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Digital metasurfaces based on space‐time coding have established themselves as a powerful and versatile platform for joint spatial/spectral control of electromagnetic waves. However, their advanced design remains a largely open problem with significant computational challenges. This study introduces a novel approach, based on deep neural networks, to address this challenge. The proposed technique enables the simultaneous and independent multi‐frequency synthesis of scattering patterns, allowing precise tailoring of the harmonic equivalent currents (both in magnitude and phase), and enhancing spectral efficiency. These results, experimentally validated at X‐band microwave frequencies, substantially broaden the capabilities of space‐time coding digital metasurfaces, paving the way for advanced applications in wireless communications, sensing, and imaging. [ABSTRACT FROM AUTHOR]

Summary The performance of millimeter‐wave (mmWave) multiple‐input multiple‐output (MIMO) systems has been significantly enhanced by the incorporation of dynamic reconfigurable intelligent surfaces (RIS). This paper proposes a novel dynamic channel estimation technique that combines dynamic atomic norm minimization with dynamic RIS to optimize RIS‐aided mmWave MIMO systems. Leveraging the dynamic nature of both atomic norm minimization and RIS, the proposed approach efficiently adapts to changing environmental conditions, providing robust and accurate channel estimation. By dynamically optimizing the RIS configuration, the system achieves improved spectral and energy efficiency, enabling high‐speed and reliable communication in challenging mmWave environments. Theoretical analysis and simulation results demonstrate the effectiveness of the proposed dynamic channel estimation technique, highlighting its potential for enhancing the performance of future wireless communication systems. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surfaces (RISs), as one of the potential key technologies in sixth generation (6G) mobile communications, feature low costs, low energy consumption, and ease of deployment. In this paper, we introduce the RIS technology into the maritime wireless communication scenario, which can transform the wireless transmission environment from uncontrollable to controllable. In the considered communication model, we derive the complex channel impulse response for the RIS propagation link and non-line-of-sight propagation link, respectively. This can be used to capture the physical properties of a communication model from different perspectives. Furthermore, based on the designed communication model, we investigate the correlation properties of propagation links in the space and time domains; they are the space correlation statistics and time correlation statistics. The provided framework addresses the technical bottleneck of the existing RIS channel modeling methods that fail to balance precision and efficiency, improves the channel model matching efficiency in the design process of RIS-enabled near-field maritime communication systems, and provides technical support for the rapid development of the 6G mobile communication industry. [ABSTRACT FROM AUTHOR]

The rapid evolution of wireless communication technologies necessitates innovative solutions to meet the increasing performance requirements of future networks, particularly in terms of spectral efficiency, energy efficiency, and computational efficiency. Reconfigurable Intelligent Surfaces (RIS) and NonOrthogonal Multiple Access (NOMA) are emerging as promising technologies to enhance wireless communication systems. This paper explores the dynamic partitioning of RIS elements in NOMA systems using Deep Reinforcement Learning (DRL) to optimize resource allocation and overall system performance. We propose a novel DRL-based framework that dynamically adjusts the partitioning of RIS elements to maximize the achievable sum rate and ensure fair resource distribution among users. Our architecture leverages the flexibility of RIS to create an intelligent radio environment, while NOMA enhances spectral efficiency. The DRL model is trained online, adapting to real-time changes in the communication environment. Empirical results demonstrate that our approach closely approximates the performance of the optimal iterative algorithm (exhaustive search) while reducing computational time by up to 90 percent. Furthermore, our method eliminates the need for an offline training phase, providing a significant advantage in dynamic environments by removing the requirement for retraining with every environmental change. These findings highlight the potential of DRL-based dynamic partitioning as a viable solution for optimizing RIS-aided NOMA systems in future wireless networks. [ABSTRACT FROM AUTHOR]

Reconfigurable Intelligent Surfaces (RIS) are gaining significant global interest as a potential technology that could enable 6G. An efficient method employs passive element reflectors to extend coverage of the non-line of sight (NLOS) millimeter-wave (mm-wave) signal. The suggested model depicts a communication situation involving many users utilizing the RIS in the presence of impediments obstructing the direct path between the users and the transmitter. There is no unobstructed visual connection between sending the message and the person receiving it. The RIS enables communication using an oblique line of vision. Inter-user interference (IUI) refers to the interference that occurs when numerous users are active in the same region, which affects the received signal of a single user. Multiple remote inspection systems (RISs) of different heights have been suggested as potential solutions for addressing this issue. The results illustrate the impact of interference among users in a communication system supported by a reconfigurable intelligent surface (RIS) at various elevations in a millimeter-wave environment. Furthermore, the findings demonstrate that this idea effectively reduced the impact of the IUI. The Signal-to-Interference-plus-Noise Ratio (SINR) was enhanced for all users. The improvement percentages for the first, second, and third users are 46%, 40%, and 38%, respectively.

Recently, as data demand has increased owing to the rapidly increasing demand for wireless devices and the influence of data traffic, various technologies are being developed to support it. Among them, millimeter-wave (mmWave) frequencies with rich spectra and high data-transmission rates suffer from the problem of large path loss. Accordingly, there is a growing interest in unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs), which can be utilized advantageously to reconstruct wireless communication environments. Therefore, this work considers a large-scale system comprising a number of users and Flying RISs, combining UAVs and RISs to increase algorithm utilization. We propose a deep neural network-based algorithm that places Flying RISs in an appropriate location so that they can support as many users as possible. Simulation results confirmed that the proposed technique could place Flying RISs in an efficient location with higher accuracy and speed in large-scale systems compared to existing techniques.

