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In this paper, passive Intelligent Reflecting Surface (IRS) is used to enhance the performance of a Full Duplex (FD) bidirectional Machine Type Communication (MTC) system with two source nodes. Each node is equipped with two antennas to operate in FD mode. In reality, self-interference and discrete phase shifting are two major impairments in FD and IRS-assisted communication, respectively. The self-interference at source nodes operating in FD mode is mitigated by increasing the number of meta-surface elements at the IRS. Bit Error Rate (BER) and outage performances are analyzed with continuous phase shifting and discrete phase shifting in IRS. Closed-form analytical expressions are derived for the outage probability and BER performances of the IRS-assisted bidirectional FD-MTC system with a continuous phase shifter. The outage and BER performances of the IRS-assisted bidirectional MTC system in the FD mode have Signal-to-Noise Ratio (SNR) improvement compared with the IRS-assisted bidirectional MTC system in Half Duplex (HD) mode, as the number of reflecting elements in IRS is doubled in the FD mode. The outage and BER performances are degraded by a discrete phase shifter. Hence, performance degradation of the proposed IRS-assisted bidirectional FD-MTC is examined for 1-bit shifter (0, π), 2-bit shifter (0, π /2, π , 3 π /2), and for 3-bit shifter (0, π /4, π /2, 3 π /4, π , 5 π /4, 3 π /2, 7 π /4). The performance degradation when a discrete phase shifter is employed in IRS is compared with the ideal continuous phase shifter in IRS. Further, achievable rate analysis is carried out for finding the best location of the IRS in a bidirectional FD-MTC system. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) acts as a significant player in enhancing the spectral efficiency (SE) of wireless telecommunications; simultaneously, the intelligent reflecting surface (IRS) technique is a valid technique for increasing the confidentiality properties of wireless telecommunications systems through the modulation of the amplitude and phase shift of the channel. Therefore, we take into consideration an IRS-assisted multiple-input single-output (MISO) CR system to raise the confidentiality rate, which is composed of a primary network with a primary receiver (PR) and an eavesdropping link, as well as a secondary network with a secondary receiver (SR) and SR transmitter (SR-TX). In particular, we minimize the SR's transmit power under the interference temperature (IT) and confidentiality capacity constraints via the joint optimization of the beamforming vector and artificial noise (AN) constraint matrix at SR-TX together with the phase shift matrix of IRS. Numerical outcomes indicate that various transmit antenna values and the IRS element numbers at SR-TX can greatly reduce transmit power while assuring secure communication. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As a new technology for reconstructing communication environments, intelligent reflecting surfaces (IRSs) can be applied to UAV communication systems. However, some challenges exist in IRS-assisted UAV communication system design, such as physical layer security issues, IRS design, and power consumption issues owing to the limitation of the hardware. Therefore, a secrecy capacity optimization scheme for an active IRS-assisted unmanned aerial vehicle (UAV) communication system is proposed to solve multi-user security issues. In particular, controllable power amplifiers are integrated into reflecting units to solve the problem of blocked links, and the UAV can dynamically select the served user according to the channel quality. In order to maximize the system average achievable secrecy capacity and ensure the power constraints of the UAV and active IRS, user scheduling, UAV trajectory, beamforming vector, and reflection matrix are jointly optimized, and the block coordinate descent (BCD) algorithm is applied to solve this non-convex problem. Simulation results show that the active IRS-assisted UAV communication scheme can significantly weaken the "multiplicative fading" effect and enhance the system secrecy capacity by 55.4% and 11.9% compared with the schemes with passive IRS and without optimal trajectory, respectively. [ABSTRACT FROM AUTHOR]

This paper considers the jointly active and passive beamforming design in the IRS-aided MISO downlink vehicle communication system where both V2I and V2V communication paradigms coexist. We formulate the problem as an optimization problem aiming to minimize the total transmit power of the base station subject to SINR requirements of both V2I and V2V users, total transmit power of base station and IRS's phase shift constraints. To deal with this non-convex problem, we propose a method which can alternately optimize the active beamforming at the base station and the passive beamforming at the IRS. By using first-order Taylor expansion, matrix analysis theory and penalized convex-concave process method, the non-convex optimization problem with coupled variables is converted into two decoupled convex sub-problems. The simulation results show that the proposed alternate optimization algorithm can significantly decrease the total transmit power of the vehicle base station. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) serves as a crucial element in interconnecting diverse devices within the realm of smart technology. However, the energy consumption of IoT technology has become a notable challenge and an area of interest for researchers. With the aim of achieving an IoT with low power consumption, green IoT has been introduced. The use of unmanned aerial vehicles (UAVs) represents a highly innovative approach for creating a sustainable green IoT network. UAVs offer advantages in terms of flexibility, mobility, and cost. Moreover, device-to-device (D2D) communication is essential in emergency communications, due to its ability to support direct communication between devices. The intelligent reflecting surface (IRS) is also a hopeful technology which reconstructs the radio propagation environment and provides a possible solution to reduce co-channel interference resulting from spectrum sharing for D2D communications. The investigation in this paper hence focuses on energy-efficient UAV-IRS-assisted D2D communications for green IoT. In particular, a problem of optimization aimed at maximizing the system’s average energy efficiency (EE) is formulated, firstly, by simultaneously optimizing the power coefficients of all D2D transmitters, the UAV’s trajectory, and the base station (BS)’s active beamforming, along with the IRS’s phase shifts. Second, to address the problem, we develop a multi-agent twin delayed deep deterministic policy gradient (MATD3)-based scheme to find a near-optimal solution, where D2D transmitters, the BS, and the UAV cooperatively learn to improve EE and suppress the interference. To conclude, numerical simulations are conducted to assess the availability of the proposed scheme, and the simulation results demonstrate that the proposed scheme surpasses the baseline approaches in both convergence speed and EE performance.

