Reconfigurable Intelligent Surface Research Articles

Navigating the Future of Intelligent Transportation: Challenges and Solutions in 6G V2X and V2V Networks

Recent advancements in Information and Communication Technologies (ICT) have transformed vehicular communication, facilitating Vehicle-to-Vehicle (V2V) and Vehicle-to-Everything (V2X) connectivity. These technologies enhance travel safety and efficiency while also reducing pollution and accident rates through optimised resource utilisation. V2X applications encounter significant challenges concerning traffic safety, data security, and scalability, necessitating thorough evaluation prior to extensive implementation. The rising prevalence of connected vehicles presents challenges, including network instability, heterogeneity, and extensive data management, which require novel solutions for effective communication. This study gives a new view on how to combine 6G technologies with V2X and V2V communication systems. It addresses important issues like security, resource allocation, and making sure that communication is very reliable and has low latency. Our study is different from others because it looks at all the technologies that make this possible, including Proportional Fairness Algorithms, machine learning, blockchain, and Reconfigurable Intelligent Surfaces (RIS). We emphasise the significance of collective perception (CP) in improving traffic safety and resource efficiency through the sharing of sensor data among V2X-enabled entities, including vehicles, roadside units (RSUs), and vulnerable road users.This research provides a thorough analysis of advanced methodologies and their limitations, making it a valuable resource for scholars, academics, and industry professionals. It offers a framework for developing future intelligent transportation systems that utilise 6G capabilities to achieve high reliability, minimal latency, and optimal spectrum efficiency in vehicular networks.

Emerging Topics in Joint Radio-Based Positioning, Sensing, and Communications

This is an editorial paper focusing on emerging topics in joint wireless positioning, sensing, and communications. After introducing the sometimes-confusing and non-unified terminology in the field and defining the overall research area under the comprehensive terminology of Joint positioning, sensing, and communications (JPSAC), a brief state-of-the art overview is given, followed by a detailed list of emerging topics and open research questions. The ongoing Horizon Europe projects are also reviewed in relation to the emerging JPSAC. Some of the main trends in the JPSAC-related areas are related to extremely large-scale antennas and apertures, reconfigurable intelligent surfaces, the integration of terrestrial and satellite-based communication, sensing, and positioning functionalities, and cell-free or distributed networks with JPSAC functions.

Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey

The expansion of 5G infrastructure and the deployment of large antenna arrays are set to substantially influence electromagnetic field (EMF) exposure levels within mobile networks. As a result, the accurate measurement of EMF exposure and the integration of EMF exposure constraints into radio resource management are expected to become increasingly important in future mobile communication systems. This paper provides a comprehensive review of EMF exposure evaluation frameworks for 5G networks, considering the impacts of high-energy beams, the millimeter wave spectrum, network densification and reconfigurable intelligent surfaces (RISs), while also examining EMF-aware radio resource management strategies for 5G networks and beyond, with RIS technology as an assistive factor. Furthermore, challenges and open research topics in the EMF evaluation framework and EMF-aware resource management for 5G mobile networks and beyond are highlighted. Despite the growing importance of RIS technology in enhancing mobile networks, a research gap remains in addressing specific EMF exposure considerations associated with RIS deployments. Additionally, the impact of EMF-aware radio resource allocation approaches on RIS-assisted 5G networks is still not fully understood.

Reconfigurable intelligent surface-aided positioning-centered multi-LED integrated visible light communication and positioning system

