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In recent years, the intelligent reflecting surface (IRS) has attracted increasing attention for its capability to intelligently reconfigure the wireless propagation channel. However, most existing works ignore the dynamic power consumption of IRS related to the phase shift configuration. This relationship gets even more intractable for a multi-bit IRS because of its nonlinearity and implicit form. In this paper, we investigate the beamforming optimization for multi-bit IRS-aided systems with the practical phase shift-dependent power consumption (PS-DPC) model, aiming at minimizing the power consumption of the system. To solve the implicit and nonlinear relationship, we introduce a selection matrix to explicitly represent the power consumption and the phase shift matrix of the IRS, respectively. Then, we propose a generalized Benders decomposition-based beamforming optimization algorithm in the single-user scenario. Furthermore, in the multi-user scenario, we design a coordinate descent-based algorithm and a genetic algorithm for the beamforming optimization. The simulation results show that the proposed algorithms significantly decrease the power consumption of the multi-bit IRS-aided systems. [ABSTRACT FROM AUTHOR]

The intelligent reflecting surface (IRS) is an emerging technology that can conquer visible light communication's (VLC) dependency on the line-of-sight (LoS) channel by offering additional non-light-of-sight (NLoS) communication links. In this paper, a newly proposed electro-tunable intelligent reflecting metasurface is deployed in dimmable single-input single-output (SISO) VLC systems. We aim to improve the bit error rate (BER) performance by jointly optimizing the transmit constellation and the reflectance of the IRS units. To this end, the optimization problem can be solved in two steps. The minimum distance of the received constellation is firstly maximized by a convex problem, which guarantees the minimum BER. Then, the transmit constellation and the synchronously-tunable reflectance of the IRS units that correspond to the optimal received constellation are determined with an iterative alternate optimization algorithm. Finally, the simulation results show the BER performance improvement and the dimming relaxation benefit of the tunable IRS-aided SISO VLC systems. [ABSTRACT FROM AUTHOR]

We consider a multi-device wireless-powered communication network, where an intelligent reflecting surface (IRS) is deployed to assist the wireless energy transfer (WET) from the power station (PS) to the information transmitter (IT) and the wireless information transfer (WIT) from the IT to the devices. The IRS, IT, and PS belong to different service providers, where the PS receives revenue from the IT for WET and pays fees to its energy source and the IRS, and the IT receives revenue from the devices for WIT and pays fees to the PS and the IRS. We model the interactions between the IT and the PS through a Stackelberg game and aim to achieve a win-win situation between them in terms of utility. Specifically, we solve a follower problem that maximizes the utility of the PS by jointly optimizing the IRS reflection coefficients and the transmit power of the PS, and a leader problem that maximizes the utility of the IT by jointly optimizing the energy price, the transmit power allocation of the IT, and the time allocation as well as the IRS reflection coefficients in the WET and WIT phases. The results show that, by deploying IRS, it is possible to effectively enhance the signal strength and thus increase the revenue of the system, and by using Time Division Multiple Access (TDMA), the performance of the system is greatly improved by coordinating the timing of the reception of the signal by each device. [ABSTRACT FROM AUTHOR]

Summary Multiple intelligent reflecting surfaces (IRS) in a wireless system are considered to enhance performance, efficiency, and flexibility in wireless networks. In this paper, we analyze outage probability (OP) for multiple IRS panels‐assisted wireless systems over Nakagami‐ m$$ m $$ fading channels. We focus on selecting the best IRS panel to maintain the quality of service and enhance the user experience. We derive two closed‐form OP expressions using the central limit theorem and Laguerre series expansion. Additionally, we develop a novel asymptotic OP expression and obtain a novel diversity order. The diversity order of the considered system model depends on the minimum fading parameter ( m$$ m $$) between the transmitter‐IRS panel and IRS panel‐receiver links and the number of IRS panels. We thoroughly investigate the impact of system parameters and validate our analytical results with simulations. Our findings emphasize that diversity order depends on the minimum fading parameter ( m$$ m $$) between the transmitter‐IRS panel and IRS panel‐receiver links, the number of IRS elements in each panel, and the number of IRS panels. [ABSTRACT FROM AUTHOR]