To improve the energy and information transmission efficiency of relay-based wireless powered communication network (WPCN), a two intelligent reflecting surface (IRS) assisted dual-hop relay wireless transmission scheme was proposed.In particular, IRS1 and IRS2 were deployed between the base station (BS) and hybrid relay node (HRN), and between the HRN and users, respectively, which could improve the efficiency of energy transfer from the HRN to users, data transmission from users to the HRN and data forwarding from HRN to BS.To maximize the system throughput, an optimization problem was formulated, where the time scheduling of energy, information transmission and forwarding, the phase shifts of IRS, and the delivered power of users were jointly optimized.To address non-convexity of the formulated problem, an efficient alternating optimization algorithm was proposed and the sub-optimal solution was obtained.In addition, the convergence and computational complexity of the proposed algorithm were analyzed.Numerical results verify that IRS can effectively improve the throughput of WPCN based on relay.In addition, compared to the benchmark schemes, the proposed optimal transmission scheme can significantly improve the system performance in terms of throughput.

We consider MIMO systems with self-sustainable intelligent reflecting surface (IRS), where IRS can harvest energy from received power. We characterize the achievable region of rate-energy pairs and propose an alternating optimization algorithm to try to achieve its boundary points. We apply the coordinate descent method to find the optimal reflecting coefficients and provide the closed-form expression to each subproblem. Numerical results show that the proposed algorithm archives higher rate than the other algorithms for given required harvested energy.

The intelligent reflecting surface (IRS) is a cutting-edge technology for cost-effectively achieving future spectrum- and energy-efficient wireless communication. In particular, an IRS comprises many low-cost passive devices that can independently reflect the incident signal with a configurable phase shift to produce three-dimensional (3D) passive beamforming without transmitting Radio-Frequency (RF) chains. Thus, the IRS can be utilized to greatly improve wireless channel conditions and increase the dependability of communication systems. This article proposes a scheme for an IRS-equipped GEO satellite signal with proper channel modeling and system characterization. Gabor filter networks (GFNs) are jointly proposed for the extraction of distinct features and the classification of these features. Hybrid optimal functions are used to solve the estimated classification problem, and a simulation setup was designed along with proper channel modeling. The experimental results show that the proposed IRS-based methodology provides higher classification accuracy than the benchmark without the IRS methodology. [ABSTRACT FROM AUTHOR]

In order to improve the performance of the mobile edge computing (MEC) system, the intelligent reflecting surface (IRS) has recently been included. This paper investigates the computation performance of an IRS‐aided MEC system, where an access point services multi‐MEC devices by utilizing two distributed IRSs to operate a partial offloading strategy. Through collaboratively constructing the cooperative passive beamforming at the two IRSs, the central processing unit (CPU) frequency, the offloading time allocation, and the transmit power of users, an optimization problem is constructed to maximize the sum computation rate of the proposed system. To tackle this non‐convex issue, the authors first illustrate that the design of passive beamforming is independent to optimize other variables and propose to solve it alternatively. Then the joint optimization problem of other parameters is proved to be a convex problem and is resolvable by adopting the Lagrange dual approach. Compared with the benchmark schemes, the simulation outcomes demonstrated that the performance of the proposed system is vastly improved by deploying two distributed IRSs. [ABSTRACT FROM AUTHOR]

