivers

Cooperative MIMO communication systems play an important role in the development of future sixth-generation (6G) wireless systems incorporating new technologies such as massive MIMO relay systems, dual-polarized antenna arrays, millimeter-wave communications, and, more recently, communications assisted using intelligent reflecting surfaces (IRSs), and unmanned aerial vehicles (UAVs). In a companion paper, we provided an overview of cooperative communication systems from a tensor modeling perspective. The objective of the present paper is to provide a comprehensive tutorial on semi-blind receivers for MIMO one-way two-hop relay systems, allowing the joint estimation of transmitted symbols and individual communication channels with only a few pilot symbols. After a reminder of some tensor prerequisites, we present an overview of tensor models, with a detailed, unified, and original description of two classes of tensor decomposition frequently used in the design of relay systems, namely nested CPD/PARAFAC and nested Tucker decomposition (TD). Some new variants of nested models are introduced. Uniqueness and identifiability conditions, depending on the algorithm used to estimate the parameters of these models, are established. Two families of algorithms are presented: iterative algorithms based on alternating least squares (ALS) and closed-form solutions using Khatri–Rao and Kronecker factorization methods, which consist of SVD-based rank-one matrix or tensor approximations. In a second part of the paper, the overview of cooperative communication systems is completed before presenting several two-hop relay systems using different codings and configurations in terms of relaying protocol (AF/DF) and channel modeling. The aim of this presentation is firstly to show how these choices lead to different nested tensor models for the signals received at destination. Then, by capitalizing on these models and their correspondence with the generic models studied in the first part, we derive semi-blind receivers to jointly estimate the transmitted symbols and the individual communication channels for each relay system considered. In a third part, extensive Monte Carlo simulation results are presented to compare the performance of relay systems and associated semi-blind receivers in terms of the symbol error rate (SER) and channel estimate normalized mean-square error (NMSE). Their computation time is also compared. Finally, some perspectives are drawn for future research work.

Advanced Beamforming and Multi‐Access Edge Computing: Empowering Ultra‐Reliable and Low‐Latency Applications in 6G Networks

ABSTRACT6G technology is expected to transform communications by allowing the Internet of Everything, representing a huge advance in 2030. While B5G has not yet been established, various nations are actively working on 5G, but certain research groups are presently devoting their attention to the creation of 6G technologies. The upcoming 6G networks promise higher quality of service (QoS) features, including virtual reality and holographic communications. Multi‐Access Edge Computing (MEC) and, in particular, the offloading idea are key components of 6G innovation that enable resource‐intensive application design. If the wireless link used for computational offloading is inefficient, MEC's true potential can be impeded. Intelligent beamforming and MEC have garnered attention recently. Systematically optimizing the wireless communication environment, these developments improve connectivity between user equipment (UE) and base station (BS). By increasing the electrical charge of the reflectable signal, intelligent beamforming increases the range and efficacy of Back Communication. Particularly, this study assesses how well the MEC structure performs in urban microcellular settings when intelligent beamforming is used in communications. It has been demonstrated that the use of Intelligent Reflecting Surfaces (IRS) greatly reduces spectrum and energy usage. This study's results are implemented in Python software. Our suggested strategy shows better latency compared to other optimization methods and shorter task completion times than traditional methods. When compared to conventional techniques, the suggested MEC‐enabled network showcasing lower latency (4.9 ms) and efficient network congestion management and task completion time (30 ms) demonstrates a significant performance. These results highlight how intelligent beamforming and MEC have a lot of potential to shape the architecture of 6G networks in the future.

4D-printed intelligent reflecting surface with improved beam resolution via both phase modulation and space modulation

Recently, intelligent reflecting surfaces (IRSs) have emerged as potential candidates for overcoming the line-of-sight issue in 5 G/6 G wireless communication. These IRSs can manipulate the direction of reflected beams, enabling efficient beam steering to enhance the performance of wireless communication. Each unit cell (or unit structure) of an IRS commonly consists of electrical elements for phase modulation. However, by employing phase modulation alone, an IRS can steer the reflected electromagnetic waves toward only discrete and specific angles, leaving a wide range of out-of-beam areas. In this work, an IRS that uses both phase modulation and space modulation is presented to improve the beam resolution and continuously cover out-of-beam areas that phase modulation alone cannot address. A positive-intrinsic-negative diode is mounted on a unit cell for phase modulation, and a 4D-printed reconfigured structure is fabricated to demonstrate space modulation. The beam-steering function is achieved by alternating the states of the diodes in the same columns, while the beam resolution is improved by controlling the gaps between the columns. The functions are first theoretically and numerically analyzed and then experimentally verified, demonstrating that additional angles of −46°/+50°, −22°/+14°, and −16°/+12° are achieved with space modulation and −60°/+62°, −30°/+22°, and ±16° are achieved by phase modulation alone. The proposed IRS offers the possibility of functional integration in a variety of indoor applications within the wireless communication field.