For 6G communications, the Ultra Massive Multiple Input Multiple Output (UM-MIMO) systems with Intelligent Reflecting Surface (IRS) assistance are capable since they can efficiently get beyond the limitations of restricted blockage and coverage. However, in the far field, a robust THz channel sparsity is unfavorable to spatial multiplexing, whereas excessive UM-MIMO and IRS dimensions extend the near field region. To address these issues, a hybrid beamforming IRS assisted UM-MIMO THz system with Deep Siamese Capsule Network is designed with the cascaded channel. The near and far field codebook-based beamforming is developed to model the proposed communication channel. The channel estimation is done based on the deep siamese capsule adaptive beluga whale neural network. The simulation results of the bit error rate, Normalized Mean Square Error (NMSE), spectral efficiency, sum rate, data rate, normalized channel gain, beamforming gain, and array gain loss shows that the proposed system achieves reliable performances compared with existing techniques. The suggested approach also demonstrates the outstanding adaptability to various network configurations and good scalability. The method provides a better channel estimation accuracy and less complexity which shows an NMSE of − 11.2 dB at an SNR of 10 dB. [ABSTRACT FROM AUTHOR]

Summary: The future wireless communication networks (beyond 5G) are intended to provide seamless connectivity, higher data rates, and lower latency. The intelligent reflecting surface (IRS) promises extended network coverage. The non‐orthogonal multiple access (NOMA) technique provides higher spectral efficiency and supports a larger number of users. In general, the cell‐edge users far from the base station are affected by severe fading, which may result in an outage. To overcome this issue, we propose an IRS‐aided device‐to‐device (D2D)‐enabled two cells coordinated NOMA system in which a common cell‐edge user acts as D2D receiver. The cell‐edge user receives the signals from near users, IRSs, and both base stations to achieve better diversity. We analyze the outage probability of cell‐edge user, while all the fading channels are assumed Nakagami‐ m and obtain a closed‐form expression at higher signal‐to‐noise ratios. We develop an algorithm to find the ergodic capacity of the cell‐edge user. We validate the mathematical expressions with simulation results and see a substantial improvement in outage performance over the existing systems. [ABSTRACT FROM AUTHOR]

Abstract A compact low-profile multi-band millimeter-wave (mm-wave) reflectarray metasurface design is presented for coverage enhancement in 5G and beyond cellular communication. The proposed single-layer metasurface exhibits a stable reflection response under oblique incidence angles of up to 60 $$^\circ$$ ∘ at 24 and 38 GHz, and transmission response at 30 GHz, effectively covering the desired 5G mm-wave frequency bands. The proposed reflectarray metasurface is polarization insensitive and performs equally well under TE and TM polarized incident waves due to the symmetric pattern. In addition, the low profile of the proposed metasurface makes it appropriate for conformal applications. In comparison to the state-of-the-art, the proposed reflectarray metasurface unit cell design is not only compact (3.3 $$\times$$ × 3.3 mm $$^2$$ 2 ) but also offers two reflections and one transmission band based on a single-layer structure. It is easy to reconfigure the proposed metasurface unit cell for any other frequency band by adjusting a few design parameters. To validate the concept of coverage enhancement, a 32 $$\times$$ × x32 unit-cell prototype of the proposed reflectarray metasurface is fabricated and measured under different scenarios. The experimental results demonstrate that a promising signal enhancement of 20–25 dB is obtained over the entire 5G mm-wave n258, n259, and n260 frequency bands. The proposed reflectarray metasurface has a high potential for application in mm-wave 5G networks to improve coverage in dead zones or to overcome obstacles that prevent direct communication linkages.

Abstract The metasurface is a promising technology that can help next-generation wireless communication systems not only improve the signal-to-noise ratio (SNR), but also increase security and mitigate interference. Further, introducing dual polarization (DP) in a metasurface can enhance its capabilities with polarization diversity, polarization multiplexing, and polarization-switched modulation. In this paper, we study a DP-metasurface-assisted single-user wireless communication system and propose a novel scheme that can improve the spectral efficiency (SE) and bit-error-rate (BER) performance compared to those of conventional schemes by exploiting the orthogonal property of dual-polarized waves. We employ the DP metasurface to increase the SNR at the receiver and create a specific phase difference between the polarized signals by controlling the transmit precoder and the phases of the metasurface reflecting elements representing some modulated bits. At the receiver, we use the recovered phase information to realign the modulated symbols in both polarizations, which are then added coherently. The simulation results demonstrate that the proposed scheme achieves significantly higher SE and BER performance than those of some closely related works.