Recently, great attention has been given to the idea of a smart environment. It often involves the use of reconfigurable intelligent surfaces (RIS) for the management of electromagnetic wave reflections as the world awaits the emergence of 6G. Changeable intelligent surfaces may enhance the creation of wireless communication. The design and analysis of several unit cell reflections are presented in this work. The first design relies on the Switching Technique which involves switching on and off to acquire the phase as well as the coefficient of reflection to accommodate 6G standards. The unit cells design is configured to operate in the millimeter band and X band. In the second design, the radius of the circular patch was changed to adjustment of the phase and reflection coefficient. The use of Floquet technique is employed in investigating the scattering characteristics of a unit cell's constituent elements based on the assumption that every element consists of an extremely iterating periodic structure. To determine the optimal force reflection and the transformation phase, the return loss alongside reflection phase graphs of each resonant component were examined. The simulation results indicate that the first design exhibits a reflection phase shift range of -180 to 90 and a reflection magnitude over 0.93 at a frequency of 11GHz. In contrast, the second design demonstrates a reflection phase shift range of -135 to 135 and a reflection magnitude surpassing 0.9 at a frequency of 28GHz. The analysis and simulation of the design models were carried out using the CST model. [ABSTRACT FROM AUTHOR]

The research on high-precision and all-scenario localization using the millimeter-wave (mmWave) band is of great urgency. Due to the characteristics of mmWave, blockages make the localization task more complex. This paper proposes a cooperative localization system among user equipment (UEs) assisted by reconfigurable intelligent surfaces (RISs), which considers device-to-device (D2D) communication. RISs are used as anchor points, and position estimation is achieved through signal exchanges between UEs. Firstly, we establish a localization model based on this system and derive the UEs' positioning error bound (PEB) as a performance metric. Then, a UE-RIS joint beamforming design is proposed to optimize channel state information (CSI) with the objective of achieving the minimum PEB. Finally, simulation analysis demonstrates the advantages of the proposed scheme over RIS-assisted base station positioning, achieving centimeter-level accuracy with a 10 dBm lower transmission power. [ABSTRACT FROM AUTHOR]

In this article, we analyze the downlink transmission of a wirelessly powered hybrid cooperative non-orthogonal multiple access (C-NOMA) system relying on reconfigurable intelligent surfaces (RIS), where self-interference related to full-duplex (FD) and power efficiency are also considered. The network contains a single-antenna base station (BS), a RIS with many reflecting elements, a power beacon, and two users that can function in FD in comparison with the benchmark, i.e. half-duplex mode. In the proposed system, there are two communication paths from the BS to the far-user either through the RIS or via a near-user employing energy-harvesting from a power beacon. To comprehend the performance of the proposed system, we study and compare the outage probability under various parameters of interest such as power allocation coefficients, energy harvesting coefficients, number of RIS reflecting elements, and transmission rates. We aim to show the trade-off between these main parameters and system outage performance. Moreover, to highlight the advantages of the wireless-powered RIS-aided CNOMA system, we compare it against with benchmark, i.e. a wireless-powered RIS-aided cooperative orthogonal multiple access (C-OMA) system. All derived closed-form outage expressions are verified by employing Monte-Carlo simulations. Numerical results show that: (1) wireless powered RIS-aided FD C-NOMA is superior to the wireless-powered RIS-aided FD C-OMA in terms of outage probability in the high signal-to-noise ratio (SNR) region; and (2) in delay-to-tolerant transmission scenarios, the energy efficiency of the proposed wireless powered RIS FD C-NOMA outperforms the wireless powered RIS FD C-OMA in the high SNR region. [ABSTRACT FROM AUTHOR]

Charging stations and electric vehicle (EV) charging networks signify a significant advancement in technology as a frontier application of the Social Internet of Things (SIoT), presenting both challenges and opportunities for current 6G wireless networks. One primary challenge in this integration is limited wireless network resources, particularly when serving a large number of users within distributed EV charging networks in the SIoT. Factors such as congestion during EV travel, varying EV user preferences, and uncertainties in decision-making regarding charging station resources significantly impact system operation and network resource allocation. To address these challenges, this paper develops a novel framework harnessing the potential of emerging technologies, specifically reconfigurable intelligent surfaces (RISs) and causal-structure-enhanced asynchronous advantage actor–critic (A3C) reinforcement learning techniques. This framework aims to optimize resource allocation, thereby enhancing communication support within EV charging networks. Through the integration of RIS technology, which enables control over electromagnetic waves, and the application of causal reinforcement learning algorithms, the framework dynamically adjusts resource allocation strategies to accommodate evolving conditions in EV charging networks. An essential aspect of this framework is its ability to simultaneously meet real-world social requirements, such as ensuring efficient utilization of network resources. Numerical simulation results validate the effectiveness and adaptability of this approach in improving wireless network efficiency and enhancing user experience within the SIoT context. Through these simulations, it becomes evident that the developed framework offers promising solutions to the challenges posed by integrating the SIoT with EV charging networks. [ABSTRACT FROM AUTHOR]

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles (IoVs). However, it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment. In order to protect data privacy and improve data learning efficiency in knowledge sharing, we propose an asynchronous federated broad learning (FBL) framework that integrates broad learning (BL) into federated learning (FL). In FBL, we design a broad fully connected model (BFCM) as a local model for training client data. To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients, we construct a joint resource allocation and reconfigurable intelligent surface (RIS) configuration optimization framework for FBL. The problem is decoupled into two convex subproblems. Aiming to improve the resource scheduling efficiency in FBL, a double Davidon–Fletcher–Powell (DDFP) algorithm is presented to solve the time slot allocation and RIS configuration problem. Based on the results of resource scheduling, we design a reward-allocation algorithm based on federated incentive learning (FIL) in FBL to compensate clients for their costs. The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency, accuracy, and cost for knowledge sharing in the IoV.

