Search Results (123)

Intelligent reflecting surfaces (IRSs) have attracted extensive attention in the design of future communication networks. However, their large number of reflecting elements still results in non-negligible power consumption and hardware costs. To address this issue, we previously proposed a green heterogeneous IRS (HE-IRS) consisting of both dynamically tunable elements (DTEs) and statically tunable elements (STEs). Compared to conventional IRSs with only DTEs, the unique DTE–STE integrated structure introduces new challenges in optimizing the positions and phase shifts of the two types of elements. In this paper, we investigate the element position and phase shift optimization problems in HE-IRS-assisted millimeter-wave systems. We first propose a particle swarm optimization algorithm to determine the specific positions of the DTEs and STEs. Then, by decomposing the phase shift optimization of the two types of elements into two subproblems, we utilize the manifold optimization method to optimize the phase shifts of the STEs, followed by deriving a closed-form solution for those of the DTEs. Furthermore, we propose a low-complexity phase shift optimization algorithm for both DTEs and STEs based on the Cauchy–Schwarz bound. The simulation results show that with the tailored element position and phase shift optimization algorithms, the HE-IRS can achieve a competitive performance compared to that of the conventional IRS, but with much lower power consumption. Full article

Intelligent reflecting surface (IRS) has emerged as a promising solution to establish propagation paths in non-line-of-sight (NLoS) scenarios, effectively mitigating blockage challenges in direct vehicle-to-infrastructure (V2I) links. This study investigates a time-varying multi-IRS-assisted multiple-input multiple-output (MIMO) communication system, aiming to maximize the system sum rate through the joint optimization of base station (BS) precoding and IRS phase configurations. The formulated problem exhibits inherent non-convexity and time-varying characteristics, posing significant optimization challenges. To address these, we propose a low-complexity dimension-wise sine maximization (DSM) algorithm, grounded in the sum path gain maximization (SPGM) criterion, to efficiently optimize the IRS phase shift matrix. Concurrently, the water-filling (WF) algorithm is employed for BS precoding design. Simulation results demonstrate that compared with traditional methods, the proposed DSM algorithm achieves a 14.9% increase in sum rate, while exhibiting lower complexity and faster convergence. Furthermore, the proposed multi-IRS design yields an 8.7% performance gain over the single-IRS design. Full article

Millimeter wave (mmWave) technology, with its ultra-high bandwidth and low latency, holds significant promise for vehicle-to-everything (V2X) communications. However, it faces challenges such as high propagation losses and limited coverage in dense urban vehicular environments. Intelligent Reflecting Surfaces (IRSs) help address these issues by enhancing mmWave signal paths around obstacles, thereby maintaining reliable communication. This paper introduces a novel Contextual Multi-Armed Bandit (C-MAB) algorithm designed to dynamically adapt beam and IRS selections based on real-time environmental context. Simulation results demonstrate that the proposed C-MAB approach significantly improves link stability, doubling average beam sojourn times compared to traditional SNR-based strategies and standard MAB methods, and achieving gains of up to four times the performance in scenarios with IRS assistance. This approach enables optimized resource allocation and significantly improves coverage, data rate, and resource utilization compared to conventional methods. Full article

In this paper, we investigate a simultaneous wireless information and power transfer (SWIPT) communication system enhanced by an intelligent reflecting surface (IRS). Our study takes into account the imperfections in the phase shift of the IRS and the presence of interfering signals reflected by the IRS at the destination terminal. Additionally, our analysis incorporates both the presence of a line-of-sight path between the source and destination and a non-linear energy-harvesting model. In order to assess the influence of phase error and interference on the considered system, closed-form and asymptotic expression for the system’s outage probability, ergodic capacity, and energy efficiency (EE) are derived. Simulation results are presented to corroborate our analysis and illustrate the impact of phase error, interference, the number of reflecting elements, and various system parameters on the system performance. Full article

