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.

This paper investigates an intelligent reflecting surface (IRS)-aided movable antenna (MA) system, where multiple IRSs cooperate with a multi-MA base station to extend wireless coverage to multiple target areas. The objective is to maximize the worst-case signal-to-noise ratio (SNR) across all locations within these areas through joint optimization of MA positions, IRS phase shifts, and transmit beamforming. To achieve this while balancing the performance-cost trade-off, we propose three coverage-enhancement schemes: thearea-adaptive MA-IRSscheme, where both the MA positions and IRS phase shifts are adaptively adjusted for each target area; thearea-adaptive MA-staIRSscheme, where only the MA positions are adjusted, while the IRS phase shifts remain unchanged after initial configuration (withstaIRSdenoting static IRSs); and theshared MA-staIRSscheme, where a common MA placement and static IRS configuration are applied across all areas. These schemes lead to challenging non-convex optimization problems with implicit objective functions, which are difficult to solve optimally. To address these problems, we propose a general algorithmic framework that can be applied to solve each problem efficiently albeit suboptimally. Simulation results demonstrate that: 1) the proposed MA-based schemes consistently outperform their fixed-position antenna (FPA)-based counterparts under both area-adaptive and static IRS configurations, with the area-adaptive MA-IRS scheme achieving the highest worst-case SNR; 2) as transmit antennas are typically far fewer than IRS elements, the area-adaptive MA-staIRS scheme may underperform the baseline FPA scheme with area-adaptive IRSs in terms of the worst-case SNR, but a modest increase in antenna number can reverse this trend; 3) under a fixed total cost, the optimal MA-to-IRS-element ratio for the worst-case SNR maximization is empirically found to be proportional to the reciprocal of their unit cost ratio.

This paper proposes a new hierarchically tunable six-dimensional movable antenna (HT-6DMA) architecture for base station (BS) in future wireless networks, aiming to improve the performance of both wireless communication and sensing. The HT-6DMA BS consists of multiple antenna arrays that can flexibly move on a spherical surface, with their three-dimensional (3D) positions and 3D rotations/orientations efficiently characterized in the global spherical coordinate system (SCS) and their individual local SCSs, respectively. As a result, the 6DMA system is hierarchically tunable in the sense that each array’s global position and local rotation can be separately adjusted in a sequential manner with the other being fixed, thus greatly reducing their design complexity and improving the achievable performance. In particular, we consider an HT-6DMA BS serving multiple single-antenna users in the uplink communication or sensing potential unmanned aerial vehicles (UAVs)/drones in a given airway area. Specifically, for the communication scenario, we aim to maximize the average sum rate of communication users in the long term by optimizing the positions and rotations of all 6DMA arrays at the BS. For the airway sensing scenario, we maximize the minimum received sensing signal power along the airway by optimizing the 6DMA arrays’ positions and rotations along with the BS’s transmit covariance matrix. Despite that the formulated problems are both non-convex and challenging to solve, we propose efficient solutions to them by exploiting the hierarchical tunability of positions/rotations of 6DMA arrays in our proposed model. Numerical results show that the proposed HT-6DMA design significantly outperforms not only the traditional BS with fixed-position antennas (FPAs), but also the existing 6DMA scheme based on alternating array position/rotation optimization. Furthermore, it is unveiled that the performance gains of HT-6DMA mostly come from the arrays’ global position adjustments on the spherical surface, rather than their local rotation adjustments, which provides a useful guide for implementing 6DMA systems under practical performance-complexity trade-off consideration.