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The sixth generation (6G) wireless network is envisioned to provide faster and more reliable communication by utilizing electromagnetic waves in the terahertz spectrum. However, terahertz waves suffer from high propagation loss and, hence, wavefront manipulation solutions to focus and redirect terahertz waves are of paramount importance. Reconfigurable intelligent surface (RIS) is one such promising dynamic wavefront engineering solution proposed for 6G networks, but the technological realization of 6G (>100 GHz) RIS is still largely lacking. Here, spatially‐selective vanadium dioxide (VO2) patches incorporating coding metasurfaces for dynamic beam steering of terahertz waves are presented. The reversible and abrupt insulator‐to‐metal phase transition property of VO2 is exploited to achieve a reconfigurable transmission angle for the normal incident terahertz wave. By designing metasurfaces with specific linear phase gradients and designer phase sequencing, electrically switchable beam steering is achieved from co‐polarized 0° (normal transmission) in the OFF state to cross‐polarized 0° (control), 11°, 23°, 36°, and 52° in the ON state at 0.6 THz. Scaling up the proposed concept to achieve individual control of each active element (VO2) within the metasurface will enable a complete 6G RIS solution for full 3D terahertz beam manipulation to support the future THz wireless communication networks. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) are innovative technologies that aim to enhance the quality of communication in next-generation networks. These technologies are individually tailored to specific applications and have demonstrated impressive results. UAVs are known for their flexibility and ease of deployment, while RIS can adapt to changing environments by altering the traditional propagation and reflection schemes. Recent research has combined these two technologies for various applications, improving communication and data transmission in next-generation networks. This paper surveys recent advancements in using tethered and untethered UAVs and RIS in 5G/6G networks. The main objective of this survey is to inspire further solutions that support RIS-assisted UAVs and to provide insights not covered in published UAV–RIS surveys. The survey summarizes how UAVs and RIS can be used to address communication issues. Two case studies are presented to further illustrate this integration's potential benefits. The first case study demonstrates a UAV mounting an RIS to mitigate the impact of base station fronthaul failure. The results suggest that co-locating tethered UAVs with a selected number of base stations can proactively restore any failed fronthaul links. The second case study explores the 3D trajectory of the UAV and the phase shift design of the RIS based on the number of RIS elements and height placement of the UAV. The study considers an untethered UAV with a terrestrial RIS and shows that the propulsion energy of the UAV can be reduced by changing and adjusting the height of the UAV. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surfaces (RISs) are considered a key enabler of highly energy-efficient 6G and beyond networks. This property arises from the absence of power amplifiers in the structure, in contrast to active nodes, such as small cells and relays. However, a certain amount of power is still required for RIS operation. To improve their energy efficiency further, we propose the notion of perpetual RISs, which secure the power needed to supply their functionalities through wireless energy harvesting (EH) of impinging transmitted electromagnetic (EM) signals. Toward this, we initially explain the rationale behind such RIS capability and proceed with a presentation of the main RIS controller architecture that can realize this vision under an in-band EH consideration. Furthermore, we present a typical EH architecture, followed by two harvesting protocols. Subsequently, we study the performance of the two protocols under a typical communications scenario. Finally, we elaborate on the main research challenges governing the realization of large-scale networks with perpetual RISs. [ABSTRACT FROM AUTHOR]

The latest discussions on upcoming 6G wireless communications are envisioning future networks as a unified communications, sensing, and computing platform. The recently conceived concept of the smart radio environment, enabled by reconfigurable intelligent surfaces (RISs), contributes toward this vision, offering programmable propagation of information-bearing signals. Typical RIS implementations include metasurfaces with almost passive unit elements capable of reflecting their incident waves in controllable ways. However, this solely reflective operation induces significant challenges for RIS optimization from the wireless network orchestrator. For example, RISs lack information to locally tune their reflection pattern, which can be acquired only by other network entities and then shared with the RIS controller. Furthermore, channel estimation, which is essential for coherent RIS-empowered communications, is challenging with the available RIS designs. This article reviews the emerging concept of hybrid reflecting and sensing RISs (HRISs), which enables metasurfaces to reflect the impinging signal in a controllable manner while simultaneously sensing a portion of it. The sensing capability of HRISs facilitates various network management functionalities, including channel parameter estimation and localization, while giving rise to potentially computationally autonomous and self-configuring metasurfaces. We discuss a hardware design for HRISs and detail a full-wave electromagnetic (EM) proof of concept. The distinctive properties of HRISs, in comparison to their solely reflective counterparts, are highlighted, and a simulation study evaluating HRISs’ capability for performing full and parametric channel estimation is presented. Future research challenges and opportunities arising from the HRIS concept are also included. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS)-aided near-field communications are investigated. First, the necessity of investigating RIS-aided near-field communications and the advantages brought about by the unique spherical wave-based near-field propagation are discussed. Then, the family of patch array-based RISs and metasurface-based RISs is introduced along with respective near-field channel models. A pair of fundamental performance limits of RIS-aided near-field communications, namely, their power scaling law and effective degrees of freedom (EDOF), are analyzed for both patch array-based and metasurface-based RISs, which reveals the potential performance gains that can be achieved. Furthermore, associated near-field beam training and beamforming design issues are studied, where a two-stage hierarchical beam training approach and a low-complexity element-wise beamforming design are proposed for RIS-aided near-field communications. Finally, a suite of open research problems is highlighted for motivating future research. [ABSTRACT FROM AUTHOR]