This paper investigates a reconfigurable intelligent surface (RIS)-aided integrated visible light communication and positioning (VLCP) system, which aims to reduce sensitivity to line-of-sight (LoS) blockage in visible light and simultaneously achieve capabilities for both visible light communication (VLC) and visible light positioning (VLP). The framework of this system is divided into three parts: positioning, channel state information (CSI) estimation, and data transmission. When the LoS links are present, due to the potential adverse positioning effects RIS caused in this scenario, the RIS only plays a role in assisting the communication. Meanwhile, the positioning process is based on the received signal strength (RSS) information from the LoS transmission. If the LoS links are blocked, the RIS provides added non-line-of-sight (NLoS) positioning signals and achieves subsidiary communications accordingly. The Cramer-Rao lower bound (CRLB) on the positioning result is derived and the CSI estimation is conducted based on the results provided by the positioning schemes to reduce the pilot overhead. The achievable rate is evaluated based on the estimated CSI. The configurations of RIS parameters and the power allocated to positioning and communication are jointly optimized to minimize the CRLB while satisfying constraints on the maximum tolerable outage probability, RIS parameter acquisition, and total transmit power. To address this non-convex optimization problem, we proposed an alternative optimization (AO) algorithm to optimize the RIS assignment matrix and the power coefficient alternatively. We employ the worst-case distribution of the Conditional Value-at-Risk (CVaR) along with successive convex approximation (SCA) techniques to derive feasible solutions of the power coefficient optimization subproblem. Meanwhile, we optimize the RIS assignment matrix based on the principle of maximum received power chosen (MRPC). Simulation results demonstrate the influence of RIS in the positioning process in our proposed VLCP system with or without the presence of LoS links, and meanwhile, the relationship between communication performance and positioning performance is validated through the simulations. From the simulation results, the RIS-aided VLCP system can be verified as a viable option for the upcoming era of sixth-generation (6G) technology.

Optimization of Bandwidth Allocation and UAV Placement in Active RIS-Assisted UAV Communication Networks with Wireless Backhaul

In this paper, we present a novel design for unmanned aerial vehicle (UAV) communication networks with wireless backhaul, where an active reconfigurable intelligent surface (ARIS) is deployed to improve connections between a UAV and multiple users, while mitigating channel impairments in complex environments. The proposed design aims to maximize the achievable sum rate of all networks by jointly optimizing UAV placement; resource management strategies; transmit power allocation; and ARIS reflection coefficients, subject to backhaul constraints and power budget limitations in the ARIS system. The resulting optimization problem is highly non-convex, posing significant challenges. To tackle this, we decompose the problem into three interrelated sub-problems and apply inner approximation (IA) techniques to handle the non-convexities within each sub-problem. Moreover, a comprehensive alternating optimization framework is proposed to implement an iterative solution for the sub-problems. Simulation results demonstrate that the proposed algorithm achieves approximately 59% improvement in the average sum rate, substantially enhancing overall network reliability compared to existing benchmark schemes.

STAR-RIS-aided NOMA communication for mobile edge computing using hybrid deep reinforcement learning

Reconfigurable intelligent surface (RIS) is expected to be able to significantly reduce task processing delay and energy consumptions of mobile users (MUs) in mobile edge computing (MEC) by intelligently adjusting its reflecting elements’ phase-shifts and amplitudes. Nevertheless, both the passive and active RISs have the disadvantage of only reflecting the received signals, which means that the transmitters and receivers must be located on the same side of the RIS. This may be unrealistic due to the movement of MUs. Simultaneously transmitting and reflecting (STAR) RIS, which can simultaneously transmit and reflect incident signals to achieve full-area coverage, has been recognized as a revolutionary technique to solve the above-mentioned problem. For the STAR-RIS-aided non-orthogonal multiple access (NOMA) communication MEC, we first formulate an optimization problem to minimize the sum of weighted delay and energy consumptions of all MUs which can move randomly at low speeds. Then, under the practical coupled phase-shift model of STAR-RIS, we propose a hybrid deep reinforcement learning (DRL) scheme, in which we determine the amplitudes and phase-shifts of STAR-RIS, task offloading decisions of MUs, and computation resource allocations of MEC servers by using the deep deterministic policy gradient (DDPG) and Dueling deep Q learning (DQN). Finally, we validate and evaluate the performances of our proposed scheme through extensive simulations, which show that our proposed scheme outperforms the existing baseline schemes and its performance can indeed be improved due to the use of STAR-RIS.

Resource and Trajectory Optimization in RIS-Assisted Cognitive UAV Networks with Multiple Users Under Malicious Eavesdropping