Future wireless networks are characterized as having a combination of various technologies such as artificial intelligence, machine learning, combined communication, sensing and others. In this context, intelligent reflecting surfaces have been identified as a powerful candidate for 6G enabling technologies. In this paper, we present the performance analysis of an intelligent reflecting surface (IRS)-assisted narrowband single-input single-output (SISO) system. We evaluate the capacity and received signal-to-noise ratio (SNR) of a SISO wireless system by considering the effect of amplitude response of the channel and changes in transmit SNR. The simulation results show that the capacity of the system can be significantly improved in the presence of passive, as well as active, reflecting elements even when the strength of the direct link between the transmitter and receiver is very low. Similarly, transmit SNR has a significant impact on the overall performance improvement of the system. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Wireless communication system are facing more and more security threats, and protecting user privacy becomes important. The goal of covert communication is to hide the existence of legitimate transmission as a practical approach. Inspired by the great success of deep reinforcement learning (DRL) on handling challenging optimization problems, DRL is used to optimize covert communication performance. To achieve this, a model-free and off-policy deep deterministic policy gradient (DDPG) algorithm is proposed to maximize the covert rate under covert constraint. The transmit beamformer vector of legitimate transmitter and the phase shifts matrix of intelligent reflecting surfaces (IRS) are the outputs of DRL neural networks. The DRL can learn from the environment and adjust transmit beamformer vector and phase shifts matrix to maximize covert communication performance. Simulation results demonstrate that the proposed DDPG algorithm can achieve comparable performance with two benchmarks algorithm. [ABSTRACT FROM AUTHOR]

We propose short packet communication in an underlay cognitive radio network assisted by an intelligent reflecting surface (IRS) composed of multiple reconfigurable reflectors. This scheme, called the IRS protocol, operates in only one time slot (TS) using the IRS. The IRS adjusts its phases to give zero received cumulative phase at the secondary destination, thereby enhancing the end‐to‐end signal‐to‐noise ratio. The transmitting power of the secondary source is optimized to simultaneously satisfy the multi‐interference constraints, hardware limitations, and performance improvement. Simulation and analysis results of the average block error rates (BLERs) show that the performance can be enhanced by installing more reconfigurable reflectors, increasing the blocklength, lowering the number of required primary receivers, or sending fewer information bits. Moreover, the proposed IRS protocol always outperforms underlay relaying protocols using two TSs for data transmission, and achieves the best average BLER at identical transmission distances between the secondary source and secondary destination. The theoretical analyses are confirmed by Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

In this paper, we consider an integrated sensing and communications system assisted by a reconfigurable intelligent surface (RIS). A small number of sparsely placed active sensors are applied in the RIS to perform effective channels and, thereby, enable optimized beamforming for both communications and sensing objectives, namely, establishing reliable communication links with communication users (CUs) and effectively localize targets. The time-varying multipath channels between the RIS and the CU as well as the time-varying channel between the RIS and the targets are estimated by exploiting an interpolated Hermitian and Toeplitz covariance matrix followed by direction-of-arrival estimation using the MUSIC algorithm. Based on such results, we jointly optimize the transmit beamformer at the base station and the unit-modulus RIS passive beamformer. The RIS beamformer is optimized to maximize its minimum beam-pattern gain towards the desired sensing angles subject to the minimum signal-to-noise ratio requirement at the CU. Simulation results verify the effectiveness of the proposed approach, and the performance of different sparse array configurations is compared. [ABSTRACT FROM AUTHOR]

A joint beamforming design method assisted by an IRS was proposed in MIMO Internet of vehicles system where the spectrum was shared by V2I and V2V users. Considering the constraints of V2V user's data rate requirement, transmit power and IRS reflection phase shift, transmit precoding matrix at base station and reflective phase shift matrix of the IRS were jointly optimized to maximize channel capacity of the V2I user. The coupled non-convex optimization problem was decoupled and converted to be convex by using minimum mean square error, matrix analysis and inner approaching algorithm. An alternate iterative optimization algorithm was proposed to find the feasible solution. The performance comparisons of the proposed algorithm with several benchmarks were carried out and the impacts of the number of IRS reflective elements, IRS location and vehicle speed on spectrum efficiency were provided, respectively. The simulation results show that the proposed algorithm can converge quickly and the spectrum efficiency can be maximally improved by using the proposed method if the IRS is deployed near the base station. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Intelligent reflecting surface (IRS), in which a large number of tunable reflective elements are employed, can enhance the wireless propagation environment in an acceptable manner. Although this issue is operated via intelligently reflecting the electromagnetic waves from the base-station (BS) toward the users, the optimal tuning of the phase shifters at the IRS is arduous. In other words, the passive nature of reflective elements causes some challenges in direct measuring the channels between the IRS, the BS and the users. Even though the conventional methods first estimate the channels and then optimize the system parameters, this paper utilizes an artificial intelligence procedure in which channel estimation has been carried out considering the levenberg-marquardt training algorithm. This is attained via performing some initial processes and trained and estimated each part at different neural network architectures. The estimated output of the system can be expressed via parameters of artificial neural networks which is an important stage for channel estimation. The aforesaid technique is not only making full use of the learning property of neural network, but also by applying this method, there is no need of any matrix computation and the proposed technique is less complex than the traditional pilot-based techniques. Simulation results demonstrate that the offered technique exhibits premiere performance in comparison with conventional common methods. [ABSTRACT FROM AUTHOR]

