Conventional fixed-orientation antenna (FOA) arrays offer limited degrees of freedom (DoF) for flexible beamforming such as null steering. To address this limitation, we propose a new rotatable antenna array (RAA) architecture in this paper, which enables three-dimensional (3D) rotational control of an antenna array to provide enhanced spatial flexibility for null steering. To characterize its performance, we aim to jointly optimize the 3D rotational angles of the RAA, to maximize the beam gain over a given desired direction, while nulling those over multiple interference directions under zero-forcing (ZF) beamforming. However, this problem is non-convex and challenging to tackle due to the highly nonlinear expression of the beam gain in terms of the rotational angles. To gain insights, we first examine several special cases including both isotropic and directional antenna radiation patterns, deriving the conditions under which full beam gain can be achieved over the desired direction while meeting the nulling constraints for interference directions. These conditions clearly indicate that compared with FOA arrays, RAAs can significantly relax the angular separation requirement for achieving effective null steering. For other general cases, we propose a sequential update algorithm, that iteratively refines the 3D rotational angles by discretizing the 3D angular search space. To avoid undesired local optimum, a Gibbs sampling (GS) procedure is also employed between two consecutive rounds of sequential update for solution exploration. Simulation results verify our analytical results and show superior null-steering performance of RAAs to FOA arrays.

Reconfigurable intelligent surfaces (RIS) have emerged as a crucial technology for making sustainable and green communication in future sixth-generation (6G) networks. By adaptively adjusting the wireless environment, RIS facilitates flexible control over signal propagation. When RIS is integrated into a simultaneous wireless information and power transfer (SWIPT) system, it can improve energy efficiency (EE) and link reliability. This enables low-power internet-of-things (IoT) devices to achieve continuous energy acquisition while maintaining reliable data access. This capability aligns well with the increasing demands of future 6G networks for enhancing EE and achieving ubiquitous connectivity. Motivated by this, we investigate a RIS-assisted multi-user SWIPT system that accounts for the nonlinear characteristics of the high power amplifier (HPA) at the transmitting end and the nonlinearity of the energy harvesting (EH) circuits at the receivers. These hardware imperfections pose major challenges for system modeling and optimization, particularly under stringent power limitations and quality of service (QoS) requirements, where improving overall EE is a key objective. Therefore, we proposed a joint optimization framework that simultaneously designs the access point (AP) beamforming, the RIS reflection coefficients, and the power splitting (PS) ratios at the receivers. The resulting problem is non-convex, with strong coupling among these variables. To tackle this issue, we propose an efficient alternating optimization (AO) framework. The fractional EE objective is handled using the Dinkelbach method, while each subproblem is iteratively convexified via successive convex approximation (SCA) and semi-definite relaxation (SDR) algorithms. Simulation results reveal that the proposed AO approach achieves substantial EE gains and confirm that integrating RIS conspicuous boosts the overall system EE performance.

Next-generation wireless networks are progressing beyond conventional connectivity to incorporate emerging sensing and computing capabilities. This convergence gives rise to integrated systems that enable not only uninterrupted communication, but also environmental awareness, intelligent decision-making, and novel applications that take advantage of these combined features. At the same time, this integration brings substantial security challenges. As computing, sensing, and communication become more tightly intertwined, the overall complexity of the system increases, creating new vulnerabilities and expanding the attack surface. The widespread deployment of data-heavy artificial intelligence applications further amplifies concerns regarding data security and privacy. This paper presents a comprehensive survey of security and privacy threats, along with potential countermeasures, in integrated wireless systems. We first review physical-layer security techniques for communication networks, and then investigate the security and privacy implications of semantic and pragmatic communications and their associated cross-layer design methodologies. For sensing functionalities, we pinpoint security and privacy risks at the levels of signal sources, propagation channels, and sensing targets, and summarize state-of-the-art defense strategies for each. The growing computational requirements of these applications drive the need for distributed computing over the network, which introduces additional risks such as data leakage, weak authentication, and multiple points of failure. We subsequently discuss secure coded computing approaches that can help overcome several of these challenges. Finally, we introduce unified security frameworks tailored to integrated communication–sensing–computing architectures, offering an end-to-end perspective on protecting future wireless systems.

The external power supply requirement of intelligent reflecting surfaces (IRS) restricts its deployment and application. To address this issue, self-sustainable IRS that can harvest energy from wireless signals significantly enhances the convenience and feasibility of IRS deployment. This paper investigates a novel IRS-aided simultaneous wireless information and power transfer (SWIPT) system, where IRS adopts power splitting (PS) protocol, time switching (TS) protocol or element splitting (ES) protocol to extract energy from the incident signals for sustaining its operation. Our aim is to maximize the weighted sum rate (WSR) while accounting for the constraints in terms of maximum transmit power, IRS reflection coefficients, the energy harvesting requirements of both users and the IRS. The WSR maximization problem is non-convex due to the coupling among variables. For the formulated problem, we propose an alternating optimization (AO) framework to transform it into several subproblems and solves them iteratively. Specifically, we utilize successive convex approximation (SCA) technique to tackle the non-convex optimization challenges in both transmit and reflective beamforming subproblems. Finally, simulation results demonstrate that employing a self-sustainable IRS together with the proposed algorithm yields a WSR improvement of 32%–60%.

Integrated sensing and communication (ISAC) is regarded as a promising solution for the development of the emerging low-altitude economy (LAE). Given the necessity of accurate and realistic wireless channel models for ISAC evaluation and optimization, this paper proposes an LAE-oriented ISAC channel modeling framework. By incorporating the target unmanned aerial vehicle (UAV) scattering response, we decouple the ISAC channel into target and background channels. For the target channel, the model is formulated as a cascade of the transmitting base station (BS)-target link, the target-sensing BS link, and the scattering response from the target UAV. For the background channel, a novel parameter is introduced to separate the influence of the target UAV. Furthermore, key statistical properties, including the space-time-frequency correlation function, coherence distance, Doppler power spectral density, root mean square delay spread, and stationarity interval, are derived and analyzed. The accuracy of the proposed channel model is verified by the close agreement between simulated statistics properties and measured data, and the effectiveness of the cascaded method is validated by the close agreement between the concatenation output and simulation results.

In this paper, we propose a new rotatable antenna (RA) model to improve the performance of wireless communication systems. Different from conventional fixed antennas, the proposed RA system can flexibly and independently alter the boresight direction of each antenna via mechanical or electronic means to exploit new spatial degrees-of-freedom (DoFs). Specifically, we investigate an RA-enabled uplink communication system, where the receive beamforming and the boresight directions of all RAs at the base station (BS) are jointly optimized to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all the users. In the special single-user and free-space propagation setup, the optimal boresight directions of RAs are derived in closed form with the maximum-ratio combining (MRC) beamformer applied at the BS. In the general multi-user and multipath channel setup, we first propose an alternating optimization (AO) algorithm to alternately optimize the receive beamforming and the boresight directions of RAs in an iterative manner. Then, a two-stage algorithm that solves the formulated problem without the need for iteration is proposed to further reduce computational complexity. Moreover, we extend the channel model to incorporate polarization effects and frequency-selective fading while catering to antenna boresight rotation. Simulation results are provided to validate our analytical results and demonstrate that the proposed RA system can significantly improve the communication performance as compared to other benchmark schemes.

In this paper, we propose a new concept of self-sustainable active metasurface (SAM), which exploits the dual advantages of energy harvesting in terms of self-sustainability and active metasurface in terms of information transmission, to achieve continuous operation and flexible deployment for reconfigurable intelligent surface (RIS) and simultaneously mitigate its multiplicative fading. SAM can enhance incident signals via power amplifiers and achieve self-sustainability by harvesting ambient energy. We propose three operation schemes to implement energy harvesting and information transmission for SAM, namely, time-switching based SAM (TS-SAM), power-splitting based SAM (PS-SAM), and element-splitting based SAM (ES-SAM). Then, we propose three new metrics, namely, energy-information outage probability (EIOP), energy-information intercept probability (EIIP), and secure energy efficiency ratio (SEER). The accurate and asymptotic EIOP and EIIP as well as accurate SEER for the three proposed schemes are analyzed, respectively. The results show that compared to self-sustainable passive RIS, TS-SAM and ES-SAM have better EIOPs, and PS-SAM has a better EIIP. Among the three schemes, PS-SAM achieves the best EIOP at low RF energy, while TS-SAM and ES-SAM perform better in high-energy scenarios. For EIIP, PS-SAM outperforms the other two schemes. In particular, compared to self-sustainable passive RIS, TS-SAM and ES-SAM have better SEERs, verifying the superiority of the proposed TS-SAM and ES-SAM. Among all inter-node distances, the distance between user and SAM dominates the performance. When the harvested energy and the number of reflecting elements are sufficiently large, the EIOP and EIIP of TS-SAM and ES-SAM are unrelated to the amplification factor of SAM.