Recently, reconfigurable intelligent surfaces (RISs) have attracted extensive attentions from the communication community due to their powerful wavefront manipulation and signal modulation abilities. Optically transparent RISs are needed in some scenarios requiring both optical transparency and high‐quality communication services. In RIS‐based wireless communications, the channel reciprocity is an important factor to be considered since the electromagnetic responses of RISs usually depend on the incident and receiving angles. To address this problem, an optically transparent 2‐bit RIS with low angular sensitivity is proposed. By applying a transparent dielectric substrate and metal‐mesh‐based patterns, the designed RIS achieves a light transmittance of 49.3%. Both the simulation and measurement results demonstrate the low angular sensitivity at transverse magnetic incidence from 0° to 60°. Moreover, beam steering experiments under various coding sequences and various incident angles are simulated and measured, and the results are consistent with the theoretically predicted results. A further study on the broadcast mode of the RIS shows that its phase response is weakly dependent to the incident and receiving directions. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surfaces (RIS) offer the potential to customize the radio propagation environment for wireless networks, and will be a key element for 6G communications. However, due to the unique constraints in these systems, the optimization problems associated to RIS configuration are challenging to solve. This paper illustrates a new approach to the RIS configuration problem, based on the use of artificial intelligence (AI) and deep learning (DL) algorithms. Concretely, a custom convolutional neural network (CNN) intended for edge computing is presented, and implementations on different representative edge devices are compared, including the use of commercial AI-oriented devices and a field-programmable gate array (FPGA) platform. This FPGA option provides the best performance, with × 20 performance increase over the closest FP32, GPU-accelerated option, and almost × 3 performance advantage when compared with the INT8-quantized, TPU-accelerated implementation. More noticeably, this is achieved even when high-level synthesis (HLS) tools are used and no custom accelerators are developed. At the same time, the inherent reconfigurability of FPGAs opens a new field for their use as enabler hardware in RIS applications. [ABSTRACT FROM AUTHOR]

Recently, great attention has been given to the idea of a smart environment. It often involves the use of reconfigurable intelligent surfaces (RIS) for the management of electromagnetic wave reflections as the world awaits the emergence of 6G. Changeable intelligent surfaces may enhance the creation of wireless communication. The design and analysis of several unit cell reflections are presented in this work. The first design relies on the Switching Technique which involves switching on and off to acquire the phase as well as the coefficient of reflection to accommodate 6G standards. The unit cells design is configured to operate in the millimeter band and X band. In the second design, the radius of the circular patch was changed to adjustment of the phase and reflection coefficient. The use of Floquet technique is employed in investigating the scattering characteristics of a unit cell's constituent elements based on the assumption that every element consists of an extremely iterating periodic structure. To determine the optimal force reflection and the transformation phase, the return loss alongside reflection phase graphs of each resonant component were examined. The simulation results indicate that the first design exhibits a reflection phase shift range of -180 to 90 and a reflection magnitude over 0.93 at a frequency of 11GHz. In contrast, the second design demonstrates a reflection phase shift range of -135 to 135 and a reflection magnitude surpassing 0.9 at a frequency of 28GHz. The analysis and simulation of the design models were carried out using the CST model.

Satellite communication is crucial for 6 G industrial wireless networks due to its comprehensive communication services, but traditional methods face challenges such as high latency, limited bandwidth, high cost, and integration with terrestrial networks. This highlights the need for innovative communication methods to boost satellite communication efficiency, reliability, and coverage within the context of 6 G industrial applications, which is attempted to address by this research. RISs (Reconfigurable Intelligent Surfaces) are thin sheets with tiny antennas that are programmed to manipulate radio signals. The RIS has the potential to revolutionize industrial wireless communication by providing greater dynamic and goal-oriented control over radio signals. From there, the wireless environment is being transformed into a service. Besides, the NOMA (Non-Orthogonal Multiple Access) mechanism is a highly effective radio access method in advanced wireless communications that optimizes spectrum utilization and offers improved spectrum efficiency, lower latency, more reliability, and increased connectivity. The current industrial communication network faces limitations such as limited capacity, signal interference overload, complex and expensive infrastructure, and latency issues. The fusion of RIS and NOMA is seen as promising strategy to meet the stringent demands of next-generation networks for immense connectivity. It is particularly innovative for the demanding environment of smart factories by enabling efficient resource sharing, dynamic signal control, ultra-reliable, low-latency communication, cost-effective network, overcoming the signal interference and simplifying the network infrastructure. In this article, we propose a novel approach for device-to-device satellite communication in industrial networks. This approach leverages a RIS (Reconfigurable Intelligent Surface) -assisted cooperative network with NOMA (Non-Orthogonal Multiple Access) and NL-PDDRL (Nonlinear Partial Differential Deep Reinforced Learning) termed AONPDDR (Alternating Optimized and Nonlinear Partial Differential Deep Reinforced) communication. This article focuses on important aspects related to industrial communication networks such as power consumption, energy efficiency, data delivery rate, data loss rate, throughput, latency, coverage, false alarm rate, and bit error rate. The Alternating Optimization-based RIS configuration algorithm optimizes reflecting elements in RIS by maximizing channel vectors and providing a sophisticated upper bound on the highest number of reflective elements. Next, The NL-PDDRL algorithm is designed to function effectively despite environmental uncertainties and constraints. The results indicate that the proposed AONPDDR method outperforms previous benchmark methods in system-achievable aspects.

The emerging fields of 5G and 6G telecommunication networks, Internet of Things, and artificial intelligence have intensified the demand for green nanostructured materials with adjustable and intelligent features that respond to external stimuli. By leveraging the insulator-to-metal transition of VO2 nanoparticles, responsive composite films were developed by integrating these nanoparticles within a biopolymeric network of cationic cellulose nanofibers (CNF+). These films exhibit a reversible change in GHz permittivity upon exposure to thermal or optical stimuli, facilitating dynamic control of their electrical properties. The layered structure of the films further enhances their robustness, featuring a VO2 nanoparticle core encased within CNF+ layers. This design not only strengthens the structure but also significantly boosts light-induced conductivity, particularly in layered variant, underscoring its potential in optoelectronic applications. Simulation studies reveal that the nonuniform, reconfigurable intelligent surface (RIS) of the developed mixed film adeptly manipulates incident electromagnetic waves, making it suitable for 5G/6G wireless signals. Conversely, the layered film serves as a switchable electromagnetic interference (EMI) shield, demonstrating notable differences in shielding efficiency between its hot and cold states. Consequently, CNF+/VO2 composite films designed in this work emerge as a versatile, adaptable platform for intelligent electronics, particularly in the realm of 5G/6G wireless communications.

