Emerging Development of Wireless Localization Technologies Aided with Reconfigurable Intelligent Surfaces: A Comprehensive Survey

A fresh line of research for future wireless networks is called reconfigurable intelligent surface (RIS)-aided communication. We developed one-bit and two-bit discrete phase shifter facilitated RIS for access point (AP) scenario in this study. In the AP scenario, RIS is considered as a source and it can transmit the information. When RIS is utilized as an AP, it can generate the phase angles needed for constellation mapping as well as compensate for channel distortion. The proximity of the radio frequency (RF) signal source to RIS minimizes the impact of fading between RIS and AP. The exact phase compensation of each RIS component is very difficult practically. Consequently, we can enhance the system’s overall performance using a discrete phase shifter. We are evaluating the effectiveness of one- and two-bit discrete phase shifters over intelligent RIS-AP and blind RIS-AP methods.KeywordsAccess point (AP)Blind communicationDiscrete phase shiftersIntelligent communicationReconfigurable intelligent surfaces (RIS)

Challenging environments comprise a range of scenarios, which share the fact that it is extremely difficult to establish a communication link using conventional technology due to many impairments typically associated with the propagation medium and increased signal scattering. Specifically, underwater and underground media are known to absorb electromagnetic radiation, which heavily affects the overall path loss. Industrial and disaster environments can be viewed as rich scattering environments with corresponding substantial multipath propagation leading to intersymbol interference and deterioration of signal quality. Although the challenges for the design of communication networks, and specifically the Internet of Things (IoT), in such environments are known, there is no common enabler or solution for all these applications. Reconfigurable intelligent surfaces (RISs) have been introduced to improve the signal propagation characteristics by focusing the signal power in the preferred direction, thus making the communication environment 'smart'. While the usual application of RIS is related to blockage avoidance, the very same technique can be used to reduce the effect of multipath and even partially compensate the signal absorption via passive beamforming. Due to the beneficial properties of RIS, its use in challenging environments can become the aforementioned enabler and a game changing technology. In this paper, we discuss potential use cases, deployment strategies and design aspects for RIS devices in underwater IoT, underground IoT as well as Industry 4.0 and emergency networks. Furthermore, we provide a potential hardware architecture and derive the expected signal quality. The numerical results reveal substantial performance gains of up to 20 dB per decade. In addition, novel research challenges to be addressed in this context are described.

Reconfigurable intelligent surface (RIS) is regarded as a promising technology to assist indoor millimeter wave (mm-Wave) communication due to its ability to manipulate electromagnetic waves directionally. Due to the complexity of indoor environment, different RIS deployment strategies significantly affect the system performance. However, the existing work investigating RIS indoor deployment ignored the influence of human mobility to indoor signal coverage, therefore a practical indoor deployment strategy remains unknown. In this letter, based on the consideration of human blockage to mm-Wave signal and probability distribution of human location, we propose an indoor RIS deployment strategy with human mobility model to minimize the outage probability, which is more applicable to the realistic indoor environment. An algorithm is presented to minimize the outage probability at a subspace scale of the room geometry and calculate the corresponding RIS location for single RIS deployment and multiple RISs cooperative deployment. Numerical results are provided to display how our deployment strategy works, which indicate that deploying RIS correctly can decrease the indoor communication outage probability significantly.

A High-Efficiency and Versatile Reconfigurable Intelligent Surface Design Paradigm with Novel Topological Representation