Non-orthogonal unicast and multicast transmission with IRS-assisted Rate-Splitting Multiple Access in multiuser MISO systems

Abstract. In MISO systems, novel communication technologies such as NOMA and multicast transmission have attracted widespread attention to meet the diverse needs of users. However, traditional NOMA and multicast transmission schemes have certain limitations in dynamic interference management and resource allocation. To resolve this issue, this paper proposes a RSMA scheme assisted by phase quantized Intelligent Reflecting Surface (IRS) for downlink MISO systems with nonorthogonal transmission of unicast and multicast signals. In this scheme, through reconfiguring unicast and multicast signals and combining them into a super-common stream and partial unicast messages, RSMA achieves dynamic interference management of multi-user while effectively separating unicast and multicast streams. To maximize the weighted sum rate(WSR), we put forward an alternating optimization(AO) algorithm that utilizes Weighted Minimum Mean Square Error (WMMSE) estimation and convex set intersection representation, which jointly optimizes the equivalent beamforming matrix,rate allocation and phase shift matrix. Simulation results illustrate that the proposed single-layer RSMA exhibits performance closer to that of DPC encoding compared to traditional NOMA and MULP schemes. Furthermore, we extensively investigate the relationship between the WSR of RSMA and system parameters, providing important guidance for practical engineering applications.

Joint passive beamforming and deployment design for active intelligent reflecting surface-assisted energy harvesting-cognitive radio sensor networks.

To prolong the network lifetime of energy harvesting-cognitive radio sensor networks (EH-CRSNs), this paper integrates active intelligent reflecting surface (IRS) to support both downlink EH and uplink data transmissions and formulates a constrained non-convex optimization problem that maximizes the net energy gain. To solve this problem, a joint passive beamforming and IRS deployment mechanism is proposed to determine the optimal deployment location of the active IRS and optimally configure the IRS reflection coefficient matrices. Specifically, the net energy gain maximization problem at a specified location is divided into two sub-problems according to its characteristics: maximizing the cumulative energy harvested by all CRSNs nodes in the downlink and minimizing the energy consumption of cluster heads in uplink data transmissions, with each sub-problem being solved independently. Furthermore, this paper explores how factors such as the number of active reflecting elements and the amplification power budget influence the optimal deployment location of the active IRS. The findings from this investigation can guide the design of active IRS-assisted EH-CRSNs under specified parameters. Simulation results confirm the aforementioned conclusions and highlight the benefits of the proposed joint mechanism in enhancing net energy gain over various benchmark mechanisms.

Rate Optimization of Intelligent Reflecting Surface-Assisted Coal Mine Wireless Communication Systems.

This paper proposes a three-step joint rate optimization method for intelligent reflecting surface (IRS)-assisted coal mine wireless communication systems. Different from terrestrial IRS-assisted communication scenarios, in coal mines, IRSs can be installed flexibly on the tops of rectangular tunnels to address the issues of signals being blocked and interfered with by mining equipment. Therefore, it is necessary to optimize the IRS deployment position, the transmit power and IRS phase shifts to achieve the maximum effective achievable rate at user stations equipped with the proposed system. However, due to the complex channel models of coal mines, the optimization problem of IRS deployment position is non-convex. To solve this problem, two auxiliary variables along with logarithmic operations and Taylor approximation are introduced. On this basis, a three-step joint rate optimization involving the transmit power, IRS phase shifts and IRS deployment position is proposed to maximize the effective achievable rates at the user station. The simulation results show that compared with other rate optimization schemes, the effective achievable rates at the user station using the proposed joint rate optimization scheme can be improved by approximately 12.32% to 54.17% for different parameter configurations. It is also pointed out that the deployment position of the IRS can converge to the same optimal position independent of the initial deployment position. Moreover, we investigate the effects of the roughness of the tunnel walls in a coal mine on the effective achievable rates at the user station, and the simulation results indicate that the proposed three-step joint rate optimization scheme performs better in the coal mine scenario regardless of the roughness.