As a revolutionizing technology, intelligent reflecting surfaces (IRS) are expected to enhance the physical layer security (PLS) by constructing a controllable wireless environment. In this paper, we consider an IRS-aided visible light communication (VLC) system with a legitimate user and a malicious eavesdropper, in which the IRS is implemented by an intelligent rotatable mirror array. Our objective is to provide a desired optical reflecting environment with enriched spatial degrees of freedom by jointly optimizing the rotation angles of arrayed mirrors. First, from the perspective of information theory, we derive the achievable secrecy rate for the IRS-aided VLC system. Then, we formulate an achievable secrecy rate maximization problem with the orientations of mirror elements as optimization variables. Since the effect of mirror elements' orientations on the IRS channel gain makes the formulated problem hard to solve, we propose a reflected spot search (RSS) method to transform the original problem into one with reduced dimension of optimization variables. Besides, bearing in mind that the VLC system has the dual task of communication and illumination, we define and analyze the illumination uniformity of the IRS-aided secure VLC system. Our simulation results show that by utilizing the proposed method, the VLC system can achieve significant secrecy enhancement, especially when the eavesdropper is close to the legitimate user. • The achievable secrecy rate for the mIRS-aided VLC system is derived. • An RSS method is proposed to handle the secrecy rate maximization problem. • Illumination uniformity is analyzed for the mIRS-aided secure VLC system. [ABSTRACT FROM AUTHOR]

In large-scale massive MIMO systems with intelligent reflecting surfaces (IRS), dynamic channel estimation (CE) is essential for optimizing the system performance and ensuring reliable communication. Traditional channel estimation techniques are not suitable for IRS-assisted systems due to the unique characteristics of Intelligent Reflecting Surfaces channels. To address the channel estimation problem in such dynamic environments, this paper introduces two novel channel estimation methods: Khatri-Rao Factorization (KRF) and Bilinear Alternating Least Squares (BALS). The first method uses KRF to efficiently solve rank-1 matrix approximation problems with a closed-form solution. The second method employs an iterative alternating estimation scheme. By disentangling these key channel matrices' estimates, both methods provide more accurate and robust channel estimation, essential for optimizing communication system performance in challenging environments. The proposed CE-KRF-BALS-MIMO method is evaluated under performance metrics like Bit error rate (BER), Signal Noise Ratio (SNR), Normalized Mean Square Error (NMSE) Spectral Efficiency (SE), and Computational Complexity. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless communication networks and Internet of Things technology (IoT), higher requirements have been put forward for spectrum resource utilization and system performance. In order to further improve the utilization of spectrum resources and system performance, this paper proposes an intelligent reflecting surface (IRS)-assisted fair energy allocation algorithm for cognitive multiple-input single-output (MISO) wireless-powered networks. The goal of this paper is to maximize the minimum energy receiving power in the energy receiver, which is constrained by the signal-to-interference-plus-noise ratio (SINR) threshold of the information receiver in the secondary network, the maximum transmission power at the cognitive base station (CBS), and the interference power threshold of the secondary network on the main network. Due to the coupling between variables, this paper uses iterative optimization algorithms to optimize and solve different variables. That is, when solving the active beamforming variables, the passive beamforming variables are fixed; then, the obtained active beamforming variables are fixed, and the passive beamforming variables are solved. Through continuous iterative optimization, the system converges. The simulation results have verified the effectiveness of the proposed algorithm.

This paper investigates an intelligent reflecting surface (IRS)-aided integrated sensing and communication (ISAC) framework to cope with the problem of spectrum scarcity and poor wireless environment. The main goal of the proposed framework in this work is to optimize the overall performance of the system, including sensing, communication, and computational offloading. We aim to achieve the trade-off between system performance and overhead by optimizing spectrum and computing resource allocation. On the one hand, the joint design of transmit beamforming and phase shift matrices can enhance the radar sensing quality and increase the communication data rate. On the other hand, task offloading and computation resource allocation optimize energy consumption and delay. Due to the coupled and high dimension optimization variables, the optimization problem is non-convex and NP-hard. Meanwhile, given the dynamic wireless channel condition, we formulate the optimization design as a Markov decision process. To tackle this complex optimization problem, we proposed two innovative deep reinforcement learning (DRL)-based schemes. Specifically, a deep deterministic policy gradient (DDPG) method is proposed to address the continuous high-dimensional action space, and the prioritized experience replay is adopted to speed up the convergence process. Then, a twin delayed DDPG algorithm is designed based on this DRL framework. Numerical results confirm the effectiveness of proposed schemes compared with the benchmark methods.