Reconfigurable intelligent surfaces (RIS) for mmWave 5 G have been proposed in literature as a means of creating a smart, reconfigurable wireless environment, and to overcome high levels of signal loss due to free-space path loss at mmWave frequencies. These RIS devices enable the deliberate redirection of electromagnetic waves for increasing the signal strength in an environment via reflection. This redirection is done by tuning the individual phases of reflectors in a periodic array by either mechanical or electrical means. In the literature, prevailing research has focused on RIS designs made of substrate materials that are not transparent in the optical spectrum. However, this restricts RIS designs to only be applied to surfaces that do not require transparency, such as walls, and makes the design visible unless it is embedded in a structure. This impedes, if not prevents altogether, the ability to use RIS on structures that require transparency (i.e., windows). In this situation, if the RIS is not embedded into a structure, integrating RIS into walls or other structures of a building may not be desirable due to aesthetics. This work proposes a proof of concept optically transparent RIS for the 5 GHz WiFi band to demonstrate the ability of transparent materials to be used in RIS designs. The transparent RIS is made of soda lime glass sputtered with optically transparent indium tin oxide (ITO) to address the aforementioned issues of current RIS designs that are not transparent. The design is a 1-bit phase shifting RIS using diodes for reduction in costs and ease in programming the array. Additionally, the design makes use of high-impedance biasing lines for the diodes to avoid the need of multilayered design using vias, which is not feasible for a transparent design using glass or other brittle, transparent materials.

On performance of multi-RIS assisted multi-user nonorthogonal multiple access system over Nakagami-[formula omitted] fading channels

This article investigates the resource allocation of a reconfigurable intelligent surface (RIS)-aided joint communication and sensing (JCAS) system in a coal mine scenario. In the JCAS system, an RIS is implemented at the corner of the zigzag tunnels to improve the complicated wireless environment, where ground obstacles frequently block direct links. In addition, a wireless backhaul base station with a limited energy budget is deployed in the depth of the mine to sense the target area and provide Internet of Things (IoT) services and communication services for users. Furthermore, a data center is placed on the ground to analyze the obtained data and route the communication data. Under this deployment, a joint optimization problem of RIS phase-shift matrix, RIS element switches, and area sensing time is proposed. We aim to maximize the successful sensed bits under total completion time, and maximum transmit power constraints. In order to solve this problem, an iterative algorithm is proposed. The successive convex approximation (SCA)-based algorithm is used for the RIS phase-shift matrix optimization subproblem. For the sensing time optimization subproblem, the quadratic approximation method is proposed to optimize the number of area perceptions. The coordinate descent method is utilized to optimize the RIS element switches. Simulation results show that the energy efficiency is improved by up to 38%, and 7% increases the specific data size compared with the benchmark solutions.

This paper investigates the potential of active reconfigurable intelligent surfaces (RIS) to enhance wireless-powered communication networks (WPCNs), addressing the evolving connectivity needs of the internet of things (IoT). Active RIS, capable of amplifying and reflecting signals, offers a solution to surpass the limitations of passive RIS, such as double-fading attenuation, aiming to significantly improve network throughput and coverage. Our research focuses on exploiting the amplification capabilities of active RIS to boost the overall network sum throughput, engaging in a comprehensive optimization of critical network parameters, including time allocation, reflection coefficients, and phase shift matrices specific to active RIS. The formulated problem is non-convex and highly complex due to the coupling of optimization variables. We employed a successive convex approximation algorithm to solve the throughput maximization problem by converting the non-convex constraints into approximated convex constraints and solving them iteratively. Through extensive simulations, we demonstrate that our active RIS-assisted network substantially outperforms networks facilitated by passive RIS, marking a significant advancement in WPCN performance. These findings underscore the potential of active RIS technology in realizing the full capabilities of IoT connectivity.

Abstract—With the development of smart devices, Internet of Things(IoT) has requirements for high coverage, high reliability, and low power consumption for wireless communication systems. The emergence of reconfigurable intelligent surfaces(RIS) provides an achievable solution for further development of IoT. RIS consists of passive low-cost components, which can reshaping the wireless channel. Thus it can improve multi-stream transmission gain, enhance edge coverage and realize large-scale Device-to-Device communication. In this paper, we consider RIS-assisted multiple-input single-output(MISO) communication systems, and our goal is to maximize the sum-rate of all IoT receiving devices by jointly designing the beamforming of access points(AP) and the phase shift of RIS elements. For the non-convex problem form, we propose the Improved Elite Genetic Algorithm(IEGA) to obtain a smooth solution of the problem. Numerical results demonstrate the effectiveness of RIS and the proposed joint algorithm for the performance improvement of IoT wireless communication systems.We analyzed the impact of the deployment of RIS and the number of RIS elements on the sum-rate at the receiving devices, which facilitates the balance between the cost and benefit of increasing RIS elements in practical deployments.