The concept of Mobile Edge Computing (MEC) has been recently highlighted as a key enabling technology for the deployment of sixth-generation (6G) wireless network services. On the other hand, the possibility of combining Unmanned Aerial Vehicles (UAV) with Reconfigurable Intelligent Surfaces (RIS) has also been recognized as a powerful communication paradigm able to provide improved propagation characteristics of wireless communication channels, as well as increased capacity and extended coverage. Then, the possibility of merging the characteristics of such a communication paradigm with the one provided through MEC represents a valid solution to fulfill the main requirements of 6G networks. In this paper, we consider the combination of computation offloading and resource allocation in an MEC-based system where the MEC server is hosted by a massive MIMO base station, which serves multiple macro-cells assisted by a UAV-equipped RIS. In this context, we focus on minimising the latency for executing tasks of all user equipment (UE) within the considered scenario. To tackle this problem, we formulate an optimisation problem that jointly optimises computation offloading from user equipment (UE) towards the MEC server, and communication resources in the underlying UAV-assisted and RIS-aided network. The extensive simulation results demonstrate how the proposed method outperforms in terms of providing reduced latency for the considered system when compared with other conventional schemes.

The research on Reconfigurable Intelligent Surfaces (RISs) has dominantly been focused on physical-layer aspects and analyses of the achievable adaptation of the wireless propagation environment. Compared to that, questions related to system-level integration of RISs have received less attention. We address this research gap by analyzing the control signaling operations needed to integrate the RIS as a new wireless infrastructure element. As the main contribution of the paper, we build a systematic procedure for evaluating the impact of control operations on communication performance along two dimensions: i) the rate selection for the data channel (multiplexing or diversity), and ii) the allocated bandwidth of the control channels (in-band and out-of-band). Specifically, the first dimension results in two generic transmission paradigms: one focuses on optimizing RIS setting according to the propagation environment, labeled as optimization based on channel estimation (OPT-CE); the other is based on sweeping through predefined RIS phase configurations, labeled as codebook-based beam sweeping (CB-BSW). We analyze the communication performance in multiple setups built along these two dimensions. While necessarily simplified, our analysis reveals the basic trade-offs in RIS-assisted communications and the associated control operations: CB-BSW is better suited for high mobility scenarios since its operation is not conditional on performing channel estimation within the coherence time; OPT-CE performs significantly better when the channel coherence time is sufficiently long, but it requires exchanging more control data, necessitating higher control reliability and profiting more from out-of-band control channel design. Our systematic procedure can be easily adapted to include more complex systems and transmission modes.

Vehicle-to-infrastructure (V2I) communication facilitates seamless interaction between vehicles and the surrounding infrastructure. Recently, there has been a notable surge in research interest in ambient backscatter communications (AmBC), primarily due to its ability to enable battery-free communication. Concurrently, reconfigurable intelligent surfaces (RISs) have garnered attention as a promising technology, particularly for the advancement of cellular systems beyond 5G. Additionally, non-orthogonal multiple access (NOMA) assumes a pivotal role in enhancing spectrum utilization. This paper proposes a new RIS-enhanced NOMA-AmBC system where all channels are characterized by Nakagami-m fading and with the main objective of assessing the performance of such system. To this end, new closed-form expressions for the outage probabilities (OPs) were derived under the practical assumption of imperfect successive interference cancellation (SIC). In addition, to gain a deep insight into the system’s performance, we derived asymptotic, upper-bound, and lower-bound expressions for the OPs. Furthermore, we proposed a power allocation optimization problem to achieve an outage-optimal performance. To validate the analytical results, we conducted extensive investigations using Monte Carlo simulations, which indicates a high degree of consistency. Moreover, our results underscore the remarkable performance improvements achieved by the RIS-assisted AmBC-NOMA system when compared to both the traditional benchmark AmBC-NOMA system and the RIS-assisted orthogonal multiple access (AmBCOMA) counterparts.

Employing Reconfigurable Intelligent Surface (RIS) is an advanced strategy to enhance the efficiency of wireless communication systems. However, the number and positions of the RISs elements are still challenging and require a smart optimization framework. This paper aims to optimize the number of RISs subject to the technical limitations of the average achievable data rate with consideration of the practical overlapping between the associated multi-RISs in wireless communication systems. In this regard, the Differential evolution optimizer (DEO) algorithm is created to minimize the number of RIS devices to be installed. Accordingly, the number, positions, and phase shift matrix coefficients of RISs are then jointly optimized using the intended DEO. Also, it is contrasted to several recent algorithms, including Particle swarm optimization (PSO), Gradient-based optimizer (GBO), Growth optimizer (GO), and Seahorse optimization (SHO). The outcomes from the simulation demonstrate the high efficiency of the proposed DEO and GO in obtaining a 100% feasibility rate for finding the minimum number of RISs under different threshold values of the achievable rates. PSO scores a comparable result of 99.09%, while SHO and GBO attain poor rates of 66.36% and 53.94%, respectively. Nevertheless, the excellence of the created DEO becomes evident through having the lowest average number of RISs when compared to the other algorithms. Numerically, the DEO drives improvements by 5.13%, 15.68%, 30.58%, and 51.01% compared to GO, PSO, SHO and GBO, respectively.