A joint beamforming design scheme for distributed intelligent reflecting surface (IRS) assisted multi-user communication was proposed to solve the reliable communication problem in the downlink of millimeter-wave multi user-multiple input single output (MU-MISO) system in non line of sight scenarios.Considering the power and constant-mode constraints, the active beamforming of the base station and passive beamforming of multiple IRS were modeled as a joint non-convex optimization problem with the objective of user weighting and sum rate maximization.A closed-form fractional programming technique was used to decouple this joint optimization problem into an easily solvable equivalent problem.A non-convex block coordinate descent algorithm was used to alternately optimize the active beamforming and passive beamforming matrices according to the prox-linear rule and the distributed successive convex approximation rule.The convergence proof and complexity analysis of the proposed algorithm were also given.The simulation results demonstrate that the algorithm can converge fast, and can effectively improve the system transmission rate with reduced complexity compared with the two baseline algorithms.

To meet the demand of high spectrum and energy efficiency in future wireless communication systems, intelligent reflecting surface (IRS), a promising technology has attracted significant attention.By proper design of IRS, PLS can be boosted from different perspectives.Based on that, current research about IRS-assisted PLS enhancement from various aspects, e.g., information-theoretic secrecy, covert communication, secret key generation and so on, were summarized.The system features and key solutions of the related papers were reviewed and several future research directions were discussed.

Physical layer secret key generation (PLKG) is a promising technology for establishing effective secret keys. Current works for PLKG mostly study key generation schemes in ideal communication environments with little or even no signal interference. In terms of this issue, exploiting the reconfigurable intelligent reflecting surface (IRS) to assist PLKG has caused an increasing interest. Most IRS-assisted PLKG schemes focus on the single-input-single-output (SISO), which is limited in future communications with multi-input-multi-output (MIMO). However, MIMO could bring a serious overhead of channel reciprocity extraction. To fill the gap, this paper proposes a novel low-overhead IRS-assisted PLKG scheme with deep learning in the MIMO communications environments. We first combine the direct channel and the reflecting channel established by the IRS to construct the channel response function, and we propose a theoretically optimal interaction matrix to approach the optimal achievable rate. Then we design a channel reciprocity-learning neural network with an IRS introduced (IRS-CRNet), which is exploited to extract the channel reciprocity in time division duplexing (TDD) systems. Moreover, a PLKG scheme based on the IRS-CRNet is proposed. Final simulation results verify the performance of the PLKG scheme based on the IRS-CRNet in terms of key generation rate, key error rate and randomness. [ABSTRACT FROM AUTHOR]

In this study, the performance of intelligent reflecting surfaces (IRSs) with a discrete phase shift strategy is examined in multiple-antenna systems. Considering the IRS network overhead, the achievable rate model is newly designed to evaluate the practical IRS system performance. Finding the optimal resolution of the IRS discrete phase shifts and a corresponding phase shift vector is an NP-hard combinatorial problem with an extremely large search complexity. Recognizing the performance trade-off between the IRS passive beamforming gain and IRS signaling overheads, the incremental search method is proposed to present the optimal resolution of the IRS discrete phase shift. Moreover, two low-complexity sub-algorithms are suggested to obtain the IRS discrete phase shift vector during the incremental search algorithms. The proposed incremental search-based discrete phase shift method can efficiently obtain the optimal resolution of the IRS discrete phase shift that maximizes the overhead-aware achievable rate. Simulation results show that the discrete phase shift with the incremental search method outperforms the conventional analog phase shift by choosing the optimal resolution of the IRS discrete phase shift. Furthermore, the cumulative distribution function comparison shows the superiority of the proposed method over the entire coverage area. Specifically, it is shown that more than 20 % of coverage extension can be accomplished by deploying IRS with the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Covert communications is critical in many application scenarios for ensuring transmission security and privacy. Two-way protocols are widely adopted in intelligent reflecting surface (IRS) assisted relaying networks to enable covert communications and prevent wireless signals from being overheard. To quantify the performance of covert communications in this special scenario, we derive the closed-form expression of outage probability and its asymptote in this paper. Considering the worst-case of covert communications, the optimal normalized power threshold of warden's detector is analyzed under a complex Gaussian distribution approximation. To meet the requirement of the detection error probability, the ratio of the transmission power between the covert nodes is investigated. Simulation results are provided to validate the theoretical expressions as well as the asymptotic analysis with a small number of reflecting elements. [ABSTRACT FROM AUTHOR]

