Search

In the intelligent reflecting surface (IRS)-assisted MIMO systems, optimizing the passive beamforming of the IRS to maximize spectral efficiency is crucial. However, due to the unit-modulus constraint of the IRS, the design of an optimal passive beamforming solution becomes a challenging task. The feature input of existing schemes often neglects to exploit channel state information (CSI), and all input data are treated equally in the network, which cannot effectively pay attention to the key information and features in the input. Also, these schemes usually have high complexity and computational cost. To address these issues, an effective three-channel data input structure is utilized, and an attention mechanism-assisted unsupervised learning scheme is proposed on this basis, which can better exploit CSI. It can also better exploit CSI by increasing the weight of key information in the input data to enhance the expression and generalization ability of the network. The simulation results show that compared with the existing schemes, the proposed scheme can effectively improve the spectrum efficiency, reduce the computational complexity, and converge quickly. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the performance of an intelligent reflecting surface (IRS)-aided terahertz (THz) wireless communication system with pointing errors. Specifically, we derive closed-form analytical expressions for the upper bounded ergodic capacity and approximate expression of the outage probability. We adopt an α - μ fading channel model for our analysis that is experimentally demonstrated to be a good fit for THz small-scale fading statistics, especially in indoor communication scenarios. In the proposed analysis, the statistical distribution of the α - μ fading channel is used to derive analytical expressions for the ergodic capacity and outage probability. Our proposed analysis considers not only the IRS reflected channels, but also the direct channel between the communication nodes. The results of the derived analytical expressions are validated through Monte Carlo simulations. Through simulations, it has been noticed that pointing errors degrade the performance of the IRS-assisted THz wireless communication system which can be compensated by deploying an IRS having a large number of reflecting elements. [ABSTRACT FROM AUTHOR]

In this paper, we consider an intelligent reflecting surface (IRS)-assisted multi-user non-orthogonal multiple access (NOMA) ultra-reliable low-latency communication network, where an access point (AP) transmits data to multiple users with the short-packet communication (SPC) by applying the NOMA protocol. To maximize the sum throughput of all users, the transmit power of the AP and the IRS reflecting beamforming are jointly optimized. Since the data rate expression of the SPC is in a complicated form and the optimization variables are coupled with each other, the considered optimization problem is highly non-convex and challenging to solve. Based on the techniques of block coordinate ascent, semidefinite relaxation, Gaussian randomization, and successive convex approximation, we propose an efficient algorithm to obtain a high-quality solution. Simulation results show that the proposed algorithm can achieve significant throughput performance gain over some existing benchmark schemes. [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.)

Intelligent reflecting surface (IRS) is an emerging and low-cost revolutionary technology that can be deployed in future communication systems to enhance data transmission performance. An IRS comprises a lot of small, passive, and low-cost elements that can smartly reflect the incident signal from the transmitter to the receiver. In this paper, an IRS-enhanced multiple-input multiple-output (MIMO) downlink network is considered, in which a cell-edge user receives the data signal from a base station (BS). In this network, the direct path between the BS and the user is blocked by an obstacle, and the user receives only the tunable reflected signal from the IRS. To achieve spatial diversity, beamforming is applied to the antennas at the BS and the user. The goal is to jointly design the transceiver beamforming vectors and the IRS reflection coefficients so that the signal-to-noise ratio (SNR) of the user is maximized. Since the IRS elements are passive, the amplitude of the IRS reflection coefficients must be equal to or smaller than one. The constrained SNR optimization problem is non-convex. We propose a three-step procedure to obtain an effective sub-optimal solution for this problem. Accordingly, an innovative non-iterative algorithm is proposed to design the problem parameters. Simulation results show that the IRS-enhanced MIMO downlink system, in which the proposed non-iterative algorithm is used to develop the network parameters, outperforms the conventional network without IRS in terms of bit error rate (BER). [ABSTRACT FROM AUTHOR]

Mine Internet of Things (MIoT) devices in intelligent mines often face substantial signal attenuation due to challenging operating conditions. The openness of wireless communication also makes it susceptible to smart attackers, such as active eavesdroppers. The attackers can disrupt equipment operations, compromise production safety, and exfiltrate sensitive environmental data. To address these challenges, we propose an intelligent reflecting surface (IRS)-assisted secure transmission system for an MIoT device which enhances the security and reliability of wireless communication in challenging mining environments. We develop a joint optimization problem for the IRS phase shifts and transmit power, with the goal of enhancing legitimate transmission while suppressing eavesdropping. To accommodate time-varying channel conditions, we propose a reinforcement learning (RL)-based IRS-assisted secure transmission scheme that enables MIoT device to optimize both the IRS reflecting coefficients and transmit power for optimal transmission policy in dynamic environments. We adopt the deep deterministic policy gradient (DDPG) algorithm to explore the optimal transmission policy in continuous space. This can reduce the discretization error caused by traditional RL methods. The simulation results indicate that our proposed scheme achieves superior system utility compared with both the IRS-free (IF) scheme and the IRS randomly configured (IRC) scheme. These results demonstrate the effectiveness and practical relevance of our contributions, proving that implementing IRS in MIoT wireless communication can enhance safety, security, and efficiency in the mining industry. [ABSTRACT FROM AUTHOR]