Intelligent omni-surfaces (IOSs) with 360° electromagnetic radiation significantly improves the performance of wireless systems, while an adversarial IOS also poses a significant potential risk for physical layer security. In this paper, we propose a “DISCO” IOS (DIOS) based fully-passive jammer (FPJ) that can launch omnidirectional fully-passive jamming attacks. In the proposed DIOS-based FPJ, the interrelated refractive and reflective (R&R) coefficients of the adversarial IOS are randomly generated, acting like a “DISCO ball” that distributes wireless energy radiated by the base station. By introducing active channel aging (ACA) during channel coherence time, the DIOS-based FPJ can perform omnidirectional fully-passive jamming without neither jamming power nor channel knowledge of legitimate users (LUs). To characterize the impact of the DIOS-based PFJ, we derive the statistical characteristics of DIOS-jammed channels based on two widely-used IOS models, i.e., the constant-amplitude model and the variable-amplitude model. Consequently, the asymptotic analysis of the ergodic achievable sum rates under the DIOS-based omnidirectional fully-passive jamming is given based on the derived stochastic characteristics for both the two IOS models. Based on the derived analysis, the omnidirectional jamming impact of the proposed DIOS-based FPJ implemented by a constant-amplitude IOS does not depend on either the quantization number or the stochastic distribution of the DIOS coefficients, while the conclusion does not hold on when a variable-amplitude IOS is used. Numerical results1based on one-bit quantization of the IOS phase shifts are provided to verify the effectiveness of the derived theoretical analysis. The proposed DIOS-based FPJ can not only launch omnidirectional fully-passive jamming, but also improve the jamming impact by about 55% at 10 dBm transmit power per LU.

Movable antennas (MAs) have received increasing attention in wireless communications due to their capability of position adjustment to reconfigure wireless channels. However, moving MAs results in non-negligible delay, which may decrease the effective data transmission time. To reduce the movement delay, this paper investigates a new MA trajectory optimization problem. In particular, given the desired destination positions of multiple MAs, we aim to jointly optimize their associations with the initial MA positions and the corresponding movement trajectories within a two-dimensional (2D) region. The goal is to minimize the overall movement delay for all MAs subject to inter-MA minimum distance constraints and practical motor-induced moving direction constraints. However, this problem is a continuous-time mixed-integer linear programming (MILP) problem that is challenging to solve. To tackle this challenge, we first consider a special case with a one-dimensional (1D) MA array and derive the optimal trajectories for MAs in closed-form. Then, we consider another special case without the moving direction constraints and propose a two-stage optimization algorithm that sequentially optimizes the MAs’ position associations and trajectories. This algorithm first relaxes the inter-MA distance constraints and optimally solves the resulting delay minimization problem, followed by successive convex approximation (SCA) to adjust the obtained MA association and trajectory solutions. Furthermore, we extend this two-stage algorithm to the general scenario with the moving direction constraints by introducing the Manhattan distance and combining the A* and conflict-based search (CBS) algorithms. Simulation results are provided to show the effectiveness of our proposed trajectory optimization methods in reducing the movement delay as well as draw useful insights for practical design.

Intelligent reflecting surface (IRS) is composed of numerous passive reflecting elements and can be mounted on unmanned aerial vehicles (UAVs) to achieve six-dimensional (6D) movement by adjusting the UAV’s three-dimensional (3D) location and 3D orientation simultaneously. Hence, in this paper, we investigate a new UAV-enabled passive 6D movable antenna (6DMA) architecture by mounting an IRS on a UAV and address the associated joint deployment and beamforming optimization problem. In particular, we consider a passive 6DMA-aided multicast system with a multi-antenna base station (BS) and multiple remote users, aiming to jointly optimize the IRS’s location and 3D orientation, as well as its passive beamforming to maximize the minimum received signal-to-noise ratio (SNR) among all users under the practical angle-dependent signal reflection model. However, this optimization problem is challenging to be optimally solved due to the intricate relationship between the users’ SNRs and the IRS’s location and orientation. To tackle this challenge, we first focus on a simplified case with a single user, showing that one-dimensional (1D) orientation suffices to achieve the optimal performance. Next, we show that for any given IRS’s location, the optimal 1D orientation can be derived in closed form, based on which several useful insights are drawn. To solve the max-min SNR problem in the general multi-user case, we propose an alternating optimization (AO) algorithm by alternately optimizing the IRS’s beamforming and location/orientation via successive convex approximation (SCA) and hybrid coarse- and fine-grained search, respectively. To avoid undesirable local sub-optimal solutions, a Gibbs sampling (GS) method is proposed to generate new IRS locations and orientations for exploration in each AO iteration. Numerical results validate our theoretical analyses and demonstrate the superiority of our proposed AO algorithm with GS to conventional AO and other baseline deployment strategies with location or orientation optimization only.

