Secure Performance Analysis of RIS-aided Wireless Communication Systems

Reconfigurable intelligent surface (RIS) has recently been regarded as a potential technique to enhance the performance of wireless communication systems by creating additional communication links. However, it is almost impossible to get the perfect channel state information (CSI) from the base station (BS) to the Internet of Things Devices (IoTDs) and the RIS-related channels. Furthermore, residual transceiver hardware impairments inevitably affect the performance of wireless communication systems. Hence, we study the robust design for an RIS-aided wireless communication system based on the imperfect CSI and hardware impairments. Minimizing the power consumption of BS is formulated by ensuring the minimum signal-to-interference-plus-noise ratio (SINR) demands of the IoTDs and the unit-modulus constraints of the RIS. Specifically, after approximating the nonconvex constraints by using the S-procedure and the successive convex approximation (SCA) methods, we adopt the block coordinate descent (BCD) technique to iteratively optimize one set of variables while keeping the other variables fixed in various channel uncertainty scenarios. Simulation results demonstrate that the influence of transceiver hardware impairments can be effectively decreased by deploying RIS even with channel uncertainty, which is more advantageous than increasing the number of BS’s antennas.

Symbiotic radio (SR) is a promising technology to support ultralow-power or even zero-power Internet of Things (IoT) devices in the sixth-generation mobile networks. In this article, we propose an energy-aware symbiotic transmission in a reconfigurable intelligent surface (RIS) enhanced SR system, in which an IoT network embeds its own data passively over cellular downlink signals by backscattering. The base station (BS) serves multiple cellular users (CUs) through time division multiple access (TDMA) and each IoT device is associated with one CU. We formulate the BS’s energy minimization problem subject to the constraints of the minimum amounts of transmission bits required by IoT devices and CUs. The user association, the active transmit beamforming at the BS, the passive reflecting beamforming at the RIS, and the frame division policy are jointly optimized. The formulated problem is a mixed integer nonlinear programming (MINLP) problem, which is NP-hard and nonconvex. We decouple the problem and solve the subproblems alternatively. First, we design a many-to-one swap-matching-based algorithm to solve the user association subproblem. Then, we develop a joint cooperative beamforming and time allocation optimization algorithm based on the alternative optimization (AO) and semidefinite relaxation (SDR) techniques. Simulation results show that the proposed joint user association and cooperative beamforming algorithm brings significant performance gain in reducing the energy consumption of the BS with fast convergence speed compared with other schemes.

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)

Integrating visible light communication (VLC) with the reconfigurable intelligent surface (RIS) significantly enhances physical layer security by enabling precise directional signal control and dynamic adaptation to the communication environment. These capabilities strengthen the confidentiality and security of VLC systems. This paper presents a comprehensive study on the joint optimization of VLC access point (AP) power allocation, RIS association, and RIS elements orientation angles for secure VLC systems, while considering rate-splitting multiple access (RSMA) and power-domain non-orthogonal multiple access (NOMA) schemes. Specifically, two frameworks are proposed to maximize both the minimum secrecy rate (SR) and the minimum secrecy energy efficiency (SEE) by jointly optimizing power allocation, RIS association, and RIS elements orientation angles for both power-domain NOMA and RSMA-based VLC systems. The proposed frameworks consider random device orientation and guarantee the minimum user-rate requirement. The proposed optimization frameworks belong to the class of mixed integer nonlinear programming, which has no known feasible solution methodology to guarantee the optimal solution. Moreover, the increased degree of freedom and flexibility from the joint consideration of power control, RIS association and element orientation results in a large set of decision variables and constraints, which further complicates the optimization problem. To that end, we utilize a genetic algorithm-based solution method, which through its exploration and exploitation capabilities can obtain a good quality solution. Additionally, comprehensive simulations show that the RSMA scheme outperforms the power-domain NOMA scheme across both the SR and SEE metrics over various network parameters. Furthermore, useful insights on the impact of minimum user rate requirement, number of RIS elements, and maximum VLC AP transmit power on the minimum SR and SEE performances are provided.

With the rapid development of Internet of Things (IoT) services, technologies that leverage multimedia computer communication for information sharing in embedded systems have become a research focus. To address the challenges of low spectral efficiency and poor network flexibility in multimedia computer communications, this paper proposes a resource allocation scheme based on parallel Convolutional Neural Network (CNN). The scheme optimizes the base station beamforming vector and the Reconfigurable Intelligent Surface (RIS) phase shifts to maximize the secure transmission rate for cellular users (CUs), while ensuring normal and secure communication for device-to-device (D2D) users. First, to mitigate interference caused by D2D users reusing CU spectrum resources, the RIS phase shifts and beamforming vectors are optimized to suppress interference and enhance system secrecy rates. Second, to maximize the CU secrecy rate, the paper proposes a parallel CNN-based resource allocation model that considers base station transmission power, RIS reflection coefficients, and D2D communication rate constraints, incorporating multi-scale residual modules in the convolutional layers of the model. Simulation results demonstrate that the proposed CNN-based resource allocation scheme significantly improves the secrecy rate of embedded system communications, ensuring secure multimedia computing, and outperforms traditional methods.