In this paper, a reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) communication system with hybrid beamforming is considered, where the base station (BS) transmits signals to users via the RIS. Due to the difficulty in acquiring accurate channel state information (CSI) and the high complexity of hybrid beamforming design, a codebook-based method is employed to balance communication performance and computational cost. However, conventional codebook construction typically neglects the influence of surrounding communication environments, resulting in poor adaptability in complex mmWave scenarios. Therefore, we propose a three-stage environment-aware codebook design framework based on channel knowledge map (CKM) to enhance the adaptability of systems under different channel conditions. Specifically, in the first stage, a deep learning (DL)-based method is proposed to construct the CKM, which is a site-specific database that provides accurate CSI for given locations. In the second stage, the CSI of potential users is rapidly acquired based on the CKM, then a deep reinforcement learning (DRL) algorithm is employed to generate environment-aware codebooks by solving a sum achievable rate maximization problem. In the third stage, the end-to-end composite channel is first estimated. Then, the digital beamforming is computed by using the minimum mean square error (MMSE) criterion. Finally, the optimal analog beamforming and RIS phase shifts are selected through codebook scanning. Simulation results demonstrate that the proposed CKM-based environment-aware codebooks significantly outperform other codebook schemes in terms of sum achievable rate.

Prior studies on mixed near-field and far-field communications have focused exclusively onsingle-cellscenarios, where both near-field and far-field users are served by the same base station (BS), leading tointra-cellmixed-field interference. In this paper, we consider a more general and practicalmulti-cell mixed-fieldscenario consisting of multiple cells, each serving multiple users, thus resulting in more complexinter-cellmixed-field interference. To address this new challenge, we propose leveragingrotatable antenna(RA) technology to enhance multi-cell mixed-field communication performance by exploiting the additional spatial degree-of-freedom (DoF) introduced by RA rotation to mitigate interference in an efficient way. Specifically, we study an RA-enabled multi-cell mixed-field communication system in which each BS is equipped with an RA array to serve its associated users. We formulate a network-wide sum-rate maximization problem that jointly optimizes the transmit beamforming and the rotation angles of the RA arrays, subject to per-BS power constraints and admissible array rotation limits. To gain useful insights into the role of RAs in multi-cell mixed-field communications, we first analyze a special case with a single user per cell. For this case, we obtain a closed-form expression for therotation-awareinter-cell mixed-field interference using the Fresnel integrals and analytically show that RA rotation can effectively mitigate such interference, thereby substantially improving system performance. For the general case with multiple users per cell, we develop an efficientdouble-layeralgorithm: the inner layer optimizes the transmit beamforming at each BS via semidefinite relaxation (SDR) and successive convex approximation (SCA); while the outer layer determines the rotation angles of the RA arrays using particle swarm optimization (PSO). Numerical results demonstrate that RA-enabled multi-cell systems achieve significant performance gains over conventional fixed-antenna systems, and the proposed joint design consistently outperforms various benchmark schemes.

In this paper, we propose a new rotatable antenna (RA) model to improve the performance of wireless communication systems. Different from conventional fixed antenna, the proposed RA system can independently and flexibly change the three-dimensional (3D) orientation/boresight of each antenna by adjusting its deflection angles to achieve desired channel realizations. Specifically, we study an RA-enabled uplink communication system, where the receive beamforming and the deflection angles of all RAs are jointly optimized to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all the users. In the special single-user and free-space propagation setup, the optimal deflection angles are derived in closed form with the maximum-ratio combining (MRC) beamformer applied at the base station (BS). In the general multi-user and multi-path setup, we propose an alternating optimization (AO) algorithm to alternately optimize the receive beamforming and the deflection angles in an iterative manner. Simulation results are provided to demonstrate that the proposed RA-enabled system can significantly outperform other benchmark schemes.

The external power supply requirement of intelligent reflecting surfaces (IRS) restricts its deployment and application. To address this issue, self-sustainable IRS that can harvest energy from wireless signals significantly enhances the convenience and feasibility of IRS deployment. This paper investigates a novel IRS-aided simultaneous wireless information and power transfer (SWIPT) system, where the IRS adopts a power splitting (PS) protocol to extract energy from the incident signals for sustaining its operation. Our aim is to maximize the weighted sum rate (WSR) while considering the constraints in terms of maximum transmit power, IRS reflection, the harvesting energy requirements of users and IRS. The formulated problem of maximizing WSR is non-convex, which stems from the coupling among variables, making the problem complicated. To solve this problem, we propose an alternating optimization (AO) framework to transform the formulated problem into three subproblems, which enables an iterative solution approach. Specifically, we utilize successive convex approximation (SCA) technique to tackle the non-convex optimization challenges in both transmit and reflective beamforming subproblems. Simulation results demonstrate the efficacy of the proposed algorithm and substantiate the advantages of implementing self-sustained IRS for improving the WSR performance compared to other benchmark schemes.

Mobile edge computing (MEC) has emerged as a cutting-edge technique that brings computation and storage resources closer to the edge of the mobile network. However, MEC is vulnerable to be attacked by malicious users. To improve the security of computation tasks and enhance user connectivity, we design a deep reinforcement learning (DRL) network for reconfigurable intelligence surface (RIS)-aided MEC system. Specifically, we jointly optimize the phase shifts at the RIS, tasks offloaded by users and task assignment to maximize the secrecy offloading capacity and minimize energy consumption under different delay requirements of users. Furthermore, a multi-agent twin delayed deep deterministic policy gradient (TD3)-based algorithm is exploited to tackle the non-convex optimization problem. Numerical results validate the feasibility and applicability of our proposed scheme, demonstrating that the proposed scheme significantly improves the security and energy performance of the system compared to the baseline DRL algorithm.

In this paper, a reconfigurable intelligent surface (RIS) is utilized to assist wireless-powered communication network (WPCN) for achieving integrated sensing and communication (ISAC). In this system, the ISAC base station (BS) first performs radar sensing for multiple targets and wireless power transfer (WPT) for multiple energy-constrained devices with assistance of RIS during the downlink (DL), then all devices transmit their data to the ISAC BS in the uplink (UL) by employing non-orthogonal multiple access (NOMA) with the use of harvested energy. To maximize the sum achievable throughput subject to the quality of service (QoS) constraints of communication devices and sensing quality constraint of targets, we jointly optimize the energy beamforming matrix, radar beamforming matrix, phase shift matrix of RIS, and transmission timeslot allocation in the DL and UL durations. To tackle this non-convexity problem, a two-stage solution is proposed. In the first stage, the energy and radar beamforming matrices, and phase shift matrix of RIS in the DL can be derived according to the penalty-based successive convex approximation (SCA) algorithm and Riemannian conjugate gradient (RCG) algorithm, respectively. In the second stage, the transmission timeslot allocation and phase shift matrix of RIS in the UL are obtained via the polar point method and penalty-based RCG algorithm, respectively. The final optimal results of each stage are yielded based on alternating optimization (AO) algorithm. Numerical results verify the significant improvement of deploying RIS for WPT and data transmission, and the superiority of the proposed optimization solution in improving the sensing performance and achievable throughput compared to other schemes.

Different types of intelligent reflecting surfaces (IRS) are exploited for assisting wireless communications. The joint use of passive IRS (PIRS) and active IRS (AIRS) emerges as a promising solution owing to their complementary advantages. They can be integrated into a single hybrid active-passive IRS (HIRS) or deployed in a distributed manner, which poses challenges in determining the IRS element allocation and placement for rate maximization. In this paper, we investigate the capacity of an IRS-aided wireless communication system with both active and passive elements. Specifically, we consider three deployment schemes: 1) base station (BS)$\rightarrow $HIRS$\rightarrow $user (BHU); 2) BS$\rightarrow $AIRS$\rightarrow $PIRS$\rightarrow $user (BAPU); 3) BS$\rightarrow $PIRS$\rightarrow $AIRS$\rightarrow $user (BPAU). Under the line-of-sight channel model, we formulate a rate maximization problem via a joint optimization of the IRS element allocation and placement. We first derive the optimized number of active and passive elements for BHU, BAPU, and BPAU schemes, respectively. Then, low-complexity HIRS/AIRS placement strategies are provided. To obtain more insights, we characterize the system capacity scaling orders for the three schemes with respect to the large total number of IRS elements, amplification power budget, and BS transmit power. Finally, simulation results are presented to validate our theoretical findings and show the performance difference among the BHU, BAPU, and BPAU schemes with the proposed joint design under various system setups.