Movable antenna (MA) has attracted increasing attention in wireless communications recently. As compared to conventional fixed-position antennas (FPAs), the geometry of MAs can be dynamically reconfigured, such that more flexible beamforming can be achieved for different purposes. In this paper, we investigate the application of MAs to wide-beam coverage, aiming to jointly optimize the MAs’ beamforming weights and positions within a line segment to maximize the minimum beam gain among all possible directions in a target region. However, the resulting optimization problem is non-convex and difficult to be optimally solved. To tackle this difficulty, we first derive a closed-form optimal solution to this problem in the special case with two MAs. While for the case with more than two MAs, an alternating optimization (AO) algorithm is proposed to obtain a high-quality suboptimal solution, where the MAs’ beamforming weights and positions are alternately optimized by applying the successive convex approximation (SCA) technique. To reduce computational complexity, we further propose a more efficient MA position optimization method by leveraging the frequency modulation continuous wave (FMCW) design. Specifically, we construct a spatial FMCW-based continuous phase profile for the entire line segment and then select an optimal set of MA positions to optimize the wide-beam coverage performance with their FMCW-based phase profiles, thus greatly simplifying the wide-beam design. Furthermore, we extend the proposed AO and FMCW-based algorithms for the linear MA array to the planar MA array. Numerical results show that both our proposed algorithms can significantly outperform conventional FPAs even with optimized beamforming weights.

In this paper, we model the minimum achievable throughput within a transmission block of restricted duration and aim to maximize it in movable antenna (MA)-enabled multiuser downlink communications. Particularly, we account for the antenna movement delay caused by mechanical movement, which has not been fully considered in previous studies, and reveal the trade-off between the delay and signal-to-interference-plus-noise ratio at users. To this end, we first consider a single-user setup to analyze the necessity of antenna movement. By quantizing the virtual angles of arrival, we derive the requisite region size for antenna moving, design the initial MA position, and elucidate the relationship between quantization resolution and moving region size. Furthermore, an efficient algorithm is developed to optimize MA position via successive convex approximation, which is subsequently extended to the general multiuser setup. Numerical results demonstrate that the proposed algorithms outperform fixed-position antenna schemes and existing ones without consideration of movement delay. Additionally, our algorithms exhibit excellent adaptability and stability across various transmission block durations and moving region sizes, and are robust to different antenna moving speeds. This allows the hardware cost of MA-aided systems to be reduced by employing low rotational speed motors.

Movable antennas (MAs) offer additional spatial degrees of freedom (DoFs) to enhance wireless communication performance through local antenna movement in a confined region. However, to achieve accurate and fast antenna movement, MA drivers entail non-negligible mechanical power consumption, rendering energy efficiency (EE) optimization more critical compared to conventional fixed-position antenna (FPA) systems. To address this problem, we develop in this paper a fundamental power consumption model for stepper motor-driven multi-MA systems by resorting to basic electric motor theory. Based on this model, we investigate an EE maximization problem for the downlink transmission from a multi-MA base station (BS) to multiple single-antenna users. In particular, we aim to jointly optimize the MAs’ positions and moving speeds as well as the BS’s transmit precoding matrix subject to collision-avoidance constraints during the multi-MA movements. However, this problem appears to be difficult to be solved optimally. To tackle this challenge, we first reveal that the collision-avoidance constraints can always be relaxed without loss of optimality by properly renumbering the MA indices. For the resulting relaxed problem, we first consider a simplified single-user setup and uncover a hidden monotonicity of the EE performance with respect to the MAs’ moving speeds. To solve the remaining optimization problem, we develop a two-layer optimization framework. In the inner layer, the Dinkelbach algorithm is employed to derive the optimal beamforming solution in a semi-closed form for any given MA positions. In the outer layer, a sequential update algorithm is proposed to iteratively refine the MA positions based on the optimal values obtained from the inner layer. Next, we proceed to the general multi-user case and propose an alternating optimization (AO) algorithm to obtain a high-quality suboptimal solution. Numerical results demonstrate that despite the additional mechanical power consumption, the proposed algorithms can outperform both conventional FPA systems and existing EE maximization algorithms that neglect mechanical power consumption.