Unmanned aerial vehicles (UAVs) have shown significant advantages in disaster relief, emergency communication, and Integrated Sensing and Communication (ISAC). However, the escalating demand for UAV spectrum is severely restricted by the scarcity of available spectrum, which in turn significantly limits communication performance. Additionally, the openness of the wireless channel poses a serious threat, such as wiretapping and jamming. Therefore, it is necessary to improve the security performance of the system. Recently, Reconfigurable Intelligent Surfaces (RIS), as a highly promising technology, has been integrated into Cognitive UAV Network. This integration enhances the legitimate signal while suppressing the eavesdropping signal. This paper investigates a RIS-assisted Cognitive UAV Network with multiple corresponding receiving users as cognitive users (CUs) in the presence of malicious eavesdroppers (Eav), in which the Cognitive UAV functions as the mobile aerial Base Station (BS) to transmit confidential messages for the users on the ground. Our primary aim is to attain the maximum secrecy bits by means of jointly optimizing the transmit power, access scheme of the CUs, the RIS phase shift matrix, and the trajectory. In light of the fact that the access scheme is an integer, the original problem proves to be a mixed integer non-convex one, which falls into the NP-hard category. To solve this problem, we propose block coordinate descent and successive convex approximation (BCD-SCA) algorithms. Firstly, we introduce the BCD algorithm to decouple the coupled variables and convert the original problem into four sub-problems for the non-convex subproblems to solve by the SCA algorithm. The results of our simulations indicate that the joint optimization scheme we have put forward not only achieves robust convergence but also outperforms conventional benchmark approaches.

Programmable beam steering and quasi-continuous phase shift on a 2-bit terahertz coding metasurface with HEMT-switched multimodal modulation.

Terahertz reconfigurable intelligent surfaces (RIS) stand out from conventional phased arrays thanks to their unique electromagnetic properties and intelligent interconnect paradigms. They are a vital technology for terahertz wireless communication and radar detection systems. Compared with 1-bit coding metasurfaces, 2-bit coding metasurfaces offer significant advantages such as single beam steering and reduced quantization errors. Electrically controlled switchable coding metasurfaces have been restricted by the switch performance and integration technology, limiting reported devices to 1-bit coding. Additionally, metasurfaces that offer quasi-continuous phase modulation across a 2π range have garnered extensive attention for their higher phase shift precision and regulation freedom. This paper proposes a 2-bit multimodal THz quasi-continuous phase shift coding metasurface based on asymmetrically configured high electron mobility transistor (HEMT)-dipoles. The meta-device consists of two asymmetrically combined HEMT-dipole resonant structures, wherein the versatile density variations of two-dimensional electron gas (2DEG) introduce subtly local-field modulation under different external voltage combinations, rendering the quasi-continuous phase shift of 0° to 350° at 0.376 THz with a minimum incremental step of 6°. Moreover, selecting optimal 2-bit coding states within the quasi-continuous phase shift loop constructed different array phase gradients based on Snell's law, enabling real-time beam steering from 21° to 48° within the 0.365-0.385 THz frequency range. The simulation results align closely with the experimental findings, marking the first achievement of real-time programmable phase shift control and 2-bit beam steering using electrically controlled switches in the terahertz domain. This research provides a practical approach for real-time quasi-optical continuous phase modulation control and programmable single-beam electronic steering, with promising applications in terahertz wireless communication, radar detecting, and computational imaging, among other fields.

Wireless power transfer empowered by reconfigurable intelligent surfaces

ABSTRACT Reconfigurable Intelligent Surfaces (RIS) represent a significant advancement in hardware technology, enhancing both wireless energy and spectral efficiency within wireless communication systems. Comprising a programmable metasurface, RIS can adeptly manipulate the wavefronts of incident signals – encompassing parameters such as phase, amplitude, frequency, and polarisation – without necessitating complex signal processing techniques. The integration of RIS into wireless communication networks enables the strategic manipulation of radio waves, thereby mitigating the adverse effects associated with natural wireless propagation. This study examines a multi-user (MU) multiple-input multiple-output (MIMO) wireless power transfer (WPT) system augmented by multiple RISs, wherein the transmitter is equipped with a constant-envelope beamformer. The optimisation of the phase shifts of the RIS and the beamformer of the transmitter is conducted jointly to maximise the total power received by users. An alternating optimisation approach is proposed, utilising the semidefinite relaxation (SDR) method. Additionally, a problem is formulated to maximise the total received power while adhering to constraints on the minimum power received by users, thereby addressing issues of user fairness. To resolve this problem, an iterative algorithm based on the Alternating Direction Method of Multipliers (ADMM) is introduced. Analysis and simulation results indicate a significant enhancement in total received power when employing the proposed methodology compared to existing algorithms documented in the literature. Furthermore, in scenarios involving multiple users, a detailed analysis of the system’s transmission performance, facilitated by RIS, is conducted. The numerical results substantiate the validity of the analysis and demonstrate the efficacy of the proposed algorithms.