Enhancing spectral efficiency in non-line-of-sight (NLoS) environments is essential as 5G networks evolve, surpassing 4G systems with high information rates and minimal interference. Instead of relying on traditional Orthogonal Multiple Access (OMA) systems to tackle issues caused by NLoS, advanced wireless networks adopt innovative models like Non-Orthogonal Multiple Access (NOMA), cooperative relaying, Multiple Input Multiple Output (MIMO), and intelligent reflective surfaces (IRSs). Therefore, this study comprehensively analyzes these techniques for their potential to improve communication reliability and spectral efficiency in NLoS scenarios. Specifically, it encompasses an analysis of cooperative relaying strategies for their potential to improve reliability and spectral efficiency in NLoS environments through user cooperation. It also examines various MIMO configurations to address NLoS challenges via spatial diversity. Additionally, it investigates IRS settings, which can alter signal paths to enhance coverage and reduce interference and analyze the role of Unmanned Aerial Vehicles (UAVs) in establishing flexible communication infrastructure in difficult environments. This paper also surveys effective energy harvesting (EH) strategies that can be integrated with NOMA for efficient and reliable energy-communication networks. Our findings show that incorporating these technologies with NOMA not only enhances connectivity and spectral efficiency but also promotes a stable and environmentally sustainable data communication system. [ABSTRACT FROM AUTHOR]

A compact low-profile multi-band millimeter-wave (mm-wave) reflectarray metasurface design is presented for coverage enhancement in 5G and beyond cellular communication. The proposed single-layer metasurface exhibits a stable reflection response under oblique incidence angles of up to 60 ∘ at 24 and 38 GHz, and transmission response at 30 GHz, effectively covering the desired 5G mm-wave frequency bands. The proposed reflectarray metasurface is polarization insensitive and performs equally well under TE and TM polarized incident waves due to the symmetric pattern. In addition, the low profile of the proposed metasurface makes it appropriate for conformal applications. In comparison to the state-of-the-art, the proposed reflectarray metasurface unit cell design is not only compact (3.3 × 3.3 mm 2 ) but also offers two reflections and one transmission band based on a single-layer structure. It is easy to reconfigure the proposed metasurface unit cell for any other frequency band by adjusting a few design parameters. To validate the concept of coverage enhancement, a 32 × x32 unit-cell prototype of the proposed reflectarray metasurface is fabricated and measured under different scenarios. The experimental results demonstrate that a promising signal enhancement of 20–25 dB is obtained over the entire 5G mm-wave n258, n259, and n260 frequency bands. The proposed reflectarray metasurface has a high potential for application in mm-wave 5G networks to improve coverage in dead zones or to overcome obstacles that prevent direct communication linkages. [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 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 of Chongqing University of Technology (Natural Science) is the property of Chongqing 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.)

Summary: The intelligent reflecting surface (IRS) has recently become the most promising technology to achieve maximum beam‐forming gain with a simultaneous wireless information and power transfer (SWIPT) system. Many existing studies perform a single IRS deployment or utilize space division multiple access (SDMA) and non‐orthogonal multiple access (NOMA) schemes. However, the existing schemes face high time complexity and block signal transmission for larger indoor communications. Hence, this article introduces a dual IRS‐aided multiuser SWIPT system by implementing rate‐splitting multiple access (RSMA) systems. To enhance the minimum achievable rate and ensure the minimum harvesting energy (HE), this study proposes a joint successive convex approximation with an integrated optimization algorithm (SCA‐IOA). The proposed algorithm jointly optimizes the reflecting beam forming and power splitting (PS) coefficient of all the users effectively. The simulation results demonstrate the effectiveness of the proposed techniques and produce an outstanding performance for deploying dual IRS and implementing RSMA compared to traditional SDMA and NOMA systems. [ABSTRACT FROM AUTHOR]

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.)