Typical reconfigurable intelligent surface (RIS) implementations include metasurfaces with almost passive unit elements capable of reflecting their incident waves in controllable ways, enhancing wireless communications in a cost-effective manner. In this paper, we advance the concept of intelligent metasurfaces by introducing a flexible array geometry, termed flexible intelligent metasurface (FIM), which supports both element movement (EM) and passive beamforming (PBF). In particular, based on the single-input single-output (SISO) system setup, we first compare three modes of FIM, namely, EM-only, PBF-only, and EM-PBF, in terms of received signal power under different FIM and channel setups. The PBF-only mode, which only adjusts the reflecting phase, shows less effective than the EM-only mode in enhancing received signal strength. The EM-PBF mode, which optimizes both element positions and phases, further enhances performance. Additionally, we investigate the channel estimation problem for FIM systems by designing a protocol that gathers EM and PBF measurements, enabling the formulation of a compressive sensing problem for joint cascaded and direct channel estimation. We then propose a sparse recovery algorithm called clustering mean-field variational sparse Bayesian learning, which enhances estimation performance while maintaining low complexity.

Movable antennas (MAs) have emerged as a promising technology to achieve high data rates in wireless communications by dynamically adjusting their positions to mitigate deep fading within a given region. However, to determine the optimal MA positions, full channel state information (CSI) is required for each position within the transmit/receive movement region, which leads to extremely high channel estimation overhead. To tackle this challenge, this paper proposes a new learning-based approach to predict the optimal positions of multiple transmit MAs in a multiple-input single-output (MISO) system without explicit CSI estimation. Specifically, we show that there exists a clear mapping between the optimal MA positions and the channel power gains from a subset of locations within the transmit region to the receiver. To acquire and leverage this mapping, we train a deep neural network (DNN) via offline supervised learning and then use the pre-trained DNN to determine the optimized MA positions in real-time data transmission, based on partial power measurements within the transmit region only. Numerical results demonstrate that the proposed DNN-based method achieves near-optimal performance and significantly outperforms conventional fixed-position antenna (FPA) systems.

Active intelligent reflecting surface (AIRS) is expected to further improve wireless communication performance compared to traditional passive IRS (PIRS) due to its additional signal amplification capability. To leverage the benefits of AIRSs at the network level, we investigate a new AIRS-user association problem in a general wireless network consisting of multiple base stations (BSs), users and distributed IRSs. Specifically, each AIRS assists in the communication from its associated BS to user, while randomly scattering and amplifying interference and noise to all users. We first derive the average signal-to-interference-plus-noise ratio (ASINR) achievable at each user for any given AIRS-user associations. We then analytically compare the ASINRs of multi-AIRS and multi-PIRS networks in terms of different metrics, gaining key insights into the advantages of AIRSs over PIRSs at the network level. Furthermore, we jointly optimize AIRS-user associations to maximize the minimum ASINR among all users and propose an efficient successive refinement algorithm to obtain a high-quality suboptimal solution. Numerical results validate our performance analysis and demonstrate the superiority of our proposed algorithm over other baseline schemes.