In this paper, we consider deploying an intelligent reflecting surface (IRS) to enhance the downlink (DL) energy transfer and uplink (UL) information transmission efficiency for secure multiple-input multiple-output (MIMO) wireless powered communication networks (WPCNs). We aim to maximize the secrecy throughput of all users by jointly optimizing the DL/UL time allocation, the energy transmit covariance matrix of hybrid access point (AP), the information transmit beamforming matrix of users and the phase shifts of IRS in DL/UL, subject to constraints of energy/information transmit power at the hybrid AP/users and that of unit-modulus IRS phase shifts for DL/UL. To tackle the non-convex problem, we first transform the original problem into an equivalent form based on the mean-square error (MSE) method given time allocation, and then apply the alternating algorithm to update the optimization variables iteratively. Specifically, the energy covariance matrix and the information beamforming matrix are obtained based on the dual subgradient method. For the IRS phase shifts, we investigate two IRS beamforming reflection setups, namely different DL/UL IRS beamforming and identical DL/UL IRS beamforming. For the former case, the second-order cone programming technique and the Majorization-Minimization algorithm/element by element iterative algorithm are applied to obtain the DL and UL IRS phase shifts, respectively. For the latter case, the IRS phase shifts are obtained by the successive convex approximation technique. To further reduce the computational complexity of the single-user system, we derive the closed-form solutions of IRS phase shifts in each iteration for the two different reflection setups. Simulation results show that all the proposed algorithms can greatly improve the secrecy throughput compared to the conventional system without IRS. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) is a promising technology which can be integrated with non-orthogonal multiple access (NOMA) to improve the secrecy performance. In this paper, we propose two IRS-aided schemes to enhance the security of NOMA networks for the internal and external eavesdropping, respectively. First, to deal with an internal untrusted user, the secrecy rate maximization problem is formulated by jointly optimizing the active and passive beamforming, while meeting the quality of service (QoS) demand of the untrusted user, decoding order constraints, and unit modulus constraints of IRS elements. Furthermore, considering a worse scenario with both internal and external eavesdroppers, we minimize the transmit power of legitimate signals with both users’ QoS demands satisfied. In this way, the confidential information leakage will be mitigated and more transmit power can be allocated as artificial jamming to attenuate the eavesdropping. To tackle the non-convex optimization, the original problem in each scheme is first decomposed into two subproblems, which are approximated into convex ones via the semidefinite relaxation (SDR). Then, by means of alternating optimization, the suboptimal solutions to the original problems can be obtained. Simulation results demonstrate the superiority of the proposed schemes against the challenging internal and external eavesdropping by combining IRS and NOMA. [ABSTRACT FROM AUTHOR]

This letter is a novel attempt to consider intelligent reflecting surfaces (IRS) assisted user cooperation adopting the practical non-linear phase-shift model in a wireless-powered communication network (WPCN). Specifically, we aim to maximize both max-min fairness and sum throughput among the energy harvesting users while adopting the practical IRS model. We propose an alternating optimization (AO) algorithm exploiting convex relaxation and semidefinite programming to optimize the phase-shift matrices at IRS along with time and power allocation at the hybrid access point serving the users. Simulation results corroborate key analytical claims and show that the proposed jointly-optimal design provides better performance gain for max-min and sum-rate maximization over the existing benchmark schemes. [ABSTRACT FROM AUTHOR]

Antenna selection (AS) and intelligent reflecting surface (IRS) are two promising technologies to reduce the hardware cost of multi-antenna systems. This letter intends to jointly design the users’ active transmit beamformers, the IRS’s passive beamformer, and the base station’s receive AS network in order to maximize the uplink sum rate of an IRS-aided multiuser multiple-input multiple-output (MU-MIMO) system. An efficient algorithm is proposed to solve the resultant non-convex problem via capitalizing on the block coordinate descent and fractional programming techniques. Numerical results show that compared with other baseline methods, the proposed algorithm can significantly improve the uplink sum rate. [ABSTRACT FROM AUTHOR]

Although Internet of things (IoT) can enhance the quality of people's life, but it also brings the problems of energy consumption and network transmission congestion. To solve the above problems, backscatter communication technology for zero-power IoT has been proposed. Thus, it is of great significance to survey the state-of-the-art technologies and study the relevant challenges. Firstly, the basic definition and technical characteristics of zero-power IoT and backscatter communications were presented. Secondly, several typical application scenarios for zero-power backscatter communications in 6G were introduced. Thirdly, the system structure of zero-power backscatter communications was given and the potential key technologies of zero-power backscatter communications for 6G applications were also discussed. Finally, the technical challenges and potential research directions for zero-power backscatter communications were analyzed. Additionally, the proposed overview was of important theoretical significance and practical values for promoting the development of IoT toward full scenes, full spectrum, and many types of services. [ABSTRACT FROM AUTHOR]

This paper considers intelligent reflecting surface (IRS)-aided single-user (SU) massive multiple-input multiple-output (mMIMO) millimeter wave (mmWave) downlink communication system. We aim to maximize the achievable spectral efficiency by separately designing the passive beamforming and active precoding (combining) through a decoupling strategy to reduce computational complexity. We propose two algorithms for passive beamforming design, which are followed by singular value decomposition (SVD) of the effective channel matrix to generate the active precoding and combining matrices at the bases station (BS) and user equipment (UE), respectively. The first algorithm employs the SVD of the BS-IRS and the IRS-UE channel matrices to generate the unitary matrices. These matrices are used to develop the optimization problem, which is solved via a Riemannian conjugate gradient (RCG)-based algorithm, yielding a passive beamforming vector. In the second algorithm, we propose a greedy-search (GS)-based method to select the array response vectors and their corresponding path gains of the mmWave channels between the BS (IRS) and IRS (UE) required to formulate the optimization problem, which is also solved via the RCG-based algorithm, resulting in a passive beamforming vector. The simulation results show that the proposed schemes achieve an improved trade-off between the spectral efficiency and computational complexity. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) improves the physical layer security of the communication systems by adjusting the phases of the reflecting units.A scheme of physical layer security design based on alternative iteration was proposed for IRS-assisted multiple input single output (MISO) communication system.Firstly, aiming at maximizing the secrecy rate, a non-convex objective function with non-convex constraints was constructed.Secondly, the Dinkelbach algorithm and Riemannian manifold optimization algorithm were adopted to transform the non-convex problem into a series of solvable sub-problems.Finally, the alternate iteration method was used to achieve the optimization design of transmit beamforming and IRS phase shift matrix.The simulation results show that, compared with the several existing schemes, the proposed scheme can achieve a better tradeoff between computational complexity and system security rate.