In this paper, we address the critical aspect of hardware ageing effects in reconfigurable intelligent surfaces (RISs), whereby the problem of extending the RIS’s life cycle under the impact of non-residual stochastic hardware impairment is considered. Through the replication of the RIS hardware and diverse wireless environments within a statistical simulation environment, we formulate an electronic maintenance framework (EMF) for RIS, where we incorporate non-residual impairments through stochastic modeling and determine the electronic reliability of the system. Accordingly, we derive optimal analytical solutions within the EMF to determine whether systematic maintenance of the RIS hardware should be done immediately or postponed in order to extend the expected life cycle of the RIS system. Furthermore, for the scenario with imperfect maintenance of the RIS-aided system, a reliable communication framework (RCF) is also introduced with residual impairments to assess the error probability of the RIS-aided communication. The RCF is established by deriving the distribution of the received signal-to-interference-plus-noise ratio in the presence of residual hardware impairment arising due to imperfect maintenance of the RIS system. Extensive numerical examples are provided to elucidate the derived solutions and illustrate the reliability performance of the proposed framework under hardware impairments.

Recently, reconfigurable intelligent surfaces (RISs) aided millimeter wave (mmWave) and terahertz (THz) communication systems have attracted interest as enablers for 5G and beyond networks. Although the static blockers’ impact can be eliminated by the optimal deployment of RISs, the effect of dynamic and self-blockage can only be reduced. First, due to mobile blockers and the user equipment (UE) itself, the RIS-UEs links can be blocked as well as the base station (BS)-UEs links. Second, in dense environments, all links to the UE can be blocked simultaneously causing a low received power. Furthermore, the dynamic blockers frequent interruptions and duration of blockages have a vital impact on the UE quality of service (QoS). Consequently, to tackle these points, in this paper, we derive an analytical dynamic blockage model to deduce various blockage metrics, i.e., blockage probability, frequency, and duration, in RISs aided systems considering all BSs-UE and RISs-UE links are not exposed to static or selfblocking. Moreover, we discuss the minimum required BSs/RISs density to guarantee UE QoS demands, e.g., link reliability and end-to-end latency, for beyond 5G network applications. Furthermore, we study the effects of the network specifications, e.g., BSs and RISs heights, communication range, and blockers density, on the required BSs/RISs density. Additionally, based on simulation analysis, we investigate the network performance, considering the blockage, in terms of spectral efficiency and energy efficiency. Our results prove that the RISs aided networks can be designed to satisfy the UE QoS requirements for serving different applications.

With the commercialization of the fifth generation mobile networks, researchers are now focusing on discovering the potential key techniques of the next generation mobile networks, which are expected to provide more accurate perception, lower latency, and higher network capacity. As the communication equipments increase exponentially, wireless transmission environment becomes more complex, leveraging wireless security challenges more and more severe, such as computationally powerful interception, intelligent malicious jamming, communication behavior monitoring. The physical layer security (PLS) techniques have been widely explored as a complement to traditional encryption schemes by exploiting the randomness characteristics of wireless channels to achieve the security from the physical layer. Additionally, reconfigurable intelligent surface (RIS) is considered as a key enabling technology for the six generation (6G) mobile networks, due to its ability to achieve the reconstruction of wireless channel, which is also regarded as a good match with PLS techniques for improving communication security. This paper presents a comprehensive review of the latest research on the integration of RIS and PLS. First, we introduce the principles of PLS from the development of secure communications and the basics of RIS based on the generalized Snell’s law. Then, we categorize RIS according to different hardware architectures, of which the corresponding scenarios are also presented. Subsequently, we review recent works on RIS-assisted PLS in different communication networks, and classify the security scenarios in which RIS is integrated with various advanced communication technologies. Finally, we discuss the potential future research directions and challenges of RIS-aided PLS communications.

The use of visible light communication (VLC) to support wireless coverage has been receiving increasing interest, due to factors that include its operation in a large unregulated spectrum and immunity against electromagnetic interference. On the other hand, the sensitivity of VLC network performance to the line-of-sight (LOS) link quality affects the system reliability. The emergence of reconfigurable intelligent surfaces (RISs) in optical domain opens the door for minimizing the impacts of the LOS link degradation on VLC networks, given the possibilities they provide for enhancing the non-LOS channel gain. In this paper, we propose optimizing the RIS-assisted VLC systems by controlling the RIS elements to maximize the network sum-rate while maintaining fairness in the user achievable throughput. Two types of RIS elements are considered, where the addressed non-convex optimization problems are handled using the genetic algorithm and the particle swarm optimization techniques, presenting improvement in network performance in terms of sum rate, fairness, and link outage ratio. In addition, we investigate the RIS-assisted VLC system performance in cases of handling mobile users, highlighting the trade-off between the variation in the optimization rate and the network performance.

This paper aims to explore reconfigurable intelligent surface (RIS) integration in a millimeter wave (mmWave) communication system with low-complexity transceiver architecture under imperfect channel state information (CSI) assumption. Motivated by this, we propose a RIS-aided system with a fully analog architecture at the base station (BS). However, to overcome the drawback of single-user transmission due to the single RF chain in the analog architecture, we propose to employ NOMA to enable multi-user transmission. For such a system, we formulate two problems to obtain the joint transmit beamformer, RIS phase shift matrix, and power allocation solutions that maximize sum rate and energy efficiency such that the minimum rate for each user is satisfied. However, both problems are intractable due to: 1) fractional objective; 2) non-convex minimum rate and unit modulus RIS phase shift constraints; and 3) the coupled optimization variables. Hence, we first tackle the fractional objectives of both problems by reformulating the sum rate and energy efficiency maximization problems into equivalent quadratic forms using the quadratic transform. On the other hand, we employ successive convex approximation and the semi-definite relaxation technique to handle the non-convex minimum rate and unit modulus constraint of the RIS phase shifts, respectively. However, the problems remain non-convex due to the coupled optimization variables. Thus, we propose an alternating optimization-based algorithm that iterates over the transmit beamformer, power allocation, and RIS phase shift subproblems. Further, we also show that the quadratic reformulation is equivalent to the weighted mean square error-based reformulation for the case of the sum rate maximization problem. Our numerical results show that the proposed RIS-NOMA integrated analog architecture system outperforms the optimally configured fully digital architecture in terms of sum rate at low signal-to-noise ratio (SNR) and energy efficiency for a wide range of SNR while still maintaining low hardware complexity and cost. Finally, we present the numerical performance analysis of the RIS-NOMA integrated low-complexity system for various system configuration parameters.