“Zero-Power” Additively Manufactured FHE-Enabled Wireless/5G+ Ultrabroadband Modules for IoT, Industry 4.0 and Smart Cities Applications

In this talk, inkjet-/3D-printed antennas, interconnects, “smart” encapsulation and packages, RF electronics, RFIDs microfluidics and sensors fabricated on glass, PET, paper and other flexible substrates are introduced as a system-level solution for ultra-low-cost mass production of millimeter-wave modules and metasurfaces for communication, energy harvesting and sensing applications. The state- of-the-art area of fully-integrated printable FHE-Enabled broadband wireless modules will be reported covering characterization of 3D printed materials up to E-band, novel printable “ramp” interconnects and cavities for IC embedding as well as printable structures for self-monitoring and anti-counterfeiting packages. The presented approach could potentially set the foundation for the truly convergent flexible wireless sensor ad-hoc networks of the future with enhanced cognitive intelligence and “rugged” packaging. The challenge of power sources of “near-perpetual” RF modules will be discussed, including 5G-enabled wireless power grids as well as energy harvesting approaches involving thermal, EM, vibration and solar energy forms. The final step of the presentation will involve examples from shape-changing 4D-printed (origami) packages, reflectarrays and mmW wearable (e.g. biomonitoring) antennas and RF modules. Special attention will be paid on the integration of ultrabroadband (Gb/sec) inkjet-printed nanotechnology-based backscattering communication modules, opto-RF modules as well as miniaturized printable wireless (e.g.CNT) sensors for Internet of Things (IoT), 5G and smart agriculture/biomonitoring applications. It has to be noted that the talk will review and present solutions for “5S Challenges” (Scalability, Sustainability, Speed, Security and Smartness) as well as future directions in the area of environmentally-friendly transient (“green”) RF electronics and “smart-skin’ conformal sensors as well as massively scalable “tile-by-tile” RFID-enabled autonomous reconfigurable intelligent surfaces.

RF System Advancements for Satellite and Space Communications: Integrating AI with High-Frequency and Hybrid Systems

With the rapid evolution of satellite communications, the use of advanced radio frequency (RF) systems has become pivotal in addressing emerging challenges. High-frequency bands, such as millimeter-wave (mmWave) and terahertz (THz), enable high-throughput data transmission but introduce new complexities such as Doppler shifts and signal degradation. Hybrid satellite-terrestrial networks are expanding the possibilities for 5G and 6G backhauls, while low Earth orbit (LEO) constellations improve latency- sensitive applications. This paper explores RF system advancements, including beamforming, MIMO, and AI-driven optimizations. Additionally, it discusses challenges like energy efficiency, atmospheric attenuation, and spectrum management. The paper concludes with insights into future trends such as quantum RF systems, deep- space communications, and reconfigurable intelligent surfaces. Keywords – Satellite Communications, RF Systems, Millimeter-Wave, Terahertz, Beamforming, AI-Driven Optimization, Hybrid Networks, Non-Terrestrial Networks (NTN), Cognitive Radios, Network Slicing, LEO Satellites, 5G/6G Backhaul, Doppler Mitigation, Energy-Efficient RF.

Energy-Efficient Aerial STAR-RIS-Aided Computing Offloading and Content Caching for Wireless Sensor Networks.

Unmanned aerial vehicle (UAV)-based wireless sensor networks (WSNs) hold great promise for supporting ground-based sensors due to the mobility of UAVs and the ease of establishing line-of-sight links. UAV-based WSNs equipped with mobile edge computing (MEC) servers effectively mitigate challenges associated with long-distance transmission and the limited coverage of edge base stations (BSs), emerging as a powerful paradigm for both communication and computing services. Furthermore, incorporating simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) as passive relays significantly enhances the propagation environment and service quality of UAV-based WSNs. However, most existing studies place STAR-RISs in fixed positions, ignoring the flexibility of STAR-RISs. Some other studies equip UAVs with STAR-RISs, and UAVs act as flight carriers, ignoring the computing and caching capabilities of UAVs. To address these limitations, we propose an energy-efficient aerial STAR-RIS-aided computing offloading and content caching framework, where we formulate an energy consumption minimization problem to jointly optimize content caching decisions, computing offloading decisions, UAV hovering positions, and STAR-RIS passive beamforming. Given the non-convex nature of this problem, we decompose it into a content caching decision subproblem, a computing offloading decision subproblem, a hovering position subproblem, and a STAR-RIS resource allocation subproblem. We propose a deep reinforcement learning (DRL)-successive convex approximation (SCA) combined algorithm to iteratively achieve near-optimal solutions with low complexity. The numerical results demonstrate that the proposed framework effectively utilizes resources in UAV-based WSNs and significantly reduces overall system energy consumption.