While single reconfigurable intelligent surface (RIS)- aided systems have demonstrated potential in improving transmission performance, their ability to serve multi-user scenarios is inherently limited. In this paper, we investigate the deployment of multiple RISs to assist downlink (DL) millimeter-wave (mmWave) non-orthogonal multiple access (NOMA) communications between a base station (BS) and multiple users. For maximizing the system achievable throughput, we propose a two-stage resource allocation scheme. In the first stage, a two-step RIS-user pairing scheme is proposed by combining the Gale-Shapley (GS) algorithm and the worst connection swapping (WCS) algorithm. In the second stage, based on the pairing results from the first stage, an alternating optimization (AO) algorithm is utilized to iteratively optimize RIS phase shifts, digital precoder, and power allocation. During each iteration, with fixed digital precoder and arbitrary feasible power allocation, the optimal RIS phase shifts can be obtained by the difference of convex (D. C.) programming and semidefinite relaxation (SDR) techniques. Then, the minimum mean-squared error (MMSE)-based digital precoder is adopted. Based on the derived RIS phase shifts and digital precoder, the optimized power allocation can be derived by reusing the D. C. programming subject to the constraint of decoding rate for successive interference cancellation (SIC). Simulation results show that the proposed pairing scheme can achieve near-optimal system achievable throughput, while significantly reducing the computational complexity. In addition, the proposed two-stage resource allocation scheme also demonstrates advantages in improving the overall achievable throughput of the system.

In this paper, a reconfigurable intelligent surface(RIS)-assisted millimeter-wave (mmWave) non-orthogonal multiple access (NOMA) communication system is considered, where the RIS is virtually partitioned into several sub-RISs to serve different user clusters. We aim to maximize the sum achievable rate of the considered system by jointly optimizing user clustering, RIS partitioning, active beamforming, passive beamforming, and power allocation, subject to the quality-of-service (QoS) requirements of each user and the maximum transmit power constraint of the base station (BS). To tackle the formulated non-convex joint optimization problem, a three-stage algorithm based on machine learning (ML) is proposed. In the first stage, user clustering is performed by combining the K-means algorithm and the Gaussian mixture model (GMM). In the second stage, a deep learning (DL)-based RIS partitioning method is proposed, which utilizes double cascaded deep neural networks (DNNs) to partition the RIS into multiple sub-RISs virtually, each of which serves a user cluster. In the third stage, a deep reinforcement learning (DRL) algorithm based on deep deterministic policy gradient (DDPG) is invoked to obtain the active beamforming, passive beamforming, and power allocation. Simulation results demonstrate that the proposed RIS partitioning method can effectively improve the utilization of RIS and thereby increase the total throughput of the system. Moreover, the sum achievable rate obtained by the proposed ML-based three-stage algorithm is higher than that of the conventional alternating optimization (AO) algorithm.

In this paper, a reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) multi-hop communication system is considered, where the base station (BS) transmits signals to remote users via multi-hop paths formed by multiple RISs. We aim to maximize the sum achievable rate by jointly optimizing multi-hop path scheduling, active beamforming, and passive beamforming, while satisfying the maximum transmit power of the BS. To tackle the formulated non-convex joint optimization problem, a machine learning (ML)-based two-stage algorithm is proposed. In the first stage, a multi-hop path scheduling algorithm based on graph neural networks (GNN) is investigated. Specifically, the considered system is first modeled as a graph topology, where the BS, RIS, and users are all served as nodes. Then, the weights between adjacent nodes associated with the path gain and the number of RIS reflection units are defined based on the expressions of RIS-assisted equivalent channels. Finally, a GNN-based multi-hop path scheduling algorithm is proposed. In the second stage, according to the obtained optimal multi-hop path, the deep deterministic policy gradient (DDPG) strategy is adopted to optimize the active beamforming of the BS and the passive beamforming of the selected RISs. Simulation results demonstrate that the proposed GNN-based multi-hop path scheduling algorithm maintains an error within 3% compared to the global optimal solution and outperforms the graph-based methods. Additionally, the proposed two-stage algorithm improves the sum achievable rate by approximately 27.6% compared to the alternating optimization (AO) algorithm.

In this paper, we investigate a novel intelligent reflecting surface (IRS)-aided multiuser communication system, where a multi-antenna base station (BS) integrated with an IRS simultaneously serves multiple low-mobility and high-mobility users via transmit diversity and active/passive precoding, respectively. Specifically, we exploit IRS's common phase shift to help achieve transmit diversity for high-mobility users without any channel state information (CSI), while incorporating the active/passive precoding design into the IRS-integrated BS to serve low-mobility users with known CSI. Then, we formulate and solve a new problem to minimize the total transmit power at the BS by jointly optimizing the reflect precoding at the IRS and the transmit precoding at the BS to cope with interference among different users. Simulation results validate the performance superiority of our proposed IRS-aided multiuser communication.

While traditional electromagnetic stealth materials/metasurfaces can render a target virtually invisible to some extent, they lack flexibility and adaptability, and can only operate within a limited frequency and angle range, making it challenging to ensure the expected stealth performance. In view of this, we propose in this paper a new intelligent reflecting surface (IRS)-aided electromagnetic stealth system mounted on targets to evade radar detection, by utilizing the tunable passive reflecting elements of IRS to achieve flexible and adaptive electromagnetic stealth in a cost-effective manner. Specifically, we optimize the IRS's reflection at the target to minimize the sum received signal power of all adversary radars. We first address the IRS's reflection optimization problem using the Lagrange multiplier method and derive a semi-closed-form optimal solution. To meet real-time processing requirements, we further propose a low-complexity closed-form solution based on the minimum mean-square error (MMSE) principle. Simulation results validate the performance advantages of our proposed IRS-aided electromagnetic stealth system with the proposed IRS reflection designs.

This paper studies the physical layer security for a double reconfigurable intelligent surface (RIS) aided wireless powered communication system in the presence of an eaves-dropper, where a user acts as friendly jammer (J) to emit the artificial noise to improve the transmission security of another user. In particular, three RIS transmission schemes are proposed: 1) In scheme-I, both RISs are designed as optimal phase shift of wireless user (U); 2) In scheme-II, the first RIS is designed as optimal phase shift of U and the second RIS is designed as optimal phase shift of J, respectively; 3) In scheme-III, the first RIS is designed as optimal phase shift of J and the second RIS is designed as optimal phase shift of U, respectively. To reveal the achieved reliability and security performance of proposed three schemes, we analyze the connection outage probability (COP) and secrecy outage probability (SOP), respectively. The results show that scheme-II and scheme-III have the same COP performance and both of them are worse than scheme-I. The scheme-III achieves the best SOP performance, while scheme-I achieves the worst SOP performance that is slightly inferior to scheme-II.

Reconfigurable intelligent surface (RIS) is viewed as a promising technique that can be utilized to improve the performance of systems by reconfiguring signal propagation environments. This article investigates green and secure unmanned aerial vehicle (UAV) Internet of Things (IoT) communications with the aid of RIS, where multiple UAVs harvest energy from a power beacon (PB) and send information uplink to access point (AP) with nonorthogonal multiple access (NOMA). In particular, communication can be divided into two phases during each time frame: 1) energy transfer and 2) information transmission (IT). Three RIS deployment strategies are proposed. In mode I, RISs are deployed between UAVs and AP to enhance the IT. In mode II, RISs are deployed between PB and UAVs to enhance the energy transfer. In mode III, RISs are deployed between UAVs and a hybrid AP (HAP) to enhance energy transfer and IT simultaneously. Considering phase compensation error caused by imperfect conditions, we define and evaluate ergodic capacity (EC), EC probability (ECP) and ergodic secrecy capacity (ESC) of three modes to measure the reliability and security of the system. The asymptotic expressions are also derived for further insights. Numerical results are presented to validate the correctness of theoretical derivations. Results demonstrate that the passive beamforming gain promised by RIS can significantly enhance the performance of systems. Mode III outperforms other modes in terms of reliability and security. When the transmission power and the number of UAVs increase, the ESCs of modes I and III converge to the same performance floor.

Intelligent surfaces (ISs) have emerged as a key technology to empower a wide range of appealing applications for wireless networks, due to their low cost, high energy efficiency, flexibility of deployment, and capability of constructing favorable wireless channels/radio environments. Moreover, the recent advent of several new IS architectures further expanded their electromagnetic functionalities from passive reflection to active amplification, simultaneous reflection, and refraction, as well as holographic beamforming. However, the research on ISs is still in rapid progress and there have been recent technological advances in ISs and their emerging applications that are worthy of a timely review. Thus, in this article, we provide a comprehensive survey on the recent development and advances of ISs-aided wireless networks. Specifically, we start with an overview on the anticipated use cases of ISs in future wireless networks such as 6G, followed by a summary of the recent standardization activities related to ISs. Then, the main design issues of the commonly adopted reflection-based IS and their state-of-the-art solutions are presented in detail, including reflection optimization, deployment, signal modulation, wireless sensing, and integrated sensing and communications. Finally, recent progress and new challenges in advanced IS architectures are discussed to inspire future research.