Terahertz (THz) communication systems suffer severe blockage issues, which may significantly degrade the communication coverage and quality. Bending beams, capable of adjusting their propagation direction to bypass obstacles, have recently emerged as a promising solution to resolve this issue by engineering the propagation trajectory of the beam. However, traditional bending beam generation methods rely heavily on the specific geometric properties of the propagation trajectory and can only achieve sub-optimal performance. In this paper, we propose a new and general bending beamforming method by adopting the convex optimization techniques. In particular, we formulate the bending beamforming design as a max-min optimization problem, aiming to optimize the analog or digital transmit beamforming vector to maximize the minimum received signal power among all positions along the bending beam trajectory. However, the resulting problem is non-convex and difficult to be solved optimally. To tackle this difficulty, we apply the successive convex approximation (SCA) technique to obtain a high-quality suboptimal solution. Numerical results show that our proposed bending beamforming method outperforms the traditional method and shows robustness to the obstacle in the environment.

Target-mounted intelligent reflecting surfaces (IRS) introduce a novel degree of freedom (DoF) in controlling the target’s radar cross section (RCS), thereby enabling numerous advanced applications in wireless sensing and integrated sensing and communication (ISAC) systems. Nevertheless, a comprehensive analytical framework characterizing the impact of IRS reflection coefficients on wireless sensing performance remains largely unexplored in existing literature. To address this gap, this paper investigates sensing mutual information (SMI) in a general scenario where a sensing transmitter (TX) sends random signals to multiple targets each equipped with an IRS, and multiple sensing receivers (RXs) process the received echoes. We derive a closed-form tight upper bound on SMI and propose an efficient manifold optimization-based method to maximize it by jointly optimizing the transmit precoder and IRS reflection coefficients. Simulation results validate our analysis and demonstrate substantial enhancements in SMI achieved by the proposed method.

The coming industrial internet of things (IIoT) era is anticipated to see the proliferations of goal-oriented applications in real-time wireless control systems. In such systems, a low processing latency is required to guarantee the timely transmission of control information. To achieve this goal, this paper proposes a new integrated sensing, computing, communication, and control$(\text{ISC}^{3})$architecture, where semantic communications are adopted to make sensible semantic inference (SI) for the control information over time. In particular, we introduce a new criterion, i.e., mutual information (MI), for control performance evaluation by drawing from the field of wireless communications. We calculate the MI by designing a semantic feature extractor (SFE) module at the transmitter (Tx) to identify the semantic correlation among its sensed control information over time, thereby adjusting the frequency of its control information transmission. Meanwhile, a semantic feature reconstructor (SFR) module is employed at the receiver (Rx) to predict the current control information based on its previously received information if there is no control information transmission from the Tx. Finally, on-site experimental results are provided, showing that our proposed scheme can significantly reduce the communication overhead while improving the control performance significantly.

The passive and frequency-flat reflection of IRS, as well as the high-dimensional IRS-reflected channels, have posed significant challenges for efficient IRS channel estimation, especially in wideband communication systems with significant multi-path channel delay spread. To address these challenges, we propose a novel neural network (NN)-empowered framework for IRS channel autocorrelation matrix estimation in wideband orthogonal frequency division multiplexing (OFDM) systems. This framework relies only on the easily accessible reference signal received power (RSRP) measurements at users in existing wideband communication systems, without requiring additional pilot transmission. Based on the estimates of channel autocorrelation matrix, the passive reflection of IRS is optimized to maximize the average user received signal-to-noise ratio (SNR) over all subcarriers in the OFDM system. Numerical results verify that the proposed algorithm significantly outperforms existing power-measurement-based IRS reflection designs in wideband channels.

Intelligent reflecting surface (IRS) and movable antenna (MA)/fluid antenna (FA) techniques have both received increasing attention in the realm of wireless communications due to their ability to reconfigure and improve wireless channel conditions. In this paper, we investigate the integration of MAs/FAs into an IRS-assisted wireless communication system. In particular, we consider the downlink transmission from a multi-MA base station (BS) to a single-antenna user with the aid of an IRS, aiming to maximize the user's received signal-to-noise ratio (SNR), by jointly optimizing the BS/IRS active/passive beamforming and the MAs' positions. Due to the similar capability of MAs and IRS for channel reconfiguration, we first conduct theoretical analyses of the performance gain of MAs over conventional fixed-position antennas (FPAs) under the line-of-sight (LoS) BS-IRS channel and derive the conditions under which the performance gain becomes more or less significant. Next, to solve the received SNR maximization problem, we propose an alternating optimization (AO) algorithm that decomposes it into two subproblems and solve them alternately. Numerical results are provided to validate our analytical results and evaluate the performance gains of MAs over FPAs under different setups.