To have uninterrupted wireless connectivity, higher throughput, and latency rate down to nanoseconds; future networks will rely heavily on higher frequency bands, yet these signals suffer from attenuation and can be easily blocked by objects. Hence, those signals lose strength over longer distances significantly compared to their low-frequency counterparts. To overcome these constraints, reconfigurable intelligent surfaces (RIS) can increase the wireless network capacity and coverage by consciously altering the wireless propagation environment, which makes them one of the key enablers of the 6G communication networks. The potential benefits of RIS-assisted communication systems can be effectively increased using machine learning (ML) to pave the path towards Wireless 2.0, having a vision of digitization, programmability, and intelligence. This paper reviews recent developments in RIS technology with a special emphasis on tuning techniques, hardware concepts, RIS empowered by artificial intelligence techniques, and applications for next generation of networks. Finally, a viewpoint on the difficulties and potential directions of intelligent metasurfaces are also presented.

While reconfigurable intelligent surface (RIS) technology has been shown to provide numerous benefits to wireless systems, in the hands of an adversary such technology can also be used to disrupt communication links. This paper describes and analyzes an RIS-based attack on multi-antenna wireless systems that operate in time-division duplex mode under the assumption of channel reciprocity. In particular, we show how an RIS with a non-diagonal (ND) phase shift matrix (referred to here as an ND-RIS) can be deployed to maliciously break the channel reciprocity and hence degrade the downlink network performance. Such an attack is entirely passive and difficult to detect and counteract. We provide a theoretical analysis of the degradation in the ergodic sum rate that results when an arbitrary malicious ND-RIS is deployed and design an approach based on the genetic algorithm for optimizing the ND structure under partial knowledge of the available channel state information. Our simulation results validate the analysis and demonstrate that an ND-RIS channel reciprocity attack can dramatically reduce the downlink throughput.

This paper introduces an innovative deep learning-based approach for User Equipment positioning in massive MIMO systems, enhanced by Flying Reconfigurable Intelligent Surfaces. The study emphasizes the transformative impact of Massive MIMO technology, which substantially increases the capacity and efficiency of wireless networks through extensive antenna arrays. We present a knowledge distillation model where a comprehensive teacher model is trained on massive MIMO data to predict User Equipment positions, which in turn guides a more compact student model. This results in a system that not only retains high accuracy with a lower mean absolute error of 3.67m but also benefits from reduced complexity and resource requirements. Our findings reveal that the distilled student model achieves a significant reduction in computational load while maintaining precise positioning capabilities, showcasing the potential for practical deployment in future wireless communication systems.

The Integrated Sensing and Communication (ISAC) system merged with Reconfigurable Intelligent Surface (RIS) has recently received much attention. This paper proposes an intelligent metaheuristic version of Enhanced Artificial Ecosystem Optimizer (EAEO) for a suggested beamforming optimization framework in ISAC systems with RIS. Two RIS are utilized in the presented model to enhance the received signal-to-noise ratio (SNR) for multiple-input multiple-output (MIMO) communication systems. Also, each element of each RIS scatters the incoming signal with a controllable phase-shift, without increasing its power. The signal is transmitted by a Dual-Function Base Station (DFBS) that is integrated with the RIS, which performs both communication and sensing functions simultaneously. The ISAC system is designed to optimize the signal transmission through the RIS by maximizing the SNR to increase the overall performance. In the proposed EAEO version, a Fitness-Distance-Balance Model (FDBM) is combined with the standard Artificial Ecosystem Optimizer (AEO) to improve the quality of the solutions in multidimensional and nonlinear optimization scenarios. The simulation results show that the proposed EAEO algorithm improves the SNR of different users for different numbers of RIS elements. The SNR reaches 25 dB when using 200 RIS elements. Moreover, the proposed EAEO is tested on the IEEE Congress on Evolutionary Computation 2017 (IEEE CEC’17) test suite. A comparative analysis is conducted compared to the standard AEO and several recent algorithms. The proposed EAEO derives great effectiveness and robustness over the others as it provides a higher rate of success in achieving the global optimum point.

This paper focuses on optimal beamforming to maximize the mean signal-to-noise ratio (SNR) for a passive reconfigurable intelligent surface (RIS)-aided multiple-input single-output (MISO) downlink system. We consider a realistic setting where both the direct and indirect (through RIS) links to the user equipment (UE) experience correlated Rician fading. Such a general fading model is particularly important to capture the impact of line-of-sight (LoS) and correlated multipath fading that may occur due to the compact placement of a large number of RIS elements. The assumption of passive RIS imposes the unit modulus constraint, which makes the beamforming problem non-convex. To tackle this issue, we apply semidefinite relaxation (SDR) to obtain the optimal phase-shift matrix and propose an iterative algorithm to obtain a statistically optimal solution for the transmit beamforming vector and RIS-phase shift matrix. Further, to measure the performance of the proposed beamforming scheme, we analyze key system performance metrics such as outage probability (OP) and ergodic capacity (EC). Similar to the existing works, the OP and EC evaluations rely on the numerical computation of the proposed iterative algorithm. However, this is not conducive to revealing the functional dependence of system performance on key parameters such as line-of-sight (LoS) components, correlated fading, number of reflecting elements, number of antennas at the base station (BS), and fading factor. To overcome this major limitation, we derive closed-form expressions for the optimal beamforming vector and phase shift matrix for various special cases of the above general fading model. These fixed-point solutions aid in deriving a closed-form solution for OP that further provides a direct evaluation of EC. These mathematical expressions are then used to gain useful insights into the system’s performance. We analytically establish the fact that correlated fading is more beneficial than the independent and identically distributed (i.i.d.) case when the LoS components are blocked. Further, we also analytically demonstrate that the maximum mean SNR improves linearly/quadratically with the number of RIS elements in the absence/presence of LoS component under i.i.d. fading.