Extremely large-scale array (XL-array) and Terahertz (THz) band have emerged as key research areas in the sixth generation (6G) network. As such, the near-field communication becomes more important since users are more likely to be located in the near-field. Different from their far-field counterparts, the near-field focusing beams utilize additional distance information to focus beam energy at specific spatial-locations. In this letter, we consider a uniform circular array (UCA) system and propose a fast distance sampling method for UCA and design its codebook. Specifically, we propose the non-uniform distance sampling and codebook for UCA by utilizing the zero-order Bessel function, and taking into account the effect of the distance information on the correlation between the near-field beam vectors of UCA. Based on the proposed non-uniform distance sampling and codebook, a two-stage searching scheme for three-dimensional (3D) beam focusing is designed. Simulation results verify that the designed beam focusing scheme can achieve comparable accuracy and rate performance as compared to the 3D exhaustive search.

High-speed train (HST) communication plays a crucial role in providing reliable data services to passengers inside the trains. However, due to the train’s high mobility, a fast time-varying channel generally exists between the static BS and high-speed users, resulting in severe communication performance degradation. To address this issue, we propose in this paper an intelligent reflecting surface (IRS)-aided HST relaying system, where an IRS is integrated with the relay to aid the data transmission from the BS to the relay. Specifically, an optimization problem is formulated to maximize the received signal-to-noise ratio (SNR) at the relay by jointly optimizing the active transmit beamforming at the BS, the active receive beamforming at the relay, and the passive reflect beamforming at the IRS. To solve this problem, we first decouple it into two simpler sub-problems by leveraging the low-dimensional channel decomposition of the high-dimensional BS-relay channel matrix, and then solve them in closed-form with low complexity. Additionally, an efficient transmission protocol tailored for HST communication systems is proposed to implement channel estimation and beam tracking with low complexity. Simulation results verify the performance gains of the proposed IRS-aided HST relaying system, compared with the traditional relaying scheme without IRS and other benchmark schemes.

This article investigates a reliable and secure wireless-powered communication system assisted by a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) serving indoor and outdoor users. To align with practical application, we consider two eavesdropping conditions: mixed and indirect eavesdropping links. To improve the reliability and security of downlink energy transfer (ET) and uplink information transmission (IT), we propose four STAR-RIS schemes utilizing time-switching (TS) and energy-splitting (ES) protocols: 1) The dual-TS (DTS) scheme employs the TS protocol for both downlink ET and uplink IT; 2) The mixed ES-TS (MET) scheme switches from the ES protocol in downlink ET to the TS protocol in uplink IT; 3) The dual-ES (DES) scheme employs the ES protocol for both downlink ET and uplink IT; 4) The mixed TS-ES (MTE) scheme switches from the TS protocol in downlink ET to the ES protocol in uplink IT. Further, accurate and asymptotic connection outage probability, secrecy outage probability, and effective secrecy throughput are analyzed for each proposed scheme. Theoretical analysis and simulation results demonstrate that: 1) At high transmission power or with a large number of STAR-RIS elements, the MTE scheme achieves the highest reliability. Conversely, the DES scheme provides the best reliability at lower transmission power or with fewer STAR-RIS elements; 2) Under indirect eavesdropping links, the DES scheme achieves the highest security, followed by the MTE, MET, and DTS schemes. However, this performance order is reversed under mixed eavesdropping links; 3) The DES scheme achieves the best overall performance, followed by the MTE, MET, and DTS schemes, all of which outperform the benchmark schemes.

Intelligent reflecting surface (IRS) has become a cost-effective solution for constructing a smart and adaptive radio environment. Most previous works on IRS have jointly designed the active and passive precoding based on perfectly or partially known channel state information (CSI). However, in delay-sensitive or high-mobility communications, it is imperative to explore more effective methods for leveraging IRS to enhance communication reliability without the need for any CSI. In this paper, we investigate an innovative IRS-aided multiuser communication system, which integrates an IRS with its aided multi-antenna base station (BS) to simultaneously serve multiple high-mobility users through transmit diversity and multiple low-mobility users through active/passive precoding. In specific, we first reveal that when dynamically tuning the IRS’s common phase-shift shared with all reflecting elements, its passive precoding gain to any low-mobility user remains unchanged. Inspired by this property, we utilize the design of common phase-shift at the IRS for achieving transmit diversity to serve high-mobility users, yet without requiring any CSI at the BS. Meanwhile, the active/passive precoding design is incorporated into the IRS-integrated BS to serve low-mobility users (assuming the CSI is known). Then, taking into account the interference among different users, we formulate and solve a joint optimization problem of the IRS’s reflect precoding and the BS’s transmit precoding, with the aim of minimizing the total transmit power at the BS. Simulation results demonstrate that our proposed co-design of transmit diversity and active/passive precoding in IRS-aided multiuser systems can achieve superior and desirable performance compared to other benchmarks.

Intelligent reflecting surface (IRS) has emerged as a promising technology to enhance wireless communication performance via passive signal reflection. Prior works on IRS have mainly focused on investigating IRS's passive beamforming design to enhance the communication rate for users assuming that their channel state information (CSI) is fully or partially known. However, how to exploit IRS to enhance the wireless transmission reliability without any CSI, which is typical in high-mobility/delay-sensitive communication scenarios, remains largely open in the literature. In this paper, we study a new IRS-aided communication system with the IRS integrated into its aided access point (AP) to achieve the new function of transmit diversity. Specifically, we design the common phase-shift of IRS elements to achieve transmit diversity at the AP side without the need of any prior CSI of the users. Furthermore, we analyze the performance of IRS-aided transmit diversity in terms of average channel gain and symbol error rate (SER) to get some useful insights. Numerical results validate our analysis and show the performance gains of the proposed IRS-aided transmit diversity over other benchmark schemes.

Prior studies on intelligent reflecting surface (IRS) have mostly considered wireless communication systems aided by a single passive IRS, which, however, has limited control over wireless propagation environment and suffers severe product-distance path-loss. To address these issues, we propose in this paper a new multi-active multi-passive (MAMP) -IRS aided wireless communication system, where a number of active and passive IRSs are deployed to assist the communication between a base station (BS) and a remote user in complex environment, by establishing a multi-hop reflection path across active and passive IRSs. In particular, the active IRSs enable to opportunistically amplify the reflected signal along the multi-reflection link, thus effectively compensating for the severe product-distance path-loss. For the new MAMP-IRS aided system, an optimization problem is formulated to maximize the achievable rate of a typical user by designing the active-and-passive IRS routing path as well as the joint beamforming of the BS and selected active/passive IRSs. To draw useful insights into the optimal design, we first consider a special case of the single-active multi-passive (SAMP) -IRS aided system. For this case, we propose an efficient algorithm to obtain its optimal solution by first optimizing the joint beamforming given any SAMP-IRS routing path, and then optimizing the routing path by using a new path decomposition method and graph theory. Moreover, we show that the active IRS should be selected to establish the beam routing path when its amplification power and/or number of active reflecting elements are sufficiently large. Next, for the general MAMP-IRS aided system, we show that its challenging beam routing optimization problem can be efficiently solved by a new two-phase approach. Its key idea is to first optimize the inner passive-IRS beam routing between each two active IRSs for effective channel power gain maximization, followed by an outer active-IRS beam routing optimization for rate maximization. Last, numerical results are provided to validate our analytical results and demonstrate the effectiveness of the proposed MAMP-IRS beam routing scheme as compared to various benchmark schemes.

Intelligent reflecting surface (IRS) has emerged as a promising technique to control wireless propagation environment for enhancing the communication performance cost-effectively. However, the rapidly time-varying channel in high-mobility communication scenarios such as vehicular communication renders it challenging to obtain the instantaneous channel state information (CSI) efficiently for IRS with a large number of reflecting elements. In this paper, we propose a new roadside IRS-aided vehicular communication system to tackle this challenge. Specifically, by exploiting the symmetrical deployment of IRSs with inter-laced equal intervals on both sides of the road and the cooperation among nearby IRS controllers, we propose a new two-stage channel estimation scheme with off-line and online training, respectively, to obtain the static/time-varying CSI required by the proposed low-complexity passive beamforming scheme efficiently. The proposed IRS beamforming and online channel estimation designs leverage the existing uplink pilots in wireless networks and do not require any change of the existing transmission protocol. Moreover, they can be implemented by each of IRS controllers independently, without the need of any real-time feedback from the user’s serving BS. Simulation results show that the proposed designs can efficiently achieve the high IRS passive beamforming gain and thus significantly enhance the achievable communication throughput for high-speed vehicular communications.