The metasurface is a promising technology that can help next-generation wireless communication systems not only improve the signal-to-noise ratio (SNR), but also increase security and mitigate interference. Further, introducing dual polarization (DP) in a metasurface can enhance its capabilities with polarization diversity, polarization multiplexing, and polarization-switched modulation. In this paper, we study a DP-metasurface-assisted single-user wireless communication system and propose a novel scheme that can improve the spectral efficiency (SE) and bit-error-rate (BER) performance compared to those of conventional schemes by exploiting the orthogonal property of dual-polarized waves. We employ the DP metasurface to increase the SNR at the receiver and create a specific phase difference between the polarized signals by controlling the transmit precoder and the phases of the metasurface reflecting elements representing some modulated bits. At the receiver, we use the recovered phase information to realign the modulated symbols in both polarizations, which are then added coherently. The simulation results demonstrate that the proposed scheme achieves significantly higher SE and BER performance than those of some closely related works. [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. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong 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.)

Abstract The advent of intelligent reflecting surface (IRS) technology has revolutionized the landscape of wireless communication systems, offering promising opportunities for enhancing the performance of Internet of Things (IoT) applications. This paper presents a comprehensive performance evaluation of multi-agent IoT monitoring systems leveraging IRS technology. We focus on three criteria for selecting IRS units and assess the impact on system performance. Specifically, we analyze the system performance by deriving an outage probability expression for each criterion. Our study begins by introducing the concept of IRS and its role in IoT monitoring. We then present three IRS unit selection criteria: optimal selection (OS), partial selection (PS), and random selection (RS). For each criterion, we mathematically model and analyze the system outage probability, shedding light on the reliability and connectivity of IoT devices. The outage probability expressions derived in this work offer valuable insights into the trade-offs associated with IRS unit selection criteria in the context of IoT monitoring. Additionally, our findings contribute to the optimization of multi-agent IoT monitoring systems, enabling improved communication performance and enhanced reliability.

The research for the Intelligent Reflecting Surface (IRS) which has the advantages of cost and energy efficiency has been studied. Channel capacity can be effectively increased by appropriately setting the phase value of IRS elements according to the channel conditions. However, the problem of obtaining an appropriate phase value of IRS is difficult to solve due to the non-convex problem. This paper proposes an iterative algorithm for the alternating optimal solution in the Single User Multiple-Input-Multiple-Output (SU-MIMO) systems. The proposed iterative algorithm finds an alternating optimal solution that is the phase value of IRS one by one. The results show that the proposed method has better performance than that of the randomized IRS systems. The number of iterations for maximizing the performance of the proposed algorithm depends on the channel state between the IRS and the receiver.

The sixth generation of mobile communications（6G）is expected to offer low-latency, lowpower, high-capacity, and large-scale communication services in the future. Terahertz communication, with its wide bandwidth, high speed, and low latency, is considered a key technological pathway for the 6G era. This article details the development routes for signal generation in THz communication systems signal reception technologies, signal transmission characteristics, and channel modeling. It also analyzes the progress and challenges in technological issues such as ultra-massive multiple-in multiple-out, intelligent reflecting surfaces, and critical THz devices. Furthermore, potential applications of THz communication in areas like short-range ultra-high-speed communication, space communication, microscale communication, and high secure communication fields are explored. Although there are some technical obstacles, as research progresses, the challenges associated with terahertz communication are expected to be gradually resolved. The advent of the 6G era is also anticipated to be not far off. [ABSTRACT FROM AUTHOR]

The advent of intelligent reflecting surface (IRS) technology has revolutionized the landscape of wireless communication systems, offering promising opportunities for enhancing the performance of Internet of Things (IoT) applications. This paper presents a comprehensive performance evaluation of multi-agent IoT monitoring systems leveraging IRS technology. We focus on three criteria for selecting IRS units and assess the impact on system performance. Specifically, we analyze the system performance by deriving an outage probability expression for each criterion. Our study begins by introducing the concept of IRS and its role in IoT monitoring. We then present three IRS unit selection criteria: optimal selection (OS), partial selection (PS), and random selection (RS). For each criterion, we mathematically model and analyze the system outage probability, shedding light on the reliability and connectivity of IoT devices. The outage probability expressions derived in this work offer valuable insights into the trade-offs associated with IRS unit selection criteria in the context of IoT monitoring. Additionally, our findings contribute to the optimization of multi-agent IoT monitoring systems, enabling improved communication performance and enhanced reliability. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) networks, also known as drone networks, have gained significant attention for their potential in various applications, including communication. UAV networks for communication involve using a fleet of drones to establish wireless connectivity and provide communication services in areas where traditional infrastructure is lacking or disrupted. UAV communication networks need to be highly secured to ensure the technology's security and the users' safety. The proposed survey provides a comprehensive overview of the current state-of-the-art UAV network security solutions. In this paper, we analyze the existing literature on UAV security and identify the various types of attacks and the underlying vulnerabilities they exploit. Detailed mitigation techniques and countermeasures for the protection of UAVs are described in this paper. The survey focuses on the implementation of novel technologies like Q-learning, blockchain, IRS, and mmWave. This paper discusses network simulation tools that range in complexity, features, and programming capabilities. Finally, future research directions and challenges are highlighted. [ABSTRACT FROM AUTHOR]