Traditional wireless positioning techniques often suffer from accuracy degradation in indoor environments due to multipath effects and occlusion. To address this issue, this paper proposes an indoor positioning method for visible light communication (VLC) combined with intelligent reflective surface (IRS) assistance to improve the positioning accuracy and stability in complex environments. This work proposes the concepts of a virtual source and virtual receiver based on IRS and conducts positioning optimization by combining the measurements of the time difference of arrival (TDOA) and angle of arrival (AOA). The research adopts a semi-positive definite relaxation (SDR) optimization method to efficiently solve the nonlinear optimization problem, ensuring the global convergence and accuracy of the algorithm. Meanwhile, the weights of the positioning results of the virtual light source and the real light source are dynamically adjusted by using the distance residual, thereby reducing the influence of measurement noise. Monte Carlo simulation experiments demonstrate the advantages of the proposed method in terms of the positioning error and robustness compared with traditional positioning algorithms in the environment of large noise interference. The experimental results demonstrate the efficacy of the method in addressing multipath and occlusion issues, while also exhibiting notable adaptability and stability across diverse hardware configurations. Full article

In 5G-and-beyond (B5G) Internet of Things (IoT) networks, the integration of intelligent reflecting surfaces (IRSs) with millimeter-wave (mmWave) multiple-input multiple-output (MIMO) techniques can significantly improve signal quality and increase network capacity. However, a single fixed IRS lacks the dynamic adjustment capability to flexibly adapt to complex environmental changes and diverse user demands, while mmWave MIMO is constrained by limited coverage. Motivated by these challenges, we investigate the application of drone-mounted IRS-assisted MIMO communications in B5G IoT networks, where multiple IRS-equipped drones are deployed to provide real-time communication support. To fully exploit the advantages of the proposed MIMO-enabled air-to-ground integrated information transmission framework, we formulate a joint optimization problem involving beamforming, phase shift design, and drone deployment, with the objective of maximizing the sum of achievable weighted data rates (AWDRs). Given the NP-hard nature of the problem, we develop an iterative optimization algorithm to solve it, where the optimization variables are tackled in turn. By employing the quadratic transformation technique and the Lagrangian multiplier method, we derive closed-form solutions for the optimal beamforming and phase shift design strategies. Additionally, we optimize drone deployment by using a distributed discrete-time convex optimization approach. Finally, the simulation results show that the proposed scheme can improve the sum of AWDRs in comparison with the state-of-the-art schemes. Full article

In this paper, we present a practical partitioned intelligent-reflecting-surface-aided transmit spatial modulation (PIRS-TSM) scheme, where spatial modulation is implemented at the transmitter and partitioning is conducted on the IRS to enhance the spectral efficiency (SE) and reliability for multiple-input single-output (MISO) systems. The theoretical analysis of average bit error rate (ABER) based on maximum likelihood (ML) detection and the computational complexity analysis are provided. Experimental simulations demonstrate that the PIRS-TSM scheme obtains a significant ABER enhancement under the same SE compared to the existing partitioned IRS-aided transmit space shift keying or generalized space shift keying schemes by additionally carrying modulated symbols. Moreover, the system performances with different configurations of antenna numbers and symbol modulation orders under the same SE are investigated as a practical application reference. Full article

A wireless-powered communication network (WPCN) provides sustainable power solutions for energy-intensive Internet of Things (IoT) devices in remote or inaccessible locations. This technology is particularly beneficial for applications in smart transportation and smart cities. Nevertheless, WPCN experiences performance degradation due to severe path loss and inefficient long-range energy and information transmission. To address the limitation, this paper investigates an intelligent reflecting surface (IRS)-enhanced multi-cell WPCN integrated with non-orthogonal multiple access (NOMA). The emerging IRS technology mitigates propagation losses through precise phase shift adjustments with symmetric reflective components. Asymmetric resource utilization in symmetric downlink and uplink transmissions is crucial for optimal throughput and quality of service. Alternative iterations are employed to optimize time allocation and IRS phase shifts in both downlink and uplink transmissions. This approach allows for the attainment of maximum sum throughput. Specifically, the phase shifts are optimized using two algorithms called semidefinite relaxation (SDR) and block coordinate descent (BCD). Our simulations reveal that integrating the IRS into multi-cell NOMA-WPCN enhances user throughput. This surpasses the performance of traditional multi-cell WPCN. In addition, the coordinated deployment of multiple hybrid access points (HAPs) and IRS equipment can expand communications coverage and network capacity. Full article