Intelligent reflecting surface (IRS) has emerged as a cost-effective solution to enhance wireless communication performance via passive signal reflection. Existing works on IRS have mainly focused on investigating IRS’s passive beamforming/reflection design to boost the communication rate for users assuming that their channel state information (CSI) is fully or partially known. However, how to exploit IRS to improve the wireless transmission reliability without any CSI, which is typical in high-mobility/delay-sensitive communication scenarios, remains largely open. In this paper, we study a new IRS-aided communication system with the IRS integrated to its aided access point (AP) to achieve both functions of transmit diversity and passive beamforming simultaneously. Specifically, we first show an interesting result that the IRS’s passive beamforming gain in any channel direction is invariant to the common phase-shift applied to all of its reflecting elements. Accordingly, we design the common phase-shift of IRS elements to achieve transmit diversity at the AP side without the need of any CSI of the users. In addition, we propose a practical method for the users to estimate the CSI at the receiver side for information decoding. Meanwhile, we show that the conventional passive beamforming gain of IRS can be retained for the other users with their CSI known at the AP. Furthermore, we derive the asymptotic performance of both IRS-aided transmit diversity and passive beamforming in closed-form, by considering the large-scale IRS with an infinite number of elements. Numerical results validate our analysis and show the performance gains of the proposed IRS-aided simultaneous transmit diversity and passive beamforming scheme over other benchmark schemes.

Low-earth orbit (LEO) satellite communication plays an important role in assisting/complementing terrestrial communications by providing worldwide coverage, especially in harsh environments such as high seas, mountains, and deserts which are uncovered by terrestrial networks. Traditionally, the passive reflect-array with fixed phase shifts has been applied in satellite communications to compensate for the high path loss due to long propagation distance with low-cost directional beamforming; however, it is unable to flexibly adapt the beamforming direction to dynamic channel conditions. In view of this, we consider in this paper a new intelligent reflecting surface (IRS)-aided LEO satellite communication system, by utilizing the controllable phase shifts of massive passive reflecting elements to achieve flexible beamforming, which copes with the time-varying channel between the high-mobility satellite (SAT) and ground node (GN) cost-effectively. In particular, we propose a new architecture for IRS-aided LEO satellite communication where IRSs are deployed at both sides of the SAT and GN, and study their cooperative passive beamforming (CPB) design over line-of-sight (LoS)-dominant single-reflection and double-reflection channels. Specifically, we jointly optimize the active transmit/receive beamforming at the SAT/GN as well as the CPB at two-sided IRSs to maximize the overall channel gain from the SAT to each GN. Interestingly, we show that under LoS channel conditions, the high-dimensional SAT-GN channel can be decomposed into the outer product of two low-dimensional vectors. By exploiting the decomposed SAT-GN channel, we decouple the original beamforming optimization problem into two simpler subproblems corresponding to the SAT and GN sides, respectively, which are both solved in closed-form. Furthermore, we propose an efficient transmission protocol to conduct channel estimation and beam tracking, which only requires independent processing of the SAT and GN in a distributed manner, thus substantially reducing the implementation complexity. Simulation results validate the performance advantages of the proposed IRS-aided LEO satellite communication system with two-sided cooperative IRSs, as compared to various baseline schemes such as the conventional reflect-array and one-sided IRS.

Intelligent reflecting surface (IRS) has emerged as a promising technique for wireless communication networks. By dynamically tuning the reflection amplitudes/phase shifts of a large number of passive elements, IRS enables flexible wireless channel control and configuration and thereby enhances the wireless signal transmission rate and reliability significantly. Despite the vast literature on designing and optimizing assorted IRS-aided wireless systems, prior works have mainly focused on enhancing wireless links with single signal reflection only by one or multiple IRSs, which may be insufficient to boost the wireless link capacity under some harsh propagation conditions (e.g., indoor environment with dense blockages/obstructions). This issue can be tackled by employing two or more IRSs to assist each wireless link and jointly exploiting their single as well as multiple signal reflections over them. However, the resultant double-/multi-IRS-aided wireless systems face more complex design issues as well as new practical challenges for implementation compared to the conventional single-IRS counterpart, in terms of IRS reflection optimization, channel acquisition, as well as IRS deployment and association/selection. As such, a new paradigm for designing multi-IRS cooperative passive beamforming and joint active/passive beam routing arises, which calls for innovative design approaches and optimization methods. In this article, we give a tutorial overview of multi-IRS-aided wireless networks, with an emphasis on addressing the new challenges due to multi-IRS signal reflection and routing. Moreover, we point out important directions worthy of research and investigation in the future.

Intelligent reflecting/refracting surface (IRS) has recently emerged as a promising solution to reconfigure wireless propagation environment for enhancing the communication performance by tuning passive signal reflection or refraction. In this paper, we study a new IRS-aided high-mobility communication system by employing the intelligentrefractingsurface with a high-speed vehicle to aid its passenger’s communication with a remote base station (BS). Due to the environment’s random scattering and vehicle’s high mobility, a rapidly time-varying channel is typically resulted between the static BS and fast-moving IRS/user, which renders the channel estimation for IRS with a large number of passive refracting elements more challenging, as compared to that for the conventional slow fading IRS channels with low-mobility users. In order to reap the high IRS passive beamforming gain with low channel training overhead, we propose a new and efficient two-stage transmission protocol to achieve both IRS channel estimation and refraction optimization for data transmission. Specifically, by exploiting the quasi-static channel between the IRS and user both moving at the same high speed as well as the line-of-sight (LoS) dominant channel between the BS and IRS, the user first estimates the LoS component of the cascaded BS-IRS-user channel in Stage I, based on which IRS passive refraction is designed to maximize the corresponding IRS-refracted channel gain. Then, the user estimates the resultant IRS-refracted channel as well as the non-IRS-refracted channel in Stage II for setting an additional common phase shift at all IRS refracting elements so as to align these two channels for maximizing the overall channel gain for data transmission. Simulation results show that the proposed design can efficiently achieve the full IRS passive beamforming gain in the high-mobility communication scenario, which also converts the overall BS-user channel from fast to slow fading for more reliable transmission. The proposed on-vehicle IRS system is further compared with a baseline scheme of deploying fixed IRSs (intelligent reflecting surfaces) on the roadside to assist high-speed vehicular communications, which achieves significant rate improvement due to its greatly saved channel training time.

In this paper, we study a new intelligent refracting surface (IRS)-assisted high-mobility communication with the IRS deployed in a high-speed moving vehicle to assist its passenger’s communication with a static base station (BS) on the roadside. The vehicle’s high Doppler frequency results in a fast fading channel between the BS and the passenger/user, which renders channel estimation for the IRS with a large number of refracting elements a more challenging task as compared to the conventional case with low-mobility users only. In order to mitigate the Doppler effect and reap the full IRS passive beamforming gain with low training overhead, we propose a new and efficient transmission protocol to execute channel estimation and IRS refraction design for data transmission. Specifically, by exploiting the quasi-static channel between the IRS and user both moving at the same high speed, we first estimate the cascaded BS-IRS-user channel with the Doppler effect compensated. Then, we estimate the instantaneous BS-user fast fading channel (without IRS refraction) and tune the IRS refraction over time accordingly to align the cascaded channel with the BS-user direct channel, thus maximizing the IRS’s passive beamforming gain as well as converting their combined channel from fast to slow fading. Simulation results show the effectiveness of the proposed channel estimation scheme and passive beamforming design as compared to various benchmark schemes.

To achieve the more promising passive beamforming gains in the double-intelligent reflecting surface (IRS) assisted system over the conventional single-IRS system, channel estimation is practically indispensable but also a more challenging problem to tackle, due to the presence of not only the single-but also double-reflection links that are intricately coupled. In this paper, we propose a new and efficient channel estimation scheme for the double-IRS assisted uplink multiple-input multiple-output (MIMO) communication system to resolve the cascaded channel state information (CSI) of both its single- and double-reflection links. First, for the single-user case, the higher-dimensional double-reflection channel is efficiently estimated at the multi-antenna base station (BS) with low training overhead by exploiting the fact that its cascaded channel coefficients are scaled versions of those of a lower-dimensional single-reflection channel. Then, the proposed channel estimation scheme is extended to the multi-user case, where given an arbitrary user’s cascaded channel estimated as in the single-user case, the other users’ cascaded channels are scaled versions of it and thus can be estimated with reduced training overhead. Simulation results verify the effectiveness of the proposed channel estimation scheme as compared to the benchmark scheme.