IRS assisted anti jamming and beamforming technique

Recently, the emerging intelligent reflecting surfaces (IRS) has been leveraged to establish a smart and controllable wireless environment. This paper addresses the challenge of maximizing spectral efficiency in an IRS-aided downlink multi-user system while facing jammer attacks. The optimization problem focuses on improving the available transmission rate and limiting the impact of malicious jammer signals. The efficiency of the IRS in anti-jamming communications is achieved by determining the optimal phase shifts of the IRS elements and the optimal beamforming at the base station (BS). The optimization problem is non-convex. To tackle this non-convexity, an alternating optimization algorithm is proposed, dividing the main problem into two sub-problems which are solved using the semi-definite relaxation (SDR) and Gaussian randomization techniques. The simulation results confirm the enhanced spectral efficiency achieved by the proposed anti-jamming method in an IRS-assisted system. The optimal adjustment of the phase shifts in the IRS elements has resulted in a significant enhancement of about 25% in spectral efficiency compared to the random IRS phase case.

Coverage Probability Analysis of a Double IRS Assisted SISO Communication System

Intelligent reflecting surface (IRS) improves wireless communication efficiency and coverage probability. Existing studies focus on IRS deployment but overlook optimal fading channels. This research aims to identify and analyze the most effective channel fading estimation for the double IRS system model. The coverage probability using two-IRS is derived under different fading channels like Rayleigh and Rician to find the optimal solution in a Single-Input-Single-Output (SISO) system. Additionally, a closed-form expression for the coverage probability using statistical channel state information is derived while assuming no direct path between the transmitter and receiver. The analytical results are validated through simulations. It offers valuable insights into the modeling and analysis of double IRS-assisted wireless communication systems.

IRS aided visible light positioning with a single LED transmitter

We propose a visible light positioning (VLP) system with a single light emitting diode (LED) transmitter and an intelligent reflecting surface (IRS) for estimating the position of a receiver equipped with a single photo-detector. By performing a number of transmissions from the LED transmitter and optimizing the orientation vectors of the IRS elements for each transmission, position information is extracted by the receiver based on power measurements of the signals reflecting from the IRS. The theoretical limit and the maximum likelihood (ML) estimator are presented for the proposed setting. In addition, an algorithm, named IRS focusing, is proposed for determining the orientations of the IRS elements during the localization process. The effectiveness of the proposed localization approach is demonstrated through simulations. Furthermore, extensions are provided to apply the proposed approach in the presence of partial prior information about the receiver position and when the IRS is located at the LED transmitter.

On mitigating reactive jamming with dynamic resource allocation in optical IRS and UAV-assisted FSO-based networks

Free space optics (FSO) offers a promising opportunity to enhance next-generation network’s capacity with its unlicensed spectrum and wide bandwidth. However, jamming attacks, coupled with inherent anomalies in the FSO-based channel, threaten the performance of these networks. This is especially problematic for security-sensitive applications that demand a resilient communication infrastructure. To address this issue, optical intelligent reflecting surfaces (IRS) and unmanned aerial vehicles (UAVs) can provide promising solutions. This work introduces an efficient approach for mirror element assignment in UAV-assisted FSO-based networks, aimed at mitigating reactive jamming attacks while satisfying users’ quality-of-service (QoS) requirements. To ensure network reliability, we formulate an optimization problem that enhances overall network performance by simultaneously allocating resources such as mirror elements with awareness of jamming attacks. The formulated optimization problem is a binary linear programming problem, which is generally NP-hard. To address this, we introduce a batch-based sequential fixing linear programming procedure called the Reactive Jamming-Aware Mirror Element Allocation (RJA-MEA) scheme. This scheme optimally assigns mirror elements to satisfy the users’ rate demands. In this paper, the performance of the RJA-MEA scheme is compared with reference schemes such as Reactive Jamming Unaware-Mirror Element Allocation (RJU-MEA), Reactive Jamming-Aware Equal Mirror Element Allocation (RJA-EMEA), and Reactive Jamming Unaware-Equal Mirror Element Allocation (RJU-EMEA) schemes. The simulation results reveal that the proposed RJA-MEA scheme surpasses existing reference schemes, thereby significantly improving the overall network sumrate performance.