This paper proposes and studies performance of reconfigurable intelligent surface (RIS)- assisted multi-hop schemes employing short packet communication (SPC). In the proposed schemes, a source sends its data to a destination, and one RIS is deployed to assist the data transmission at each hop. For complexity reduction purposes, we propose two RIS-assisted data transmission methods at each hop. In the first one, the RIS is only used when the quality of the direct link is not good. In the second one, the direct link or the relay link via the RIS is selected for the data transmission. We evaluate performance of the two proposed schemes by deriving formulas of end-to-end block error rate (BLER-e2e) over Rayleigh fading channel. Finally, the derived BLER-e2e expressions are validated by computer simulations. [ABSTRACT FROM AUTHOR]

To meet the needs of society, the next generation of communication systems  will have to learn to juggle multiple responsibilities effectively. This is leading to exponential growth in both circuit complexity and error rates. It is possible to solve these issues by using the intelligent reflecting surface (IRS)-Assisted space shift keying (SSK) modulation technique. IRS is a cutting-edge technology that shows promise for future networks due to its low mistake rates and low-energy consumption. Similarly, SSK modulation is favored due to its preference for simpler hardware. Therefore, integrating IRS with SSK is preferable for minimizing both error rates and circuit complexity. In this article, a new framework is developed to characterize the average bit error rate (ABER) with the help of the moment generating function (MGF) over Nakagami-m fading channels. The ABER is derived by factoring the direct path and IRS reflected path at the receiver and also comparing the performance analysis over the Rayleigh fading channel. The analytical results are validated through Monte-Carlo simulations. [ABSTRACT FROM AUTHOR]

Recently, hyperdense small cells have been proposed to meet the challenge of the tremendous increment in cellular data service requirements. To reduce the deployment cost, as well as operated cost, these small cells are usually connected to limited backhauls, in which case the backhaul capacity may become a bottleneck in busy hours. In this paper, we propose an optimal scheme for the small cells to utilize the macrocell links as its wireless backhaul. Based on stochastic geometry, the analytical expressions of network capacity with in-band and out-band wireless backhaul are derived and validated using simulation results. The optimized results show that our proposed scheme can significantly improve the network performance in scenarios with a high traffic load. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) is a revolutionizing technology for improving the spectral and energy efficiency of future wireless networks. In this paper, we consider a downlink large-scale system empowered by multi-IRS to aid communication between the multiple base stations (BSs) and multiple user equipment (UEs). We target maximizing the sum rate by jointly optimizing the UE association, the transmit powers of BSs, and the configurations of the IRS beamforming. Due to the applicability restrictions of conventional optimization methods and their high complexity with large-scale networks in dynamic environments, deep reinforcement (DRL) learning is adopted as an alternative approach to finding optimal solutions. First, we model the optimization problem as a multi-agent Markov decision problem (MAMDP). Then, because large-scale wireless networks are naturally complex and changeable, and because many entities interact and affect how the whole system works, it is important to use a multi-agent approach to understand the complex dependencies and relationships between the different parts. In order to solve the problem, we propose a cooperative multi-agent deep reinforcement learning (MADRL)-based algorithm that works for both continuous and discrete IRS phase shifts. Simulation results validate that the proposed algorithm surpasses iterative optimization benchmarks regarding both sum rate performance and convergence. [ABSTRACT FROM AUTHOR]

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.)

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.)