Intelligent reflecting surface (IRS) is an enabling technology to engineer the radio signal propagation in wireless networks. By smartly tuning the signal reflection via a large number of low-cost passive reflecting elements, IRS is capable of dynamically altering wireless channels to enhance the communication performance. It is thus expected that the new IRS-aided hybrid wireless network comprising both active and passive components will be highly promising to achieve a sustainable capacity growth cost-effectively in the future. Despite its great potential, IRS faces new challenges to be efficiently integrated into wireless networks, such as reflection optimization, channel estimation, and deployment from communication design perspectives. In this paper, we provide a tutorial overview of IRS-aided wireless communications to address the above issues, and elaborate its reflection and channel models, hardware architecture and practical constraints, as well as various appealing applications in wireless networks. Moreover, we highlight important directions worthy of further investigation in future work.

To achieve the more significant passive beamforming gain in the double-intelligent reflecting surface (IRS) aided system over the conventional single-IRS counterpart, channel state information (CSI) is indispensable in practice but also more challenging to acquire, due to the presence of not only the single- but also double-reflection links that are intricately coupled and also entail more channel coefficients for estimation. In this paper, we propose a new and efficient channel estimation scheme for the double-IRS aided multi-user multiple-input multiple-output (MIMO) communication system to resolve the cascaded CSI of both its single- and double-reflection links. First, for the single-user case, the single- and double-reflection channels are efficiently estimated at the multi-antenna base station (BS) with both the IRSs turned ON (for maximal signal reflection), by exploiting the fact that their cascaded channel coefficients are scaled versions of their superimposed lower-dimensional CSI. Then, the proposed channel estimation scheme is extended to the multi-user case, where given an arbitrary user's cascaded channel (estimated as in the single-user case), the other users' cascaded channels can also be expressed as lower-dimensional scaled versions of it and thus efficiently estimated at the BS. Simulation results verify the effectiveness of the proposed channel estimation scheme and joint training reflection design for double IRSs, as compared to various benchmark schemes.

Recent research on reconfigurable intelligent surfaces (RISs) suggests that RISs can perform passive beamforming and information transfer (PBIT) simultaneously via smart reflections. In this paper, we propose an RIS-enhanced multiple-input single-output system with reflection pattern modulation (RPM) to achieve PBIT, where the joint active and passive beamforming is carefully designed by taking into account the communication outage probability. We formulate an optimization problem to maximize the average received signal power by jointly optimizing the active beamforming at the access point (AP) and passive beamforming at the RIS under the assumption that the RIS's state information is statistically known by the AP, and propose a high-quality suboptimal solution based on the alternating optimization technique. Moreover, a closed-form expression for the asymptotic outage probability of the proposed scheme in Rician fading is derived. The achievable rate of the proposed scheme is also investigated under the assumption that the transmitted symbols are drawn from a finite constellation. Simulation results validate the effectiveness of the proposed scheme and reveal the effect of various system parameters on the achievable rate. It is shown that the proposed scheme outperforms, in terms of achievable rate, the conventional RIS-assisted system without information transfer.

Intelligent reflecting surface (IRS) has emerged as an enabling technology to achieve smart and reconfigurable wireless communication environment cost-effectively. Prior works on IRS mainly consider its passive beamforming design and performance optimization without the inter-IRS signal reflection, which thus do not unveil the full potential of multi-IRS assisted wireless networks. In this paper, we study a double-IRS assisted multi-user communication system with the cooperative passive beamforming design that captures the multiplicative beamforming gain from the inter-IRS channel. Under the general channel setup with the co-existence of both double- and single-reflection links, we jointly optimize the (active) receive beamforming at the base station (BS) and the cooperative (passive) reflect beamforming at the two distributed IRSs (deployed near the BS and users, respectively) to maximize the minimum signal-to-interference-plus-noise ratio (SINR) of all users. Moreover, for the single-user and multi-user setups, we analytically show the superior performance of the double-IRS cooperative system over the conventional single-IRS system in terms of the maximum signal-to-noise ratio (SNR) and multi-user effective channel rank, respectively. Simulation results validate our analytical results and show the practical advantages of the proposed double-IRS system with cooperative passive beamforming designs.

Reconfigurable intelligent surfaces (RISs) have recently emerged as an innovative technology for improving the energy and/or spectrum efficiency of future wireless communications. In this paper, we propose a transmission protocol for the wideband RIS-assisted single-input multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) system to execute channel estimation and passive beamforming simultaneously. A new channel estimation method is proposed to progressively resolve the channel state information (CSI) over the training symbols, based on which the passive beamforming at the RIS is optimized to improve the channel gains on the data tones in the remaining training symbols. Based on the incomplete CSI, we formulate an optimization problem to maximize the average achievable rate by designing the passive beamforming at the RIS, which needs to balance the received signal power over different subcarriers and different receive antennas. Moreover, we propose a low-complexity algorithm to find a high-quality solution for the formulated problem. Simulation results validate the effectiveness of the proposed channel estimation and beamforming optimization methods.

Recent considerations for reconfigurable intelligent surfaces (RISs) assume that RISs can convey information by reflection without the need of transmit radio frequency chains, which, however, is a challenging task. In this paper, we propose an RIS-enhanced multiple-input single-output system with reflection pattern modulation, where the RIS can configure its reflection state for boosting the received signal power via passive beam-forming and simultaneously conveying its own information via reflection. We formulate an optimization problem to maximize the average received signal power by jointly optimizing the active beamforming at the access point (AP) and passive beamforming at the RIS for the case where the RIS's information is statistically known by the AP. Since the formulated problem is non-convex and thus difficult to solve optimally, we propose an efficient algorithm based on the alternating optimization technique to find a high-quality solution. Simulation results validate the effectiveness of the proposed reflection pattern modulation and beamforming design. It is shown that the proposed scheme outperforms the conventional RIS-assisted system with full RIS reflection in terms of achievable rate performance.

As an important role in achieving seamless world-wide coverage for the maritime communication, the VDE satellite component (VDE-SAT) aims to provide efficient services to a large number of ship terminals each with only a limited satellite coverage time, which requires efficient resource allocation strategies. In this paper, we investigate the downlink VDE-SAT system consisting of a low-earth orbiting (LEO) satellite communicating with multiple randomly distributed ship terminals. As the LEO satellite orbits the Earth at a fixed altitude and speed, ship terminals are served in a periodic manner with known but periodically time-varying channel gains from the satellite. By exploiting the periodic channel gain variations in the line-of-sight (LoS) dominant satellite-ship channels, we propose an angle partition based time division multiple access (TDMA) scheme to schedule downlink communications from the satellite to ship terminals based on the moving satellite's real-time position. Specifically, time resources allocated to different ship terminals are optimized via the partition of the satellite's azimuth angle range to maximize their minimum/common throughput. Simulation results verify the effectiveness of the proposed angle partition-based TDMA scheme and show its performance improvement over benchmark schemes.

In this paper, we consider an intelligent reflecting surface (IRS)-aided single-user system where an IRS with discrete phase shifts is deployed to assist the uplink communication. A practical transmission protocol is proposed to execute channel estimation and passive beamforming successively. To minimize the mean square error (MSE) of channel estimation, we first formulate an optimization problem for designing the IRS reflection pattern in the training phase under the constraints of unit-modulus, discrete phase, and full rank. This problem, however, is NP-hard and thus difficult to solve in general. As such, we propose a low-complexity yet efficient method to solve it sub-optimally, by constructing a near-orthogonal reflection pattern based on either discrete Fourier transform (DFT)-matrix quantization or Hadamard-matrix truncation. Based on the estimated channel, we then formulate an optimization problem to maximize the achievable rate by designing the discrete-phase passive beamforming at the IRS with the training overhead and channel estimation error taken into account. To reduce the computational complexity of exhaustive search, we further propose a low-complexity successive refinement algorithm with a properly-designed initialization to obtain a high-quality suboptimal solution. Numerical results are presented to show the significant rate improvement of our proposed IRS training reflection pattern and passive beamforming designs as compared to other benchmark schemes.