In this work, we study the security performance of a double intelligent reflecting surface non-orthogonal multiple access (DIRS-NOMA) wireless communication system supporting communication for a group of two NOMA users (UEs) at the edge, with the existence of an eavesdropping device (ED). We also assume that there is no direct connection between the BS and the UEs. From the proposed model, we compute closed-form expressions for the secrecy outage probability (SOP) and the average security rate (ASR) for each UE. After that, we discuss and analyze the system security performance according to the NOMA power allocation for each user and the number of IRS counter-emission elements. In addition, we analyze the SOP of both the considered DIRS-NOMA and conventional NOMA systems to demonstrate that DIRS-NOMA systems have much better security than conventional NOMA systems. Based on the analytical results, we develop an ASR optimization algorithm using the alternating optimization method, combining NOMA power allocation factor optimization and IRS passive beam optimization through the Lagrange double transform. The derived analytical expressions are validated through Monte Carlo simulations. Full article

Due to their distinct economic efficiency and adaptability advantages, Unmanned Aerial Vehicles (UAVs) can serve as mobile data collectors, collecting data from Internet of Things Devices (IoTDs). As a promising emerging technology, the Intelligent Reflecting Surface (IRS) holds the potential to overcome architectural barriers and improve communication quality in urban environments. This study investigates the development of an IoT data collection system tailored for urban environments, leveraging the synergistic operation of multiple UAVs and IRSs. In light of the limited coverage capacity of an individual IRS, we deploy several IRSs, with multiple UAVs stationed at various base stations (BSs) to collect data from IoTDs. We propose a grouping genetic algorithm-independent double deep-Q network-alternating optimization (GGA-IDDQN-AO) approach, aiming to minimize the average mission completion time for a mission cycle. This approach optimizes both the deployment and mission allocation strategies of UAVs using the grouping genetic algorithm. Additionally, by integrating deep reinforcement learning with the alternating optimization algorithm, the flight trajectories of UAVs and IRSs’ phase shifts are fine-tuned. The effectiveness of the GGA-IDDQN-AO approach is validated through comprehensive simulations, which demonstrate that the integration of IRSs leads to a notable performance enhancement in the IoT data collection system. Full article

The present study is concerned with the power transfer efficiency enhancement using a combination of the multi-hop node (MHN) and the Intelligent Reflecting Surface (IRS)-based passive beamforming technologies. The primary objective is to ensure a high RF-DC converter power conversion efficiency (PCE) used at the receiving end, which is difficult to achieve due to path loss and multi-path propagation. An electronically tunable reconfigurable reflectarray (RRA) designed to operate at the sub-GHz ISM band (865.5 MHz) is utilized to implement the IRS concept. Both the MHN and RRA were developed and studied in our earlier research. The RRA redirects the reflected power-carrying wave amplified by the MHN toward the intended receiver. It comprises two layers: the RF layer containing tunable phase shifters and the ground plane. Each phase shifter comprises two identical eight-shaped metal patches coupled by a pair of varactor diodes used to achieve the reflection phase tuning. The phase gradient method is used to synthesize the RRA phase profiles, ensuring different desired reflection angles. The RRA prototype, composed of 36 phase shifters, is employed in conjunction with the MHN equipped with two antennas and an amplifier. The RRA parameter optimization is accomplished by randomly varying the varactor diode voltages and measuring the corresponding received power levels until the power reflected in the desired direction is maximized. Two measurement scenarios are examined: power transmission without and with the MHN. In the first scenario, the received power is calculated and measured at several distinct beam steering angles for different distances between the Tx antenna and RRA. The same procedure is applied to different distances between the RRA and MHN in the second scenario. The effect of slight deviations in the operating frequency from the designed one (865.5 MHz) on the RRA performance is also examined. Additionally, the received power levels for both scenarios are estimated via full-wave analysis performed using the full-wave simulation software Ansys HFSS 2023 R1. A Huygens’ surface equivalence principle-based model decomposition method was developed and employed to reduce the CPU time. The calculated results are consistent with the measured ones. However, some discrepancies attributed to the adverse effect of RRA diode biasing lines, manufacturing tolerances, and imperfection of the indoor environment model are observed. Full article

This paper presents an innovative approach to improving visible light communication (VLC) systems in total shadowing conditions by integrating intelligent reflecting surfaces (IRSs), angle diversity receivers (ADRs), and recurrent neural networks (RNNs). Two ADR configurations (pyramidal and hemispherical) are evaluated, along with signal combination mechanisms: maximum ratio combining (MRC) and select best combining (SBC). The RNN is employed to dynamically optimize the IRS placement, maximizing the signal-to-noise ratio (SNR) at the ADRs and enhancing overall system performance in non-line-of-sight (NLoS) scenarios. This study investigates the spatial distribution of SNRs in VLC systems using RNN-optimized IRSs, comparing the performance of different ADR configurations and signal combination methods. The results demonstrate significant improvements in received power and the SNR compared to non-optimized setups, showcasing the effectiveness of RNN-based optimization for robust signal reception. This article highlights the potential of machine learning in enhancing VLC technology, offering a practical solution for real-world indoor applications. The findings emphasize the importance of adaptive IRS placement and spur further exploration of advanced algorithms and ADR designs to address challenges in complex indoor environments. Full article