Ensuring content security and copyright protection is a critical concern in wireless communication networks. Furthermore, the emergence of services demanding high-band width and very low delay such as online Games, extended reality (XR), Metaverses, etc., Non-Orthogonal Multiple Access (NOMA) is a technology with the potential to enhance the spectral efficiency of fifth-generation (5G) wireless network and beyond. Hence, this paper studies a NOMA-based downlink system that employs intelligent reconfigurable surfaces (IRS) and operates in a physical-layer security environment. Our study also incorporates the utilization of Fountain codes (FCs), all while contending with the presence of a malicious eavesdropper. Furthermore, a cooperative jamming technique is employed to degrade the quality of the eavesdropping channel. The main contribution of this paper is to derive precise closed-form expressions of outage probability (OP), energy efficiency (EE), intercept probability (IP), and average secrecy rate (ASR) for the proposed system. We also develop a Deep Neural Network (DNN) model to evaluate OP, IP, ASR and the average number of time slots (ATS). Subsequently, Monte Carlo simulations are presented as a means to validate the theoretical findings. The simulation results yield the following insights: i) Their primary purpose is to validate the analytical formulas. ii) This research significantly contributes to deepening our understanding of IRS-NOMA systems, providing a foundation for future investigations into practical implementations. iii) We investigate the optimal power allocation factors within the IRS-NOMA framework, offering valuable insights into designing IRS-NOMA systems to achieve reliable and secure communication. iv) The results illustrate the superior performance of IRS-NOMA in comparison to the conventional IRS-Orthogonal Multiple Access (OMA) method.

Fifth-generation(5G) and beyond communication systems, assisted by Intelligent Reflecting Surfaces (IRS), often encounter hindrances such as unreliable connections, high energy usage, and prolonged latency. Channel estimation in IRS-aided systems is challenging in vehicular communication systems with roadside IRS units and fast-moving users. This paper proposes an efficient and low-complex channel estimation strategy for high-speed vehicular mmWave communication systems equipped with roadside IRS. The method consists of two stages, sensing and prediction, which aim to improve efficiency and accuracy under dynamic channel conditions. In the sensing phase, an initial assessment of channel characteristics is estimated by exploiting the sparse nature of the channel. We use the Compressive Sampling Matching Pursuit (CoSaMP) algorithm for accurate estimation with reduced computational complexity. The prediction stage consists of real-time tracking and prediction of the Angle of Arrival (AoA) and the Angle of Departure (AoD) using the Extended Kalman Filter (EKF). This ensures more accurate dynamic channel estimation based on predicted array response vectors without increasing the pilot overhead. Simulation results demonstrate that our proposed approach can offer precise channel estimation with significantly reduced training overhead.

B5G networks are envisioned to meet higher spectral efficiencies through advanced transmission technologies. Consequently, it is important to comprehend the typical propagation characteristics of wireless communication medium that experience multipath effects, fading, shadowing, and nonlinear distortions. Most of these challenging issues arise under non-line of sight conditions concerning the propagation medium, which deteriorates the quality of the signal received due to uncontrollable channel variations. The recent advances in Intelligent Reflecting Surface (IRS), enable a dynamically alterable wireless environment to achieve flexible reconfigurable channels. The deployment of IRS can evidently bypass the obstacles through smart reflections and shall mitigate channel fading impairments. Motivated by the potentials of the IRS, this paper investigates the performance of IRS-assisted multi-input-multi-output (MIMO) systems to analyze the effect of nonlinear distortions and system adaptation over IID and non-IID Gaussian channel variations. The simulations show the distortion levels with the help of power spectral density for correlated or uncorrelated channel conditions. Furthermore, Monte Carlo simulations are performed to understand the behavior of the wireless system through the analysis of bit error rate (BER) vs signal-to-noise ratio (SNR), Ergodic Channel capacity vs SNR in the presence of IID-Gaussian/ non-IID Gaussian and in-band nonlinear distortions. Additionally, in realistic channel conditions, it is observed that the effect of increasing the number of IRS elements increases the BER performance of ~8dB in both IID and non-IID Gaussian variations. Finally, improvement in the performance of ergodic capacity of 5 bps/Hz has been observed at a fixed SNR of 5dB.