Reconfigurable intelligent surfaces (RISs) have recently emerged as an innovative technology for improving the coverage, throughput, and energy/spectrum efficiency of future wireless communications. In this paper, we propose a new transmission protocol for wideband RIS-assisted single-input multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) communication systems, where each transmission frame is divided into multiple sub-frames to execute channel estimation simultaneously with passive beamforming. As the training symbols are discretely distributed over multiple sub-frames, the channel state information (CSI) associated with RIS cannot be estimated at once. As such, we propose a new channel estimation method to progressively estimate the associated CSI over consecutive sub-frames, based on which the passive beamforming at the RIS is fine-tuned to improve the achievable rate for data transmission. In particular, during the channel training, the RIS plays two roles of embedding training reflection states for progressive channel estimation and performing passive beamforming for data transmission on the data tones. Based on the estimated CSI in each sub-frame, we formulate an optimization problem to maximize the average achievable rate by designing the passive beamforming at the RIS, which needs to balance the received signal power over different sub-carriers and different receive antennas. As the formulated problem is non-convex and thus difficult to solve optimally, we propose two efficient algorithms to find high-quality solutions. Simulation results validate the effectiveness of the proposed channel estimation and beamforming optimization methods. It is shown that the proposed joint channel estimation and passive beamforming scheme is able to drastically improve the average achievable rate and reduce the delay for data transmission as compared to existing schemes.

Prior studies on intelligent reflecting surface (IRS) have mostly assumed perfect channel state information (CSI) available for designing the IRS passive beamforming as well as the continuously adjustable phase shift at each of its reflecting elements, which, however, have simplified two challenging issues for implementing IRS in practice, namely, its channel estimation and passive beamforming designs both under the constraint of discrete phase shifts. To address them, we consider in this paper an IRS-aided single-user communication system and design the IRS training reflection matrix for channel estimation as well as the passive beamforming for data transmission, both subject to the new constraint of discrete phase shifts. We show that the training reflection matrix design with discrete phase shifts greatly differs from that with continuous phase shifts, and the corresponding passive beamforming design should take into account the correlated IRS channel estimation errors due to discrete phase shifts. Moreover, a novel hierarchical training reflection design is proposed to progressively estimate IRS elements' channels over multiple time blocks by exploiting the IRS-elements grouping and partition. Based on the resolved IRS channels in each block, we further design the progressive passive beamforming at the IRS with discrete phase shifts to improve the achievable rate for data transmission over the blocks. Extensive numerical results are presented, which demonstrate the significant performance improvement of proposed channel estimation and passive beamforming designs as compared to various benchmark schemes.

Intelligent reflecting surface (IRS) is a promising new technology for achieving both spectrum and energy efficient wireless communication systems in the future. However, existing works on IRS mainly consider frequency-flat channels and assume perfect knowledge of channel state information (CSI) at the transmitter. Motivated by the above, in this paper we study an IRS-enhanced orthogonal frequency division multiplexing (OFDM) system under frequency-selective channels and propose a practical transmission protocol with channel estimation. First, to reduce the overhead in channel training as well as exploit the channel spatial correlation, we propose a novel IRS elements grouping method, where each group consists of a set of adjacent IRS elements that share a common reflection coefficient. Based on this method, we propose a practical transmission protocol where only the combined channel of each group needs to be estimated, thus substantially reducing the training overhead. Next, with any given grouping and estimated CSI, we formulate the problem to maximize the achievable rate by jointly optimizing the transmit power allocation and the IRS passive array reflection coefficients. Although the formulated problem is non-convex and thus difficult to solve, we propose an efficient algorithm to obtain a high-quality suboptimal solution for it, by alternately optimizing the power allocation and the passive array coefficients in an iterative manner, along with a customized method for the initialization. Simulation results show that the proposed design significantly improves the OFDM link rate performance as compared to the case without using IRS. Moreover, it is shown that there exists an optimal size for IRS elements grouping which achieves the maximum achievable rate due to the practical trade-off between the training overhead and IRS passive beamforming flexibility.

To achieve the full passive beamforming gains of intelligent reflecting surface (IRS), accurate channel state information (CSI) is indispensable but practically challenging to acquire, due to the excessive amount of channel parameters to be estimated which increases with the number of IRS reflecting elements as well as that of IRS-served users. To tackle this challenge, we propose in this paper two efficient channel estimation schemes for different channel setups in an IRS-assisted multi-user broadband communication system employing the orthogonal frequency division multiple access (OFDMA). The first channel estimation scheme, which estimates the CSI of all users in parallel simultaneously at the access point (AP), is applicable for arbitrary frequency-selective fading channels. In contrast, the second channel estimation scheme, which exploits a key property that all users share the same (common) IRS-AP channel to enhance the training efficiency and support more users, is proposed for the typical scenario with line-of-sight (LoS) dominant user-IRS channels. For the two proposed channel estimation schemes, we further optimize their corresponding training designs (including pilot tone allocations for all users and IRS time-varying reflection pattern) to minimize the channel estimation error. Moreover, we derive and compare the fundamental limits on the minimum training overhead and the maximum number of supportable users of these two schemes. Simulation results verify the effectiveness of the proposed channel estimation schemes and training designs, and show their significant performance improvement over various benchmark schemes.

In this paper, we propose a coordinated direct and relay transmission scheme based on non-orthogonal multiple access (NOMA), where the relay and cell- center user operate in full-duplex mode to help enhance the quality of service of the cell-edge user as well as ensure the secure transmission. In the proposed system, the base station directly serves the cell-center user and relay while communicating with the cell-edge user through the relay and cell-center user. Taking advantage of NOMA, the cell-center user and relay are able to cancel the mutual interference between themselves. Moreover, they can also cooperatively apply the artificial noise scheme to interfere any potential eavesdropper without impairing the legitimate cell-edge user. Exact and closed-form expressions for the outage probability, achievable rate, and achievable secure rate are derived. Numerical results prove that the proposed system outperforms the existing counterpart in the low signal-to-noise ratio region, and ensures secure communications with appropriate power allocation.

To enjoy advantages of both the single-carrier (SC) transmission and the generalized spatial modulation (GSM), a hybrid architecture of SC-GSM has been developed in the literature. However, low-complexity demodulation for SC-GSM signals with near-optimal performance is challenging, especially in soft decision making. To this end, based on the deterministic sequential Monte Carlo (SMC) principle, we propose two low-complexity demodulators for the SC-GSM system with zero-padding (ZP) in broadband channels. By exploiting the natural triangular structure of the multi-path channel to apply SMC sampling, a vector-level SMC demodulator without the need of the sequential construction process is proposed, significantly saving the computational burden. To lower the required computation during the sampling and weight updating processes of the vector-level SMC demodulator, a symbol-level SMC demodulator is further proposed, which takes samples from the expanded constellation including the origin. In particular, an efficient online legality check scheme, which eliminates illegal samples during each sampling step, is integrated into the symbol-level SMC demodulator to avoid error propagation and save the computational burden dramatically. Both proposed demodulators produce soft outputs, which can be used for soft demodulation in coded systems, especially for turbo receivers. The simulation results verify the near-optimal performance of the two proposed demodulators in the uncoded and coded SC-GSM system with ZP, respectively.

Spatial modulation (SM) is an innovative and promising digital modulation technology that strikes an appealing tradeoff between spectral efficiency and energy efficiency with a simple design philosophy. SM enjoys plenty of benefits and shows great potential to fulfill the requirements of future wireless communications. The key idea behind SM is to convey additional information typically through the ON/OFF states of transmit antennas and simultaneously save the implementation cost by reducing the number of radio-frequency chains. As a result, the SM concept can have widespread effects on diverse applications and can be applied in other signal domains, such as frequency/time/code/angle domain or even across multiple domains. This survey provides a comprehensive overview of the latest results and progresses in SM research. Specifically, the fundamental principles, variants of system design, and enhancements of SM are described in detail. Furthermore, the integration of the SM family with other promising techniques, applications to emerging communication systems, and extensions to new signal domains are also extensively studied.

In this paper, we develop a secure non-orthogonal multiple access (NOMA)-based two-way relay network, in which two users wish to exchange their NOMA signals via a trusted relay in the presence of an eavesdropper. To ensure secure communications, the relay not only forwards confidential information to the legitimate users but also keeps emitting jamming signals all the time to confuse any potential eavesdropper. Specifically, we equip the relay and each user with the full-duplex technique in the multiple-access phase to combat the eavesdropping and improve the data transmission efficiency, respectively. Different decoding schemes based on the successive interference cancellation (SIC) are proposed for the legitimate users, relay, and eavesdropper. Closed-form expressions for the achievable ergodic secrecy rates of all data symbols are derived, validated by the excellent fitting to the computer simulation results for our proposed network.