Over-the-air computation (AirComp) is a promising technology that is capable of achieving fast data aggregation in Internet of Things (IoT) networks. The mean-squared error (MSE) performance of AirComp is bottlenecked by the unfavorable channel conditions. This limitation can be mitigated by deploying a reconfigurable intelligent surface (RIS), which reconfigures the propagation environment to facilitate the receiving power equalization. The achievable performance of RIS relies on the availability of accurate channel state information (CSI), which however is generally difficult to be obtained. In this paper, we consider an RIS-aided AirComp IoT network, where an access point (AP) aggregates sensing data from distributed devices. Without assuming any prior knowledge on the underlying channel distribution, we formulate a stochastic optimization problem to maximize the probability that the MSE is below a certain threshold. The formulated problem turns out to be non-convex and highly intractable. To this end, we propose a data-driven approach to jointly optimize the receive beamforming vector at the AP and the phase-shift vector at the RIS based on historical channel realizations. After smoothing the objective function by adopting the sigmoid function, we develop an alternating stochastic variance reduced gradient (SVRG) algorithm with a fast convergence rate to solve the problem. Simulation results demonstrate the effectiveness of the proposed algorithm and the importance of deploying an RIS in reducing the MSE outage probability.

In this paper, a novel framework for guaranteeing ultra-reliable millimeter wave (mmW) communications using multiple artificial intelligence (AI)-enabled reconfigurable intelligent surfaces (RISs) is proposed. The use of multiple AI-powered RISs allows changing the propagation direction of the signals transmitted from a mmW access point (AP) thereby improving coverage particularly for non-line-of-sight (NLoS) areas. However, due to the possibility of highly stochastic blockage over mmW links, designing an intelligent controller to jointly optimize the mmW AP beam and RIS phase shifts is a daunting task. In this regard, first, a parametric risk-sensitive episodic return is proposed to maximize the expected bitrate and mitigate the risk of mmW link blockage. Then, a closed-form approximation of the policy gradient of the risk-sensitive episodic return is analytically derived. Next, the problem of joint beamforming for mmW AP and phase shift control for mmW RISs is modeled as an identical payoff stochastic game within a cooperative multi-agent environment, in which the agents are the mmW AP and the RISs. Two centralized and distributed controllers are proposed to control the policies of the mmW AP and RISs. To <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">directly</i> find a near optimal solution, the parametric functional-form policies for the controllers are modeled using deep recurrent neural networks (RNNs). The deep RNN-based controllers are then trained based on the derived closed-form gradient of the risk-sensitive episodic return. It is proved that the gradient update algorithm converges to the same locally optimal parameters as the deep RNN-based centralized and distributed controllers. Simulation results show that the error between the policies of the optimal and the RNN-based controllers is less than 1.5%. Moreover, the variance of the achievable rates resulting from the deep RNN-based controllers is 60% less than the variance of the risk-averse baseline.

With the development of the next generation of mobile networks, new research challenges have emerged, and new technologies have been proposed to address them. On the other hand, reconfigurable intelligent surface (RIS) technology is being investigated for partially controlling wireless channels. RIS is a promising technology for improving signal quality by controlling the scattering of electromagnetic waves in a nearly passive manner. Heterogeneous networks (HetNets) are another promising technology that is designed to meet the capacity requirements of the network. RIS technology can be used to improve system performance in the context of HetNets. This study investigates the applications of reconfigurable intelligent surfaces (RISs) in heterogeneous downlink networks (HetNets). Due to the network densification, the small cell base station (SBS) interferes with the macrocell users (MUEs). In this paper, we utilise RIS to mitigate cross-tier interference in a HetNet via directional beamforming by adjusting the phase shift of the RIS. We consider RIS-assisted heterogeneous networks consisting of multiple SBS nodes and MUEs that utilise both direct paths and reflected paths. Therefore, the aim of this study is to maximise the sum rate of all MUEs by jointly optimising the transmit beamforming of the macrocell base station (MBS) and the phase shift of the RIS. An efficient RIS reflecting coefficient-based optimisation (RCO) is proposed based on a successive convex approximation approach. Simulation results are provided to show the effectiveness of the proposed scheme in terms of its sum rate in comparison with the scheme HetNet without RIS and the scheme HetNet with RIS but with random phase shifts.