The metaverse is emerging as a next-generation internet paradigm that will enhance human interaction and connectivity. Digital twinning, a fundamental strategy used in the metaverse, allows for the virtualization of real-world items, people, actions, and settings. A virtual world called the metaverse is built on a variety of technologies. Wireless communication is an important part of these technologies. In particular, wireless 6G communication can be essential for the growth of the metaverse. In line with the goal of achieving higher rates in the next-generation wireless network for the metaverse, in this paper, a novel conceptualization of intelligent reflecting surface (IRS)-assisted multiple-input single output-based wireless communication in physical world environments is proposed. More specifically, this paper proposes that in the physical world, the IRS-assisted communication between a communication network and users can be reflected in the metaverse through the virtual world (such as digital avatars and the virtual environment). In the simulations, the bit-error rate and spectral efficiency of the receiver terminal were performed and calculated in the metaverse engine for future consideration. [ABSTRACT FROM AUTHOR]

In this paper, we propose to utilize an intelligent reflecting surface (IRS) as a promising technology to enhance the coverage and physical layer security of non-orthogonal multiple access (NOMA) system. In particular, an IRS-assisted NOMA system is considered with the aid of careful channel ordering of the NOMA users, in which the transmitter sends superposed signals to multiple legitimate users by virtue of the IRS in the presence of multiple eavesdroppers. Meanwhile, the secrecy performance of the IRS-assisted NOMA system is investigated under two wiretapping cases: non-colluding and colluding external eavesdroppers. In the non-colluding case, eavesdroppers operate independently, while in the colluding case, every eavesdroppers can combine their observations to decode the messages. To this end, we derive the approximate closed-form expressions for the secrecy outage probability (SOP) and the asymptotic SOP for each wiretapping case. Also, we assume that the phase of the IRS elements is set by using the ON–OFF control method. Based on analytical results, we show that the secrecy diversity order of the IRS-NOMA at legitimate users is in connection with the number of reflecting elements. From the numerical results, it can be seen that the IRS-NOMA can achieve superior secrecy performance with increasing the number of reflecting elements of the IRS. However, we also find out that using the finite ON state reflective elements can improve the secrecy performance. Actually, increasing the number of ON state reflective elements above five has a negative effect on the system's secrecy performance. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. 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.)

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.)

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing 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.)

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing 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.)

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing 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.)

This communication aims to address the recent surge of interest in reconfigurable intelligent surfaces (RISs) among both academic and industrial communities, which has largely neglected the historical developments of two other underpinning technologies, i.e., liquid crystal on silicon (LCOS) and liquid crystal reflectarray antenna (LCRA). Specifically, this communication focuses on the state of the art of LC-RIS, highlighting the unique features of this newly raised enabling technology for post-5G (6G) networks and comparing it to LCOS, which operates at different frequencies and is suited to different use cases. Drawing on insights from existing knowledge of LCOS and LCRA, opportunities and challenges are explored for LC-RIS's technical advancements in enhancing the coverage, capacity, and energy efficiency of wireless networks. In particular, the development status and roadmap of LC-RIS in China is reviewed. [ABSTRACT FROM AUTHOR]

This letter studies an OFDM based intelligent reflecting surface (IRS)‐aided multiuser simultaneous wireless information and power transfer (SWIPT) system. With subcarrier allocation, users use the assigned subcarriers for information decoding (ID), and use signals received on subcarriers allocated to other users for energy harvesting (EH). An optimization problem is formulated to maximize the system energy efficiency by jointly optimizing subcarrier and power allocation and IRS reflection coefficients matrix. It can be solved by an alternating optimization algorithm. Simulation results reveal that energy efficiency can be enhanced in our proposed scheme compared with the benchmark schemes. [ABSTRACT FROM AUTHOR]