Intelligent reflecting surface (IRS)-assisted simultaneous wireless information and power transfer (SWIPT) system is set as research background.Joint design and optimization of energy-efficiency priority based active beamforming at multi-antenna transmitter and passive beamforming at IRS were investigated.The optimization objective was to maximize minimum energy-efficiency, which was subject to transmitter power control, energy constraint at energy harvesting (EH) receiver and IRS phase shift constraint.Alternating direction method of multipliers (ADMM) was implemented to solve this non-linear non-convex optimization problem.Firstly, the fraction objective function was transformed via Dinkelbach’s approach.Singular value decomposition (SVD) andsemi-definite relaxation (SDR) were applied to obtain active beam forming vector at transmitter.Simulation results indicate that, IRS assisted SWIPT could significantly reduce energy threshold at EH receiver.The achievable energy-efficiency of the proposed strategy is 300 KB/J when total circuit power consumption is -15 dBm.Maximum energy-efficiency can be achieved with the maximum numbers of passive reflecting elements and transmitter antennas.

Due to the uninterrupted flight of the unmanned aerial vehicles (UAV) and the varying phase of the intelligent reflecting surface (IRS) in the IRS-assisted UAV communication system, it is very difficult to estimate the channel information of the system accurately. The traditional solutions try to model the whole communication system as an autoencoder including encoder, channel, and decoder. However, the autoencoder needs to be retrained when the environment changes and the generalization ability is lousy. To solve above problems, a novel C1DAE-ANIL algorithm is proposed to estimate the channel information of the IRS-assisted UAV communication system. On one hand, one-dimensional convolution is introduced to the traditional multi-perceptron autoencoder (MLPAE) for enhancing the encoding and decoding performances of the one-dimensional convolutional autoencoder (C1DAE). On the other hand, the meta-learning is applied to the C1DAE, and the almost no inner loop (ANIL) algorithm is used to find an optimal initial parameter vector of the C1DAE to realize the rapid training in dynamic environments. Compared with the existing contenders, the simulation results show that the end-to-end communication model using the C1DAE-ANIL algorithm not only improves the accuracy of channel estimation but also greatly accelerates the training process in dynamic environments.

Intelligent reflecting surface (IRS) is a revolutionary and low-cost technology for boosting the spectrum and energy efficiencies in future wireless communication network. In order to create controllable multipath transmission in the conventional line-of-sight (LOS) wireless communication environment, an IRS-aided directional modulation (DM) network is considered. In this paper, to improve the transmission security of the system and maximize the receive power sum (Max-RPS), two alternately optimizing schemes of jointly designing receive beamforming (RBF) vectors and IRS phase shift matrix (PSM) are proposed: Max-RPS using general alternating optimization (Max-RPS-GAO) algorithm and Max-RPS using zero-forcing (Max-RPS-ZF) algorithm. Simulation results show that, compared with the no-IRSassisted scheme and no-PSM optimization scheme, the proposed IRS-assisted Max-RPS-GAO method and Max-RPS-ZF method can significantly improve the secrecy rate (SR) performance of the DM system. Moreover, compared with the Max-RPS-GAO method, the proposed Max-RPS-ZF method has a faster convergence speed and a certain lower computational complexity.

In this paper, with the help of an intelligent reflecting surface (IRS), the source (S) and destination (D) exchange information through the two-way decode-and-forward relay (TW-DFR). We focus on the phase optimization of IRS to improve the system sum rate performance. Firstly, a maximizing receive power sum (Max-RPS) method is proposed via eigenvalue decomposition (EVD) with an appreciable system sum rate enhancement, which is called Max-RPS-EVD. To further achieve a higher system sum rate, a method of maximizing minimum rate (Max-Min-R) is proposed with high complexity. To reduce its complexity, a low-complexity method of maximizing the sum rate (Max-SR) via general power iterative (GPI) is proposed, which is called Max-SR-GPI. Simulation results show that the proposed three methods outperform the random phase method, especially the proposed Max-SR-GPI method is the best one achieving at least 20% system sum rate gain over random phase. Additionally, it is also proved the optimal system sum rate can be achieved when TW-DFR and IRS are located in the middle of S and D.