By providing cost-efficient flexibility beyond wired control systems, real-time wireless control systems (WCSs) are pivotal in industrial internet of things (IIoT), which can enable massive emerging applications in IIoT, e.g., autonomous driving, remote medical, teleoperation, etc. To guarantee good control performance over wireless networks, ultra-reliable and low-latency communications (URLLCs) are required from communication perspective. However, this would result in frequent high-rate data transmission, leading to extremely high resource consumption or even network congestion, which impedes the application of WCSs in IIoT. This paper proposes a novel co-design method for communication and control in WCSs, which offers a new strategy to address the challenges introduced by URLLC. Specifically, we first formulate an optimization problem aiming to minimize the communication and control cost by jointly optimizing transmission trigger, communication power, and Lyapunov cost, which, however, is NP-hard. To address this challenge, we employ a two-stage strategy by first defining a new metric, i.e, state-to-error ratio (SER), as a trigger condition to evaluate control performance. Based on this metric, we analyze the relationship between SER and signal-to-noise-ratio (SNR) and show their hidden consistency in evaluating both communication and control performance, thus facilitating our communication and control co-design. Subsequently, we establish a closed-form relationship between the control convergence rate and communication reliability and thereby obtain the optimal transmit power to ensure the overall system performance. Finally, simulation results are provided to demonstrate the efficacy of our proposed method.

Optical intelligent reflecting surface (OIRS) has attracted increasing attention due to its capability of overcoming signal blockages in visible light communication (VLC), an emerging technology for the next-generation advanced transceivers. However, current works on OIRS predominantly assume known channel state information (CSI), while its estimation problem has not been studied yet. To bridge such a gap, this paper proposes a new and customized OIRS channel estimation protocol with joint space-time sampling under the alignment-based OIRS channel model. First, we unveil the spatial and temporal coherence characteristics and derive OIRS coherence distance and coherence time in closed form. Next, to achieve dynamic beam alignment for pilot transmission within the coherence time, we propose to tune the rotation angles of the OIRS reflecting elements following a geometric optics-based non-uniform codebook. Then, given the beam alignment within the considered coherence time, a sequential OIRS channel estimation method is proposed, where the OIRS is divided into multiple subarrays based on the coherence distance. The CSI for each subarray is estimated sequentially, followed by a space-time interpolation to retrieve full CSI for other non-aligned transceiver antennas. Numerical results validate our theoretical analyses and demonstrate the efficacy of the proposed OIRS channel estimation protocol as compared to benchmark schemes.

Movable antenna (MA) has been deemed as a promising technology to flexibly reconfigure wireless channels by adjusting the antenna positions in a given local region. In this paper, we investigate the application of the MA technology in both decode-and-forward (DF) and amplify-and-forward (AF) relaying systems, where a relay is equipped with multiple MAs to assist in the data transmission between two single-antenna nodes. For the DF relaying system, our objective is to maximize the achievable rate at the destination by jointly optimizing the positions of the MAs in two stages for receiving signals from the source and transmitting signals to the destination, respectively. To drive essential insights, we first derive a closed-form upper bound on the maximum achievable rate of the DF relaying system. Then, a low-complexity algorithm based on projected gradient ascent (PGA) and alternating optimization (AO) is proposed to solve the antenna position optimization problem. For the AF relaying system, our objective is to maximize the achievable rate by jointly optimizing the two-stage MA positions as well as the AF beamforming matrix at the relay, which results in a more challenging optimization problem due to the intricate coupling variables. To tackle this challenge, we first reveal the hidden separability among the antenna position optimization in the two stages and the beamforming optimization. Based on such separability, we derive a closed-form upper bound on the maximum achievable rate of the AF relaying system and propose a low-complexity algorithm to obtain a high-quality suboptimal solution to the considered problem. Simulation results validate the efficacy of our theoretical analysis and demonstrate the superiority of the MA-enhanced relaying systems to the conventional relaying systems with fixed-position antennas (FPAs) and other benchmark schemes.