In this paper, an intelligent reflecting surface (IRS)-assisted integrated sensing and communication (ISAC) system is considered, where the ISAC base station (BS) serves the communication user equipments (UEs) and tracks sensing targets simultaneously. Unfortunately, it is challenging to obtain the perfect channel state information (CSI) of user equipments (UEs)-related links and target directions in practice. To address the imperfection, we investigate the robust beamforming design under the statistical CSI and bounded target direction uncertainty models. With these considerations, we formulate a Cramér-Rao bound (CRB) of sensing targets minimization problem, subject to the signal-to-interference-plus-noise ratio (SINR) outage probability constraint, the maximum transmit power limits and the unit-modulus constraint of each IRS element. In order to solve this non-convex problem, we formulate the robust beamforming design as a bi-level optimization (BLO) problem and then apply the double-loop deep unfolding (DU) approach to solve this problem. Specifically, we formulate the problem into a primal-dual problem by integrating the SINR outage probability constraint into the objective function which is further approximated by using the worst-case SINR. The projection method and Riemannian manifold optimization method are used to deal with the maximum transmit power constraint and the unit-modulus constraint, respectively. Therefore, the problem can be reformulated as the bi-level optimization (BLO) problem. The lower level (LL) problems aim to find the worst-case CSI and angle of sensing targets. The upper level (UL) problem is the primal-dual problem solved by beamforming design and the Lagrange multipliers optimization with the certain CSI and angle. Then, we apply double-loop DU neural network which unfolds the iterative gradient descent into multi-layer structure, and each layer consists of UL loop and LL loop. In addition, the trainable step sizes and offset parameters are introduced as the network parameters, and the network ultimately outputs optimal optimization variables. Simulation results demonstrate the effectiveness of the proposed robust beamforming design algorithm.

In this paper, we consider multiple intelligent reflecting surfaces (IRSs) assisted cognitive radio network (CRN) and investigate user-IRS association problem. The CRN consists of multiple secondary transmitters (STs), multiple secondary users (SUs) and a single primary user (PU). With the help of IRSs, the received signal strength at the SUs is improved. This is achieved by optimizing IRS phase shifts, which also helps reducing the interference at the PU. The main objective of this study is to obtain the user-IRS association that maximizes the minimum rate of the user in the network. For this purpose, we employ the well-known optimal relay assignment (ORA) algorithm, known for its polynomial-time complexity. The numerical results clearly demonstrate that the user-IRS assignment obtained through the ORA algorithm yields higher minimum rate performance compared to the sum-rate maximizing and random user-IRS assignment methods.

Due to its ability to reconfigure the optical channel, intelligent reflecting surface (IRS) has revealed its great potential for enhancing the performance of visible light communication (VLC) systems. In this paper, we apply a mirror array-based IRS in typical multiple-input multiple-output based VLC (MIMO-VLC) systems and optimize the IRS configuration to minimize the system bit error rate (BER). By utilizing the tight analytical BER upper bound, a BER minimization problem is formulated subject to the IRS configuration constraints. To solve this problem, an optimization algorithm is proposed which decomposes the original problem into three sub-problems, and the complexity of the algorithm is analyzed. The superiority of the obtained optimal IRS configuration scheme over the baseline scheme in terms of BER performance has been successfully verified through simulations in an indoor IRS-aided $4\times 4$ MIMO-VLC system applying different MIMO techniques.

Recently, intelligent reflective surface (IRS)-aided systems are becoming a prospective technology in realizing for sixth generation (6G) wireless communication era because of extremely low power transmission, seamless coverage and their superiority. These network systems can allow many users and devices to connect to each other, extending the coverage. To empower IRS-aided systems, non-orthogonal multiple access (NOMA) can be leveraged to work with IRS technique enabling further benefits such as mass connectivity, flexible resource allocation and improved performance. Increasing connected devices and expanding coverage means devices have the potential to interfere with each other. Recent studies focusing on researching and analyzing the performance of the IRS-supported NOMA network have not taken into account or not fully calculated the impact of interference on system performance. In this study, we first analyze the effect of co-channel interference (CCI) at users in downlink IRS-NOMA systems. In particular, the CCIs generated by the terminals deployed randomly in the coverage area affect the signal reception at the user in the downlink. In this network model, the channel conditions that follow the Rayleigh distribution and the CCI statistical model are independent and identically distributed. We analyze and evaluate network performance by extracting closed-form expressions of outage probability, ergodic capacity, total achievable rate then highlighting the adverse effects of CCI on IRS-NOMA. In addition, to improve the performance of the IRS-NOMA downlink, we present a framework of theorical analysis to look more insights of users’ performance, i.e. diversity order. Our analytical derivatives are verified through computer simulations based on Monte-Carlo and intuitive comparisons with the benchmarks.