In this paper, we develop a non-orthogonal multiple access (NOMA)-based two-way relay network with secrecy considerations, in which two users wish to exchange their NOMA signals via a trusted relay in the presence of single and multiple eavesdroppers. To ensure secure communications, the relay not only forwards confidential information to the legitimate users but also keeps emitting jamming signals all the time to degrade the performance of any potential eavesdropper. Moreover, we equip the relay and each user with the full-duplex technique in the multiple-access phase to combat the eavesdropping and improve the data transmission efficiency, respectively. We propose different decoding schemes based on the successive interference cancellation for the legitimate users, relay, and eavesdroppers. Closed-form expressions for the achievable ergodic secrecy rates of all data symbols under both single- and multiple-eavesdropper cases are derived, validated by the excellent fitting to the computer simulation results for our proposed network.

As an emerging multi-carrier transmission scheme, multiple-mode orthogonal frequency division multiplexing with index modulation (MM-OFDM-IM) conveys information through multiple distinguishable constellations (or modes, alternatively) and their full permutations, increasing the spectral efficiency of OFDM-IM and classical OFDM. However, both MM-OFDM-IM and its extension, that is, MM-OFDM with in-phase/quadrature index modulation (MM-OFDM-IM-IQ), cannot provide any transmit diversity gain, which may be critical for ultra-reliable communications. In this paper, aiming at enhancing the diversity gain of MM-OFDM-IM(-IQ) schemes, coordinate interleaved (CI-)MM-OFDM-IM and linear constellation precoded (LCP-) MM-OFDM-IM-IQ are proposed, both of which achieve a diversity order of two without loss of spectral efficiency. The optimal rotation angle for CI-MM-OFDM-IM and the optimal precoding matrix for LCP-MM-OFDM-IM-IQ, in the sense of maximizing their coding gains, are derived in closed form. Computer simulations corroborate the advantages of the proposed schemes in terms of diversity and bit error rate performance toward next-generation wireless networks.

Multiple-input multiple-output orthogonal frequency division multiplexing with index modulation (MIMO-OFDM-IM), which provides a flexible trade-off between spectral efficiency and error performance, is recently proposed as a promising transmission technique for energy-efficient 5G wireless communication systems. However, due to the dependence of subcarrier symbols within each subblock and the strong interchannel interference, it is challenging to detect the transmitted data effectively while imposing low computational burden to the receiver. In this paper, we propose a low-complexity detector based on the sequential Monte Carlo (SMC) theory for the detection of MIMO-OFDM-IM signals. The proposed detector, which draws samples based on the importance weights at the subblock level, achieves near-optimal error performance with considerably reduced computational complexity. Simulation and numerical results in terms of bit error rate (BER) and number of complex multiplications (NCM) corroborate the superiority of the proposed detector.

Recently, a new multiple-input multiple-output (MIMO) transmission scheme termed as receive spatial modulation (RSM) is proposed, which activates a subset of receive antennas to carry additional information bits through the index domain. In this paper, a novel variant of RSM, which is referred to as enhanced RSM (ERSM), is proposed to further improve the system performance, especially in high-mobility scenarios. For ERSM, the log-likelihood ratio (LLR) and maximum likelihood (ML) detectors are developed, which exhibit very low implementation complexity. With the proposed ML detector, the ERSM is analyzed using different performance metrics, including the asymptotic average bit error probability (ABEP), the exact coding gain, and the achievable rate. Both the analysis and simulation results corroborate the superiority of the proposed scheme.

In this paper, we develop a non-orthogonal multiple access (NOMA)-based multi-pair two-way relay (TWR) network, in which a rate splitting scheme and a successive group decoding strategy are employed. By exploiting the interference signals received from neighbor users with the leverage of the full-duplex technique, we enhance the decoding ability of each user and further achieve an effective multiuser interference management for the network. We propose different decoding strategies for different types of nodes by processing the received signals with only local incoming channel state information in different manners. Moreover, under the limited group decoding size, each individual node decodes its own desired messages along with a fraction of the interference successively. We further investigate the joint uplink and downlink fair rate allocation problem based on the max-min criterion, and the solution to which also contains the optimal group decoding schedule. Simulation results in terms of ergodic rate and outrage probability corroborate the superiority of our NOMA-based multi-pair TWR network over the OMA-based counterpart.

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) performs transmission by considering two modes over OFDM subcarriers, which are the null and the conventional$M$-ary signal constellation. The spectral efficiency (SE) of the system, however, is limited, since the null mode itself does not carry any information and the number of subcarrier activation patterns increases combinatorially. In this paper, a novel IM scheme, called multiple-mode OFDM-IM (MM-OFDM-IM), is proposed for OFDM systems to improve the SE by conveying information through multiple distinguishable modes and their full permutations. A practical and efficient mode selection strategy, which is constrained on the phase shift keying/quadrature amplitude modulation constellations, is designed. Two efficient detectors that provide different tradeoffs between the error performance and detection complexity are also proposed. The principle of MM-OFDM-IM is further extended to the in-phase and quadrature components of OFDM signals, and the method of generating multiple modes from the$M$-ary pulse amplitude modulation constellation for this modified scheme is also introduced. Bit error rate (BER) analyses are provided for the proposed schemes. Monte Carlo simulations on BER corroborate the analyses and show that the proposed schemes appear as promising multi-carrier transmission alternatives by outperforming the existing OFDM-IM counterparts.

Due to the interchannel interference and the varying active antennas in generalized spatial modulation (GSM), it is challenging to detect the transmitted data effectively while imposing low computational burden to the receiver. In this paper, we propose a novel algorithm based on the K-best sphere decoding for the soft detection of the GSM signal, which performs the breadth-first tree search in two stages sequentially. By exploiting the null space of the GSM channel, the inactive antenna searching is carried out antennawise in the first stage, in which a recursive algorithm for updating the QL decomposition (a decomposition of a matrix into a product of an orthogonal matrix and a lower triangular matrix) is proposed to calculate the branch metrics and an effective examination scheme is developed to prune those illegal or repetitive child nodes. Based on the available QL decomposition structure that has been updated recursively during the first stage, the second stage of the proposed detector is then readily concatenated to search for the modulated symbols carried on the active antennas in a one-by-one manner. Moreover, owing to the soft-input soft-output nature, the proposed detector can be applied in a turbo decoder for a coded GSM system. Both complexity analysis and simulation results corroborate the superiority of the proposed soft detector for the GSM system.

Generalized spatial modulation (GSM) is a relatively new multi-input multi-output transmission technique that enables a flexible trade-off between the achievable transmission rate and the cost of radio frequency chains. However, due to the constraint of transmit antenna combination and the variation of interchannel interference, the efficient low-complexity demodulation of GSM signals is challenging, especially when soft demodulation is needed. In this paper, we propose two low-complexity algorithms based on the deterministic sequential Monte Carlo (SMC) technique for the demodulation of GSM. The type-I SMC demodulator, which uses the conventional successive interference cancellation as the kernel and draws antenna-wise samples from the extended constellation, is proposed for the overdetermined GSM system. The type-II SMC demodulator, which consists of two stages and uses the orthogonal matching pursuit as the kernel in the first stage, is proposed for the underdetermined GSM system. A key component in both algorithms is an efficient online scheme to eliminate the illegal samples during the sampling process. Both proposed algorithms achieve near-optimal performances with complexity linear in terms of the antenna size. Moreover, owing to their soft-input soft-output nature, they can be employed in a turbo receiver for a coded GSM system.

Frequency domain pilot multiplexing technique (FDPMT) has recently emerged as an appealing technique for channel estimation in single-carrier frequency domain equalization systems as it achieves high spectral efficiency at the expense of tiny signal distortion. In FDPMT, pilot positions are dynamically selected to achieve a relatively low level of signal distortion, rendering pilot position detection (PPD) indispensable at the receiver. In this letter, by exploiting both the prior known pilots and the statistical information of the data tones, we propose a novel PPD scheme based on the maximum-likelihood criterion. Simulation results show that our proposed PPD scheme significantly improves the PPD accuracy and outperforms the existing counterparts in terms of bit error rate.

Orthogonal frequency division multiplexing (OFDM) is promising for underwater acoustic (UWA) communications as it is robust against large delay spread. However, OFDM suffers from intercarrier interference (ICI) caused by the serious Doppler effect in UWA channels. Attentive to the property that the ICI is mainly contributed by the adjacent subcarriers in UWA channels, in this paper, we propose a novel low-complexity frequency-domain equalizer to combat the ICI for OFDM UWA communications. Specifically, the coefficients of the equalizer are obtained by using the nor- malized gradient algorithm which is based on the minimum symbol error rate (MSER) criterion. The proposed equalizer uses very few (typical 3) FIR taps to mitigate the ICI for each subchannel, yet achieves better SER performance than the conventional equalizer based on the minimum mean square error (MMSE) criterion.