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.

This paper investigates the downlink of an internet-of-things (IoT) network with a base station serving multiple IoT devices (IoTDs) with the assistance of two far-apart reconfigurable intelligent surfaces (RISs). We propose joint design of the BS’s beamformer and RISs’ quantized programmable reflecting elements (PREs). Considering the IoTDs’ minimum rate (MR) as the primary optimization objective, we further aim to optimize the multi-objective function of both the MR and sum rate (SR) in the Pareto-optimal sense. We develop convex-solver and closed-form algorithms. Simulations demonstrate that the latter, with scalable complexity, performs as well as the former, which exhibits polynomially increasing complexity. Furthermore, the simulations reveal the advantages of the double-RIS assisted solution over its single-RIS assisted counterpart of the same size.

Reconfigurable intelligent surface (RIS) is thought to be a potential key technique for future wireless communications due to its ability for manipulating the electromagnetic environment smartly. This paper focuses on the rank and capacity analysis when a RIS is introduced into a multiple-input multiple-output (MIMO) system. By establishing a system model for this communication system, various simulations are conducted for identifying the characteristics of the channel. The simulations of different distance between the access point (AP) and user equipment (UE) show that the condition number of the channel is worsen when the distance increases and the role of the RIS in rank improvement is weaken. The spatial distributions of the reciprocal condition number of the channel which corresponds to the RIS locations are obtained and rank-deficient zones are found in different AP, UE and RIS configurations, which depicts the spatial characteristics of the RIS-assisted MIMO channel. The simulations also indicate that the condition number of the channel not only varies with the RIS location, but is also affected by the antenna array size and orientation of AP and UE. In addition, when the AP has a larger amount of antennas than UE, it is advantageous to place the RIS near the UE rather than the AP to achieve better channel condition. Modulation and coding schemes are applied in the simulations for comparison and capacity improvement is witnessed. Beneficial suggestions for RIS deployment in the MIMO system are concluded according to the simulation results.

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

In this article, we investigate and analyze energy recycling for a reconfigurable intelligent surface (RIS)-aided wireless-powered communication network. As opposed to the existing works where the energy harvested by Internet of Things (IoT) devices only comes from the power station, IoT devices are also allowed to recycle energy from other IoT devices. In particular, we propose group switching- and user switching-based protocols with time-division multiple access to evaluate the impact of energy recycling on the system performance. Two different optimization problems are, respectively, formulated for maximizing the sum throughput by jointly optimizing the energy beamforming vectors, the transmit power, the transmission time, the receive beamforming vectors, the grouping factors, and the phase-shift matrices, where the constraints of the minimum throughput, the harvested energy, the maximum transmit power, the phase shift, the grouping, and the time allocation are taken into account. In light of the intractability of the above problems, we, respectively, develop two alternating optimization-based iterative algorithms by combining the successive convex approximation method and the penalty-based method to obtain corresponding suboptimal solutions. Simulation results verify that the energy recycling-based mechanism can assist in enhancing the performance of IoT devices in terms of energy harvesting and information transmission. Besides, we also verify that the group switching-based algorithm can obtain more sum throughput of IoT devices, and the user switching-based algorithm can harvest more energy.

In this paper, we apply a reconfigurable intelligent surface (RIS) to a two-way (TW) full-duplex (FD) wireless communication system, where a RIS has N reflecting elements and operates as a relay station forwarding signals between two FD terminals. To demonstrate the advantages of RIS compared with traditional relay, we also present the system and signal models of the TW-FD amplify-and-forward relay (AFR) system. We mathematically analyze the performance of the TW-FD-RIS system by deriving the exact closed-form expressions of the average outage probability (AOP) and the average symbol error rate (ASER) in the case N=1, and approximate expressions of the AOP and the ASER in the case N ≥ 2. Besides, we provide the exact closed-form expression of the AOP and approximate expression of the ASER of the TW-FD-AFR system. Monte-Carlo simulations validate all derived mathematical expressions. Numerical results demonstrate that using the RIS greatly improves the TW-FD communication system's performance compared with using amplify-and-forward (AF) relay. Specifically, even the RIS with one element ( N=1), the AOP and ASER of the TW-FD-RIS system are still lower than those of the TW-FD-AFR system in the low signal-to-noise ratio (SNR) regime. However, in the high SNR regime, the AOP and ASER of the TW-FD-RIS system are higher. In the case N ≥ 2, the AOP and ASER of the TW-FD-RIS system are significantly reduced. On the other hand, the impact of residual self-interference (SI) due to FD transmission mode on the performance of TW-FD-RIS and TW-FD-AFR systems is remarkable, making the AOP and ASER of these two systems reach the error floor in the high SNR regime. Fortunately, the usage of RIS with N ≥ 2 can greatly reduce this impact and avoid the error floor for the TW-FD-RIS system.