A hybrid PSO-GWO-based phase shift design for a hybrid-RIS-aided heterogeneous network system

This paper proposes a partition-based reconfigurable intelligent surface (RIS)-assisted multi-user multiple-input single-output (MU-MISO) symbiotic radio (SR) system. Unlike conventional RIS designs in SR systems that utilize all elements for secondary transmissions, which restricts its ability to support the primary transmission, the RIS is divided into two subsurfaces: one enhances primary transmissions while the other transmits IoT information via spectrum sharing. We formulate a joint optimization problem to minimize transmit power under rate constraints for both primary and IoT transmissions through active beamforming and passive RIS phase-shift design. To tackle the non-convex constraints and variable coupling in the formulated problem, we propose efficient optimization techniques, including alternating optimization and difference-of-convex methods. Furthermore, we propose a low-complexity interference-free scheme leveraging partitioned RIS to eliminate inter-user interference, which is unachievable with conventional RIS design. Simulation results reveal that compared to the conventional scheme, which can be viewed as a special case of partition-based RIS where all the reflecting elements are used to transmit IoT information, partition-based RIS demonstrates superior performance, thereby validating the advantages of RIS partitioning in SR systems.

Chapter 3 of the monograph meticulously presents the methodology used for integrating Artificial Intelligence (AI) with Reconfigurable Intelligent Surfaces (RIS) into urban infrastructure, forming a comprehensive framework for implementing and assessing these technologies. This chapter delineates the systematic approach taken from the technical setup and configuration of AI and RIS systems to the sophisticated analytical techniques used to evaluate their effectiveness. The technical implementation details the strategic placement and integration of RIS panels throughout urban environments, particularly in areas plagued by poor connectivity or where high usage demands more robust infrastructure. AI systems are finely tuned to manage these panels, dynamically adjusting to real-time data concerning environmental changes, network traffic, and specific urban events, ensuring optimal performance. This section highlights the complex coordination required with existing city infrastructure and the adaptable, scalable nature of the technology to fit within the evolving urban landscape. Furthermore, the methodology encompasses advanced analytical tools and models to rigorously assess the impact of AI-enhanced RIS on urban infrastructure. Employing software like MATLAB and Python, along with GIS for spatial analysis, the study intricately models how RIS modifies electromagnetic fields to improve network performance across varied urban settings. Additionally, machine learning algorithms are employed to refine RIS behavior continuously, illustrating a practical application of AI in managing city-scale communication networks. The chapter also introduces a case study approach that provides real-world insights into the deployment of these technologies, offering a detailed examination of their application across different urban scenarios. This approach not only illustrates the practical challenges and successes encountered but also reinforces theoretical models with empirical evidence, enhancing the credibility and reliability of the research. Overall, Chapter 3 lays down a solid foundation of the methodologies employed, emphasizing the blend of technical precision and analytical rigor required to advance AI and RIS integration in urban infrastructures. It underscores the potential of these technologies to significantly enhance urban connectivity and adaptability, setting a benchmark for future implementations and studies in the field. &nbsp; 3.1 Implementation Framework The implementation framework for integrating Artificial Intelligence (AI) with Reconfigurable Intelligent Surfaces (RIS) in urban infrastructure projects is a cornerstone of this monograph, setting the foundation for understanding how these technologies are applied in real-world settings. The framework delineates the technical setups and configurations necessary to harness the full potential of AI-enhanced RIS, ensuring that the technology not only fits within the existing urban infrastructure but also optimally performs its intended functions.