This paper presents a communications framework to overcome the connectivity constraints due to the nonavailability of the line-of-sight transmissions in indoor optical communication systems. This nonavailability can arise for various reasons, such as blockages due to physical objects, unfavorable device orientations or large distances between the transmitter and the receiving devices. The proposed system utilizes multiple intelligent reflecting surface (IRS) arrays and device-to-device (D2D) communications. The D2D communication is realized using infrared (IR) light-emitting diodes (LEDs) with limited output power for eye safety. The performance of this system depends significantly on the assignment of the mirrors in the IRS arrays to the appropriate user links and a direct combinatorial assignment search is too complex to implement. The proposed approach identifies the assignment of each mirror in the IRS arrays as a multi-armed bandit (MAB) problem, and the assignment of all the mirrors together as a combinatorial MAB (CMAB) problem. Since a simultaneous movement of all the IRS mirrors during the implementation of the CMAB algorithm could cause frequent link disruptions, a CMAB algorithm with low disruptions (CMAB-LD) is proposed to obtain the best mirror assignment with low link disruptions. Simulation results demonstrate that the proposed algorithm can provide significant improvement in reward performance and the total reward increases by more than 100% over random mirror assignments when the channels are blocked with high probabilities. In small size problems, the proposed CMAB-LD is found to achieve the global optimal solution in just a few rounds of full arm explore operations.

The intelligent reflecting surface (IRS) is an emerging technology that can conquer visible light communication’s (VLC) dependency on the line-of-sight (LoS) channel by offering additional non-light-of-sight (NLoS) communication links. In this paper, a newly proposed electro-tunable intelligent reflecting metasurface is deployed in dimmable single-input single-output (SISO) VLC systems. We aim to improve the bit error rate (BER) performance by jointly optimizing the transmit constellation and the reflectance of the IRS units. To this end, the optimization problem can be solved in two steps. The minimum distance of the received constellation is firstly maximized by a convex problem, which guarantees the minimum BER. Then, the transmit constellation and the synchronously-tunable reflectance of the IRS units that correspond to the optimal received constellation are determined with an iterative alternate optimization algorithm. Finally, the simulation results show the BER performance improvement and the dimming relaxation benefit of the tunable IRS-aided SISO VLC systems.

In recent years, the intelligent reflecting surface (IRS) has attracted increasing attention for its capability to intelligently reconfigure the wireless propagation channel. However, most existing works ignore the dynamic power consumption of IRS related to the phase shift configuration. This relationship gets even more intractable for a multi-bit IRS because of its nonlinearity and implicit form. In this paper, we investigate the beamforming optimization for multi-bit IRS-aided systems with the practical phase shift-dependent power consumption (PS-DPC) model, aiming at minimizing the power consumption of the system. To solve the implicit and nonlinear relationship, we introduce a selection matrix to explicitly represent the power consumption and the phase shift matrix of the IRS, respectively. Then, we propose a generalized Benders decomposition-based beamforming optimization algorithm in the single-user scenario. Furthermore, in the multi-user scenario, we design a coordinate descent-based algorithm and a genetic algorithm for the beamforming optimization. The simulation results show that the proposed algorithms significantly decrease the power consumption of the multi-bit IRS-aided systems.

This paper proposes to combat active eavesdropping using intelligent reflecting surface (IRS) backscatter techniques. To be specific, the source (Alice) sends the confidential information to the intended user (Bob), while the eavesdropper (Willie) transmits a jamming signal to interrupt the transmission for more data interception. To enhance the secrecy, an IRS is deployed and connected with Alice through fiber to transform the jamming signal into the confidential signal by employing backscatter techniques. Based on the considered model, an optimization problem is formulated to maximize the signal-to-interference-plus-noise ratio (SINR) at Bob under the constraints of the transmit power at Alice, the reflection vector at the IRS, and the allowable maximum the SINR at Willie. To address the optimization problem, an alternate optimization algorithm is developed. The simulation results verify the achievable secrecy gain of the proposed scheme. The proposed scheme is effective in combating active eavesdropping. Furthermore, the deployment of large-scale IRS significantly enhances the secrecy rate. [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. [ABSTRACT FROM AUTHOR]