Optimal Reconfigurable Intelligent Surface Deployment for Secure Communication in Cell-Free Massive Multiple-Input Multiple-Output Systems with Coverage Area

This paper investigates the secure communication in the reconfigurable intelligent surface (RIS)-aided cell-free massive multiple-input multiple-output (CF-mMIMO) system in the presence of an eavesdropper (Eve). Since the RIS can only reflect the incident signal from its front, we define the RIS coverage and non-coverage area based on whether the incident signals can be reflected. The RIS coverage area is affected by the deployment position and rotation angle, and thus, we take both of these two factors into account and a closed-form approximation for the ergodic secrecy rate of the legitimate user is derived. Based on it, the optimal RIS deployment position and phase shift are obtained through an alternating iteration method, and the optimal RIS angle is achieved through an exhaustive enumeration of angles with a certain interval. Simulations confirm that our optimal RIS deployment can achieve a superior secrecy rate. We find that to guarantee the best secrecy rate, the RIS should be placed near the target user, and its rotation angle should be adjusted to make as many access points (APs) as possible within the RIS coverage area.

Adaptive Filtering for Channel Estimation in RIS-Assisted mmWave Systems.

The advent of millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems, coupled with reconfigurable intelligent surfaces (RISs), presents a significant opportunity for advancing wireless communication technologies. This integration enhances data transmission rates and broadens coverage areas, but challenges in channel estimation (CE) remain due to the limitations of the signal processing capabilities of RIS. To address this, we propose an adaptive channel estimation framework comprising two algorithms: log-sum normalized least mean squares (Log-Sum NLMS) and hybrid normalized least mean squares-normalized least mean fourth (Hybrid NLMS-NLMF). These algorithms leverage the sparse nature of mmWave channels to improve estimation accuracy. The Log-Sum NLMS algorithm incorporates a log-sum penalty in its cost function for faster convergence, while the Hybrid NLMS-NLMF employs a mixed error function for better performance across varying signal-to-noise ratio (SNR) conditions. Our analysis also reveals that both algorithms have lower computational complexity compared to existing methods. Extensive simulations validate our findings, with results illustrating the performance of the proposed algorithms under different parameters, demonstrating significant improvements in channel estimation accuracy and convergence speed over established methods, including NLMS, sparse exponential forgetting window least mean square (SEFWLMS), and sparse hybrid adaptive filtering algorithms (SHAFA).

Reconfigurable intelligent surfaces using NOMA with wind energy harvesting and power variation

Reconfigurable intelligent surfaces using NOMA with wind energy harvesting and power variation

Antenna Selection for Receive Spatial Modulation System Empowered by Reconfigurable Intelligent Surface

Antenna Selection for Receive Spatial Modulation System Empowered by Reconfigurable Intelligent Surface

A Convolutional Neural Network-Based Method for Accurate Computation of Scattered Fields From Reconfigurable Intelligent Surfaces

A Convolutional Neural Network-Based Method for Accurate Computation of Scattered Fields From Reconfigurable Intelligent Surfaces

Reconfigurable Intelligent Surfaces in 6G Radio Localization: A Survey of Recent Developments, Opportunities, and Challenges

Reconfigurable Intelligent Surfaces in 6G Radio Localization: A Survey of Recent Developments, Opportunities, and Challenges

The Emergence of Multi-Functional and Hybrid Reconfigurable Intelligent Surfaces for Integrated Sensing and Communications -A Survey

The Emergence of Multi-Functional and Hybrid Reconfigurable Intelligent Surfaces for Integrated Sensing and Communications -A Survey

Wireless Powered Reconfigurable Intelligent Surface Aided Cognitive NOMA Network With Hardware Impairments: IBL and FBL Analysis

Wireless Powered Reconfigurable Intelligent Surface Aided Cognitive NOMA Network With Hardware Impairments: IBL and FBL Analysis