UAV-mounted IRS assisted wireless powered mobile edge computing systems: Joint beamforming design, resource allocation and position optimization

Intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have been recently used in wireless-powered mobile edge computing (MEC) systems to enhance the computation bits and energy harvesting performance. However, in the conventional IRS- and UAV-aided MEC systems, the IRS is installed at fixed locations on a building, which restricts the computation performance. UAV-mounted IRS (UAV-IRS), as a promising technology, combines the advantages of UAV and IRS. Hence, in this work, we study a UAV-IRS wireless-powered MEC system, where multiple UAV-IRSs are considered between Internet of Things (IoT) devices and the base station to improve the computation bits and energy harvesting. The multi-antenna base station first charges the IoT devices via radio frequency signals, and then IoT devices offload their computation tasks to the base station via UAV-IRSs. We formulate a computation bits maximization problem for all IoT devices by jointly determining detection beamforming at IoT devices, active energy beamforming at the base station, power allocation, time slot assignment, CPU frequency, the phase shifts design in the wireless energy transfer (WET) and task offloading, and UAV-IRSs positions. A block coordinate descent (BCD) algorithm by decomposing the introduced problem into four blocks is proposed, while the detection beamforming, active energy beamforming, transmit power, time slot assignment, CPU frequency, and the phase shifts design in the task offloading are derived in closed-form results. Also, the successive convex approximation and semidefinite relaxation (SDR) are adopted to obtain the UAV-IRS positions and the phase shifts in the WET, respectively. The simulation results verify the effectiveness of the presented BCD method compared with the different benchmark schemes.

Energy Efficiency Maximization for Multi-UAV-IRS-Assisted Marine Vehicle Systems

Mobile edge computing is envisioned as a prospective technology for supporting time-sensitive and computation-intensive applications in marine vehicle systems. However, the offloading performance is highly impacted by the poor wireless channel. Recently, an Unmanned Aerial Vehicle (UAV) equipped with an Intelligent Reflecting Surface (IRS), i.e., UIRS, has drawn attention due to its capability to control wireless signals so as to improve the data rate. In this paper, we consider a multi-UIRS-assisted marine vehicle system where UIRSs are deployed to assist in the computation offloading of Unmanned Surface Vehicles (USVs). To improve energy efficiency, the optimization problem of the association relationships, computation resources of USVs, multi-UIRS phase shifts, and multi-UIRS trajectories is formulated. To solve the mixed-integer nonlinear programming problem, we decompose it into two layers and propose an integrated convex optimization and deep reinforcement learning algorithm to attain the near-optimal solution. Specifically, the inner layer solves the discrete variables by using the convex optimization based on Dinkelbach and relaxation methods, and the outer layer optimizes the continuous variables based on the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3). The numerical results demonstrate that the proposed algorithm can effectively improve the energy efficiency of the multi-UIRS-assisted marine vehicle system in comparison with the benchmarks.

Survey on Unmanned Aerial Vehicle Communications for 6G

Although the application of the fifth-Generation (5G) mobile communication has brought tremendous innovations to the daily life of human beings, e.g., autonomous vehicles and internet of everything, the upcoming huger data requirement leads to the emergence of the sixth-Generation (6G) mobile communication. Compared to 5G, the transmission rate, time delay, and wireless coverage need to be improved significantly. Thus, in this paper the applications of Unmanned Aerial Vehicles (UAVs) to the ubiquitous, intelligent and coupling 6G network are surveyed. First, the utilization of UAVs in the framework of space-air-ground-sea integrated network is demonstrated, and the roles and functions of UAVs in different scenarios are emphasized, e.g., the swarm base stations, the deployment for holographic projection, the long-distance relaying and the data collection. Then, the potential 6G key techniques of terahertz, ultra-massive multiple-input and multiple-output, endogenous artificial intelligence, Intelligent Reflecting Surface (IRS), intelligent edge computing, blockchain and integrated sensing and communication for UAV communications are investigated. Finally, the future challenges of UAV communications for 6G, including the limited duration, integration of networks, compatibility of IRS, development of THz communications, and user security are discussed.

Intelligent Reflecting Surface-Assisted Wireless Communication Using RNNs: Comprehensive Insights

By adjusting the propagation environment using reconfigurable reflecting elements, intelligent reflecting surfaces (IRSs) have become potential techniques used to improve the efficiency of wireless communication networks. In IRS-assisted communication systems, accurate channel estimation is crucial for optimizing signal transmission and achieving high spectral efficiency. As mobile data traffic continues to surge and the demand for high-capacity and low-latency wireless connectivity grows, IRSs are becoming pivotal technologies in the development of next-generation communication networks. IRSs offer the potential to revolutionize wireless propagation environments, improving network capacity and coverage, particularly in high-frequency wave scenarios where traditional signals encounter obstacles. Amidst this evolving landscape, machine learning (ML) emerges as a powerful tool to harness the full potential of IRS-assisted communication systems, particularly given the escalating computational complexity associated with deploying and operating IRSs in dynamic environments. This paper presents an overview of preliminary results for IRS-assisted communication using recurrent neural networks (RNNs). We first implement single- and double-layer LSTM, BiLSTM, and GRU techniques for an IRS-based communication system. In the next phase, we explore a hybrid approach, combining different RNN techniques, including LSTM-BiLSTM, LSTM-GRU, and BiLSTM-GRU, as well as their reverse configurations. These RNN algorithms were evaluated with respect to bit error rate (BER) and symbol error rate (SER) for IRS-enhanced communication. According to the experimental results, the BiLSTM double-layer model and the BiLSTM-GRU combination demonstrated the highest BER and SER accuracy compared to other approaches.