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.

Recently, reconfigurable intelligent surface (RIS) is emerging as an enabling technology to further enhance the performance of wireless communication systems. RIS is actually a metasurface which is consist of a large number of reflection elements. Each of the elements is capable of re-scatter the incident wave by an independent reflection coefficient. Signals can be redirected to the receiver by delicately assigning reflection coefficients to the elements. Thus a better receiving condition can be realized, and system performance can be enhanced. Existing studies already proved the superiority of general RIS-aided systems, e.g., single-user and multi-user, unicast and multicast, orthogonal and non-orthogonal multiple access. However, most of them considered ideal reflection elements. They assumed that full reflection can be realized at any values of phase-shift, which however is unrealistic due to energy dissipation in reflection elements. To circumvent this problem, in this paper we consider a practical phase shift model where reflection amplitude and phase shift are coupled. Specifically, under this practical model we try to maximize the achievable rate of an RIS-aided MIMO system. An alternating optimization based algorithm is proposed to solve the corresponding optimization problem. Reflection coefficients at the RIS and transmit covariance matrix at the transmitter are optimized alternately with the others being fixed. Simulation results are presented to manifest the validity of the proposed scheme.

The evolution toward 6G wireless networks necessitates innovative solutions to support the massive Internet of Things (IoT) deployments with unprecedented computational and communication requirements, motivating this A comprehensive framework that integrates Reconfigurable Intelligent Surface (RIS) technology with hierarchical aerial computing networks by combining RIS-equipped Unmanned Aerial Vehicles (UAVs) operating as mobile edge computing nodes with High-Altitude Platforms (HAPs) to create a three-tier computing hierarchy addressing the limitations of conventional terrestrial infrastructure. The system model encompasses RIS-equipped UAVs serving terrestrial IoT devices with a single HAP providing high-capacity computational resources, where the RIS phase optimization is formulated as a Riemannian conjugate gradient problem on complex circle manifolds to maximize total system throughput while naturally handling unit modulus constraints through a three-stage sequential decomposition approach. Extensive Monte Carlo simulations demonstrate significant performance improvements over the comparable algorithm without RIS enhancement, with the RIS-enhanced system achieving 18% throughput improvement, near-linear scalability serving approximately 100% of available IoT devices compared to the algorithm In the comparable algorithm at 100 devices, a 95% task completion rate was maintained across all network loads versus 79-80% for the algorithm compared to the comparable algorithm. The results validate the potential of RIS-enabled aerial networks as a transformative solution for scalable and efficient 6G IoT services, with enhanced channel quality from intelligent phase configuration, enabling superior resource utilization and service provisioning in hierarchical computing architectures, establishing key contributions including novel RIS-aerial computing integration, advanced Riemannian manifold optimization with superior convergence properties, unified resource allocation combining stable matching theory with externality elimination, comprehensive performance analysis demonstrating practical viability, and real-world implementation considerations for future multi-UAV scenarios and energy-efficient designs.

Reconfigurable intelligent surface (RIS) technology offers significant promise in enhancing wireless communication systems, but its dual-use potential also introduces substantial security risks. This survey explores the security implications of RIS in next-generation wireless networks. We first highlight the dual-use nature of RIS, demonstrating how its communication-enhancing capabilities can be exploited by adversaries to compromise legitimate users. We identify a new class of security vulnerabilities termed “passive-active hybrid attacks,” where RIS, despite passively handling signals, can be reconfigured to actively engage in malicious activities, enabling various RIS-assisted attacks, such as eavesdropping, man-in-the-middle (MITM), replay, reflection jamming, and side-channel attacks. Furthermore, we reveal how adversaries can exploit the openness of wireless channels to introduce adversarial perturbations in artificial intelligence-driven RIS networks, disrupting communication terminals and causing misclassifications or errors in RIS reflection predictions. Despite these risks, RIS technology also plays a critical role in enhancing security and privacy across radio frequency (RF) and visible light communication (VLC) systems. By synthesizing current insights and highlighting emerging threats, we provide actionable insights into cross-layer collaboration, advanced adversarial defenses, and the balance between security and cost. This survey provides a comprehensive overview of RIS technology’s security landscape and underscores the urgent need for robust security frameworks in the development of future wireless systems.