The fifth generation (5G) of wireless communication is in its finalization stage and has received favorable reception in many nations. However, research is now geared towards the anticipated sixth-generation (6G) wireless network. The new 6G promises even more severe performance criteria than the current 5G generation. New sophisticated technologies and paradigms are expected to be incorporated into the 6G network designs and procedures to meet the ever-dynamic user needs and standards. These 6G-enabling technologies include digital twin (DT), intelligent reflecting surface (IRS), visible light communication (VLC), quantum computing (QC), blockchain, unmanned aerial vehicles (UAVs), and non-orthogonal multiple access (NOMA), among others. Optimal network performance requires that machine learning (ML) techniques be integrated over the 6G wireless network to provide solutions to highly complex networking problems, massive users, high overhead, and computational complexity. Consequently, we have provided a state-of-the-art overview of wireless network generations leading to the future 6G, and huge emphases have been laid on ML’s role in optimization applications for different enabling 6G technologies. Several key performance indicators for the different application scenarios have been highlighted. ML has proved to significantly improve the performance of the existing 6G-enabling technologies, and choosing the appropriate approach can ultimately yield optimal results. Full article

Intelligent reflecting surfaces (IRSs) have emerged as a promising technology to enhance link reliability in a cost-effective manner, especially for line-of-sight (LOS) link blocking caused by obstacles. In this paper, we investigate an IRS-assisted single-cell photon-counting communication system in the presence of building shadows, where one IRS is deployed to assist the communication between a multi-antenna base station (BS) and multiple single-antenna users. Photon counting has been widely adopted in sixth-generation (6G) optical communications due to its exceptional detection capability for low-power optical signals. However, the correlation between signal and noise complicates analyses. To this end, we first derive the channel gain of the IRS-assisted MIMO system, followed by the derivation of the mean square error (MSE) of the system using probabilistic methods. Given the constraints of the transmit power and IRS configuration, we propose an optimization problem aimed at minimizing the MSE of the system. Next, we present an alternating optimization (AO) algorithm that transforms the original problem into two convex subproblems and analyze its convergence and complexity. Finally, numerical results demonstrate that the IRS-assisted scheme significantly reduces the MSE and bit error rate (BER) of the system, outperforming other baseline schemes. Full article

In this paper, we explore intelligent reflecting surface (IRS)-assisted physical layer security (PLS) in a wireless-powered Internet of Things (IoT) network (WPIN) by combining an IRS, a friendly jammer, and energy harvesting (EH) to maximize sum secrecy throughput in the WPIN. Specifically, we propose a non-line-of-sight system where a hybrid access point (H-AP) has no direct link with the users, and a secure uplink transmission scheme utilizes the jammer to combat malicious eavesdroppers. The proposed scheme consists of two stages: wireless energy transfer (WET) on the downlink (DL) and wireless information transmission (WIT) on the uplink (UL). In the first phase, the H-AP sends energy to users and the jammer, and they then harvest energy with the help of the IRS. Consequently, during WIT, the user transmits information to the H-AP while the jammer emits signals to confuse the eavesdropper without interfering with the legitimate transmission. The phase-shift matrix of the IRS and the time allocation for DL and UL are jointly optimized to maximize the sum secrecy throughput of the network. To tackle the non-convex problem, an alternating optimization method is employed, and the problem is reformulated into two sub-problems. First, the IRS phase shift is solved using quantum particle swarm optimization (QPSO). Then, the time allocation for DL and UL are optimized using the bisection method. Simulation results demonstrate that the proposed method achieves significant performance improvements as compared to other baseline schemes. Specifically, for IRS elements N = 35, the proposed scheme achieves a throughput of 19.4 bps/Hz, which is 85% higher than the standard PSO approach and 143% higher than the fixed time, random phase (8 bps/Hz) approach. These results validate the proposed approach’s effectiveness in improving network security and overall performance. Full article