Hardware impairments (HI) are always present in low-cost wireless devices. This paper investigates the outage behaviors of intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) networks by taking into account the impact of HI. Specifically, we derive the approximate and asymptotic expressions of the outage probability for the IRS-NOMA-HI networks. Based on the asymptotic results, the diversity orders under perfect self-interference cancellation and imperfect self-interference cancellation scenarios are obtained to evaluate the performance of the considered network. In addition, the system throughput of IRS-NOMA-HI is discussed in delay-limited mode. The obtained results are provided to verify the accuracy of the theoretical analyses and reveal that: 1) The outage performance and system throughput for IRS-NOMA-HI outperforms that of the IRS-assisted orthogonal multiple access-HI (IRS-OMA-HI) networks; 2) The number of IRS elements, the pass loss factors, the Rician factors, and the value of HI are pivotal to enhancing the performance of IRS-NOMAHI networks; and 3) It is recommended that effective methods of reducing HI should be used to ensure system performance, in addition to self-interference cancellation techniques. [ABSTRACT FROM AUTHOR]

In this paper, a downlink energy efficiency maximization problem is investigated in an intelligent reflective surface (IRS)-assisted visible light communication system. In order to extend wireless communication coverage of the onshore base station, an IRS mounted on a unmanned aerial vehicle (UAV) is introduced to assist an onshore lighthouse with simultaneously providing remote ship users wireless communication services and illumination. Aiming to maximizing the energy efficiency of the proposed system, a resource allocation problem is formulated as the ratio of the achievable system sum rate to the total power consumption under the constraints of the user’s data requirement and transmit power budget. Due to the non-convexity of the proposed problem, the Dinkelbach method and mean-square error (MSE) method are adopted to turn the non-convex origin problem into two equivalent problems, namely transmit beamforming and reflected phase shifting. The Lagrangian method and semidefinite relaxation technique are used to obtain the closed-form solutions of these two subproblems. Accordingly, an alternative optimization-based resource allocation scheme is proposed to obtain the optimal system energy efficiency. The simulation results show that the proposed scheme performs better in terms of energy efficiency over benchmark schemes.

In order to further improve the spectral efficiency of intelligent reflecting surface (IRS)-assisted millimeter wave non-orthogonal multiple access (NOMA) system, a joint design scheme of user clustering, successive interference cancellation (SIC) decoding order, transmit power allocation, analog beam selection and IRS phase shifting was investigated, under the transmit power constraint, SIC decoding rate constraint, beam selection constraint, SIC decoding order constraint, user rate constraint, and IRS phase shift constraint, aiming at maximizing system sum rate.Because of the coupling of discrete variables, combinatorial variables and continuous variables in objective function and constraints, the formulated optimization problem was difficult to solve by traditional optimization methods.To solve it, first the SIC decoding sequence variables were transformed into binary discrete variables through a query table, then the discrete variables and continuous variables were decoupled based on the hybrid whale optimization algorithm, and delt with the optimization constraints based on the penalty function method, and the non-convex optimization problem was transformed into a heuristic problem for iterative solution.Simulation results verify the effectiveness of the proposed scheme.

This paper considers the intelligent reflecting surface-aided MIMO secrecy rate maximization. We apply the alternating optimization to maximize the secrecy rate of the MIMO wiretap channels. We first provide the closed-form solution to each subproblem of optimizing reflecting unit coefficient and show that the sequence generated by the coordinate descent method converges to a stationary point of the main optimization problem. Numerical results show that the intelligent reflecting surface significantly increases secrecy rates in MIMO wiretap channels.

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.

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]

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]

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 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]

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.

Intelligent Reflecting Surfaces (IRSs) coupled with Massive Multiple-Input-Multiple-Output (MIMO) millimeter wave (mmWave) systems hold immense promise for the next generation of wireless communications. However, harnessing their full potential requires accurate channel state information (CSI). Despite the benefits of IRSs, such as passive element integration and energy efficiency, precise channel estimation becomes a formidable challenge due to the absence of active elements. In this paper, we tackle these challenges by employing generative adversarial networks (GANs) to estimate the channel's cascade matrix between the base station (BS) and mobile users. To leverage the high correlation among adjacent elements in the IRS, we propose turning off a majority of these elements during the estimation phase, effectively creating a low-resolution channel. We then introduce the semi-super resolution GAN (SSRGAN) model, capable of inferring channel values for the deactivated elements based on existing correlations. Our new SSRGAN-based channel estimation method transforms low-resolution channel data into high-resolution channel data. Through a comprehensive comparative analysis, our study showcases the superior performance of our SSRGAN channel estimation method compared to established benchmark schemes. • Introducing SSRGAN model for enhanced cascade channel estimation in IRS-MIMO mmWave. • Using a cluster delay line channel model from 3GPP standard ensures real-world relevance. • Directly converts low-resolution channel data to high-resolution without interpolation. • Activate a subset of IRS elements during channel estimation to reduce pilot signals. [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.

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.

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.)

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]

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]

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.)

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]

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.

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.