Bridging the Gap to 6G: Leveraging the Synergy of Standardization and Adaptability

The field of wireless network and communication technology is evolving from generation to generation from 1G to 6G as of now till expected to be deployed and used by 2030. It is to succeed in 5G and bring significant improvements in terms of connectivity, speed, and size in next-generation communication technology. 6G aims to deal with the rising need for more rapid information speed, low latency, and wider network coverage. This intelligent communication is proposed to meet these demands and enable new services and applications. This review paper highlights the key enablers and challenges involved in implementing intelligent communication beyond 5G. The paper identifies the research gaps for incorporating beyond 5G communication networks and outlines the possible 6G key objectives from a flexibility standpoint. It reviews infrastructure deployment, network densification, spectrum capacity and network energy efficiency in predecessors to 6G. This paper emphasizes the need for standardization and adaptation of research areas to revolutionize 6G wireless communication, focusing on areas like, ultra massive MIMO, Terahertz Communications, Cell-Free Communications, Intelligent Reflecting Surface, Visible Light Communication, Internet of Things, Big Data management, Artificial Intelligence, and network connectivity techniques.

Performance Enhancement for B5G/6G Networks Based on Space Time Coding Schemes Assisted by Intelligent Reflecting Surfaces with Higher Modulation Orders.

Intelligent Reflecting Surfaces (IRS) and Multiple-Input Single-Output (MISO) technologies are essential in the fifth generation (5G) networks and beyond. IRS optimizes the signal propagation and the coverage and is a viable approach to address the issues caused by fading channels that limits the spectral efficiency, while MIMO enhances data rates, reliability, and spectral efficiency by using multiple antennas at both transmitter and receiver ends. This paper proposes an IRS-assisted MISO system using the Orthogonal Space-Time Block Code (OSTBC) scheme to enhance the channel reliability and reduce the Bit Error Rate (BER) in wireless communication systems. The proposed system exploits the benefits from the transmit diversity gain of the OSTBC scheme as well as from the bit energy to noise power spectral density (Eb/No) improvement of the IRS technology. The presented work explores these combined technologies across different modulation schemes. The obtained results outperform the similar previously published works by considering higher-order modulation schemes as well as the deployment of rate ¾ OSTBC-assisted IRS. Moreover, the obtained results demonstrate that the integration of OSTBC with IRS can yield significant performance improvements in terms of Eb/No by 7 dB and 13 dB when using 16 reflecting elements and 64 reflecting elements, respectively.

Deep Learning Aided Intelligent Reflective Surfaces for 6G: A Survey

The envisioned sixth-generation (6G) networks anticipate robust support for diverse applications, including massive machine-type communications, ultra-reliable low-latency communications, and enhanced mobile broadband. Intelligent Reflecting Surfaces (IRS) have emerged as a key technology capable of intelligently reconfiguring wireless propagation environments, thereby enhancing overall network performance. Traditional optimization techniques face limitations in meeting the stringent performance requirements of 6G networks due to the intricate and dynamic nature of the wireless environment. Consequently, Deep Learning (DL) techniques are employed within the IRS framework to optimize wireless system performance. This paper provides a comprehensive survey of the latest research in DL-aided IRS models, covering optimal beamforming, resource allocation control, channel estimation and prediction, signal detection, and system deployment. The focus is on presenting promising solutions within the constraints of different hardware configurations. The survey explores challenges, opportunities, and open research issues in DL-aided IRS, considering emerging technologies such as digital twins (DTs), computer vision (CV), blockchain, network function virtualization (NFC), integrated sensing and communication (ISAC), software-defined networking (SDN), mobile edge computing (MEC), unmanned aerial vehicles (UAVs), and non-orthogonal multiple access (NOMA). Practical design issues associated with these enabling technologies are also discussed, providing valuable insights into the current state and future directions of this evolving field.