This paper analyzes the performance of millimeter-wave (mmWave) non-orthogonal multiple access (NOMA) system with the aid of an intelligent reflecting surface (IRS). We derive the outage probability (OP) expressions at two users D1 and D2 of the IRS aided mmWave-NOMA system (the IRS-mmWave-NOMA system in the following). We validate the derived expressions via computer simulations. By considering the Nakagami- $m$ fading channels and the path loss model proposed for 5G standard in the IRS-mmWave-NOMA system, our results are more suitable in practical scenarios. Specifically, numerical results observe that even the carrier frequency is extremely high ( $f_{c}=90$ GHz) and the transmitter-receiver distances are further than 50 m, the OPs at D1 and D2 can achieve 10−4 when the transmit power of base station (BS) is 30 dBm (dB in the following). Moreover, the OPs at D1 and D2 with IRS are greatly lower than OPs without IRS. This result demonstrates the huge benefits of utilizing IRS in mmWave-NOMA system. In addition, we can use an IRS with larger number of reflecting elements to maintain the OP performance at D1 and D2 when the distances are increased and $f_{c}$ is higher. On the other hand, we should choose a suitable value of power allocation coefficients to obtain the same performance at D1 and D2.

In this study, the use of intelligent reflecting surfaces (IRSs) to improve the capacity of a multi-user multiple-input-single-output downlink system is investigated. Unlike existing IRS-assisted wireless systems equipped with entirely passive IRS elements or purely active IRS elements, we propose a new IRS architecture in which each IRS element can operate either in active mode or in passive mode. To reduce power consumption, only a small portion of the IRS elements operate in active mode. We seek to maximize the capacity of the proposed IRS-assisted wireless system by jointly optimizing the phase shifts of all IRS elements, the transmit beamforming of the base station, and the IRS active-passive mode vector, which leads to a non-convex problem. In this study, the non-convex problem is addressed by using a probability learning-based algorithm from the cross-entropy optimization framework with an explicit expression for learning the targeted sampling distribution’s tilting parameters. The simulation results reveal that a significant capacity improvement over traditional IRS-assisted wireless systems with entirely passive IRS elements can be achieved using the proposed scheme, which employs only a small portion of active IRS elements.

In this paper, we study the uplink transmission in an intelligent reflecting surface (IRS) assisted cell-free multiple-input multiple-output (MIMO) system where the central processing unit (CPU) only has statistical channel state information (CSI) to detect symbols, and to design the receiver filter coefficients, the power allocations, and the IRS phase shifts. The access points (APs) estimate only their local end-to-end channels with the users using minimum mean squared error (MMSE) estimation to implement matched filtering, thereby avoiding the large overhead associated with estimating individual IRS-assisted channels. Under this framework, we derive a closed-form expression for the achievable uplink net rate that only depends on the channel statistics. Using this expression, we formulate the problem of maximizing the minimum (max-min) signal-to-interference plus noise ratio (SINR) to design the receiver filter coefficients at the CPU, the power allocations at the users, and the phase shifts at the IRS, subject to per user power constraints as well as IRS phase shift resolution constraints. The resulting problem is jointly non-convex in the three design variables and is solved using an alternating optimization algorithm. In particular, the receiver filter design is formulated as a generalized eigenvalue problem leading to a closedform solution, the power allocation problem is solved using a geometric programming (GP) approach, and the IRS phase shifts are designed using an alternating maximization algorithm. For comparison, we also formulate and solve the max-min SINR problem for the scenario where the instantaneous imperfect CSI of all individual direct and IRS-assisted channels is available at the CPU. Numerical results show that the scheme designed using statistical CSI has the potential to outperform the scheme based on instantaneous CSI for moderate to large number of IRS elements, due to savings in the channel estimation overhead.

The applications of Intelligent Transportation System (ITS) and autonomous driving in the 6G era heavily rely on the massive information exchange of ultra-wide bandwidth, high reliability, and low latency to guarantee the safety and experience. On the other hand, the signals in high frequency bands including Millimeter Wave (mmWave) and Terahertz (THz) can be easily blocked by the obstacles. To address this problem, Intelligent Reflecting Surface (IRS) has attracted a lot of attention since it can reflect the signals and change propagation directions the propagation directions in a highly intelligent and energy efficient way, which creates smart radio environments. Due to its easy deployment and low expense, it has been widely recognized as a promising technology for ground and aerial vehicular networks to enhance signal strength, physical layer security, and positioning accuracy. In this paper, we give a comprehensive survey of current research works on different IRS applications in ground-based vehicular communications and aerial vehicular communications after discussing its basic knowledge and main characteristics. To improve current works and spark more ideas, we summarize the challenges and discuss the future perspectives of IRS-aided 6G vehicular communications.