This study investigates the problem of decentralized dynamic resource allocation optimization for ad-hoc network communication with the support of reconfigurable intelligent surfaces (RIS), leveraging a reinforcement learning framework. In the present context of cellular networks, device-to-device (D2D) communication stands out as a promising technique to enhance the spectrum efficiency. Simultaneously, RIS have gained considerable attention due to their ability to enhance the quality of dynamic wireless networks by maximizing the spectrum efficiency without increasing the power consumption. However, prevalent centralized D2D transmission schemes require global information, leading to a significant signaling overhead. Conversely, existing distributed schemes, while avoiding the need for global information, often demand frequent information exchange among D2D users, falling short of achieving global optimization. This paper introduces a framework comprising an outer loop and inner loop. In the outer loop, decentralized dynamic resource allocation optimization has been developed for self-organizing network communication aided by RIS. This is accomplished through the application of a multi-player multi-armed bandit approach, completing strategies for RIS and resource block selection. Notably, these strategies operate without requiring signal interaction during execution. Meanwhile, in the inner loop, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm has been adopted for cooperative learning with neural networks (NNs) to obtain optimal transmit power control and RIS phase shift control for multiple users, with a specified RIS and resource block selection policy from the outer loop. Through the utilization of optimization theory, distributed optimal resource allocation can be attained as the outer and inner reinforcement learning algorithms converge over time. Finally, a series of numerical simulations are presented to validate and illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

An uplink multi-user tracking problem aided by multiple passive reconfigurable intelligent surfaces (RISs) is addressed in this work. Under a near-field circumstance, a multi-antenna base station (BS) localizes multiple moving single-antenna users by processing the received signals transmitted by users and reflected by RISs. Considering the users' mobility and the potential obstruction of line-of-sight paths, a multi-user tracking system based on the extended Kalman filter (EKF) which fully exploits the temporal correlations between each user's coordinate changes is designed. Then, the Bayesian Cramér–Rao bound (BCRB) of tracking errors is derived in a pattern consistent with the EKF process. Subsequently, an optimization scheme for passive phase shift design at the RISs is devised by minimizing the derived BCRB and is solved using the Gradient Descent method. Numerical results indicate that the accuracy of our tracking algorithm can approach the BCRB. With abundant RISs deployed and optimized, high-precision multi-user tracking via a single BS can be realized even in harsh localization environments. [ABSTRACT FROM AUTHOR]

In this paper, we propose an optimization algorithm for RIS-assisted multiple-input single-output vehicle communication systems, Given a vehicle-to-vehicle user signal to interference plus noise ratio requirement, we optimize the transmit beamforming vector and phase shift matrix of RIS to obtain the maximum transmission rate of vehicle to infrastructure user. To deal with the coupled variables in the optimization problem, the alternate iterative algorithm is exploited to divide the original optimization problem into two sub-problems, each with a single variable. Moreover, the first-order Taylor expansion and the semidefinite relaxation methods are used to transform the nonconvex sub-problems into convex optimization problems. The simulation results are presented to validate the superiority of the proposed method compared to the benchmark schemes. Additionally, the simulation results also reveal that there exits an optimal vehicle speed under different path loss exponents so as to achieve the maximum transmission rate if the RIS is used by our proposed beamforming method. [ABSTRACT FROM AUTHOR]

In this research, we propose an advanced hybrid precoding design for massive MIMO systems. Our approach integrates two innovative strategies: employing low-resolution ADCs and DACs with non-uniform quantization (NniQ) and implementing dynamic hybrid relay reconfigurable intelligent surfaces (DHRR-RIS). We utilize an iterative alternating minimization algorithm to improve spectral and energy efficiency in the first strategy. The second approach integrates DHRR-RIS with machine learning techniques, including adaptive back propagation neural network (ABPNN) for channel estimation, deep deterministic policy gradient (DDPG) algorithm for hybrid precoding and combining, fire hawk optimization (FHO) for DHRR-RIS, and enhanced fuzzy C-means (EFCM) for data clustering. These methodologies significantly enhance bit error rate (BER) and weighted sum rate (WSR) compared to traditional uniform quantization (UniQ) methods. Our results show that combining low-resolution ADCs/DACs with NniQ and DHRR-RIS, further optimized by machine learning, effectively reduces hardware complexity and power usage while markedly improving BER and WSR, offering a promising direction for efficient massive MIMO system development. [ABSTRACT FROM AUTHOR]

The research on high-precision and all-scenario localization using the millimeter-wave (mmWave) band is of great urgency. Due to the characteristics of mmWave, blockages make the localization task more complex. This paper proposes a cooperative localization system among user equipment (UEs) assisted by reconfigurable intelligent surfaces (RISs), which considers device-to-device (D2D) communication. RISs are used as anchor points, and position estimation is achieved through signal exchanges between UEs. Firstly, we establish a localization model based on this system and derive the UEs’ positioning error bound (PEB) as a performance metric. Then, a UE-RIS joint beamforming design is proposed to optimize channel state information (CSI) with the objective of achieving the minimum PEB. Finally, simulation analysis demonstrates the advantages of the proposed scheme over RIS-assisted base station positioning, achieving centimeter-level accuracy with a 10 dBm lower transmission power.