Movable antenna (MA) and intelligent reflecting surface (IRS) are considered promising technologies for the next-generation wireless communication systems due to their shared capabilities of reconfiguring and improving wireless channel conditions. This, however, raises a fundamental question: Does the performance gain of MAs over conventional fixed-position antennas (FPAs) still exist in the presence of the IRS passive beamforming? To answer this question, we investigate in this paper an IRS-assisted multi-user multiple-input single-output (MISO) MA system, where a multi-MA base station (BS) transmits to multiple single-FPA users. We formulate a sum-rate maximization problem by jointly optimizing the active/passive beamforming of the BS/IRS and the MA positions within a one-dimensional transmit region, which is challenging to be optimally solved. To drive essential insights, we first study a simplified case with a single user. Then, we analyze the performance gain of MAs over FPAs in the light-of-sight (LoS) BS-IRS channel and derive the conditions under which this gain becomes more or less significant. In addition, we propose an alternating optimization (AO) algorithm to solve the signal-to-noise ratio (SNR) maximization problem in the single-user case by combining the block coordinate descent (BCD) method and the graph-based method. For the general multi-user case, our performance analysis unveils that the performance gain of MAs over FPAs diminishes with typical transmit precoding strategies at the BS under certain conditions. We also propose a high-quality suboptimal solution to the sum-rate maximization problem by applying the AO algorithm that combines the weighted minimum mean square error (WMMSE) algorithm, manifold optimization method and discrete sampling method. Numerical results validate our theoretical analyses and demonstrate that the performance gain of MAs over FPAs may be reduced if the IRS passive beamforming is optimized.

Integrated sensing and communication (ISAC) systems may face a heavy computation burden since the sensory data needs to be further processed. This paper studies a novel system that integrates sensing, communication, and computation, aiming to provide services for different objectives efficiently. This system consists of a multi-antenna multi-functional base station (BS), an edge server, a target, and multiple single-antenna communication users. The BS needs to allocate the available resources to efficiently provide sensing, communication, and computation services. Due to the heavy service burden and limited power budget, the BS can partially offload the tasks to the nearby edge server instead of computing them locally. We consider the estimation of the target response matrix, a general problem in radar sensing, and utilize Cramér-Rao bound (CRB) as the corresponding performance metric. To tackle the non-convex optimization problem, we propose both semidefinite relaxation (SDR)-based alternating optimization and SDR-based successive convex approximation (SCA) algorithms to minimize the CRB of radar sensing while meeting the requirement of communication users and the need for task computing. Furthermore, we demonstrate that the optimal rank-one solutions of both the alternating and SCA algorithms can be directly obtained via the solver or further constructed even when dealing with multiple functionalities. Simulation results show that the proposed algorithms can provide higher target estimation performance than state-of-the-art benchmarks while satisfying the communication and computation constraints.

Channel state information (CSI) is essential to the performance optimization of intelligent reflecting surface (IRS)-aided wireless communication systems. However, the passive and frequency-flat reflection of IRS, as well as the high-dimensional IRS-reflected channels, have posed practical challenges for efficient IRS channel estimation, especially in wideband communication systems with significant multi-path channel delay spread. To tackle the above challenge, we propose a novel neural network (NN)-empowered IRS channel estimation and passive reflection design framework for the wideband orthogonal frequency division multiplexing (OFDM) communication system based only on the user’s reference signal received power (RSRP) measurements with time-varying random IRS training reflections. As RSRP is readily accessible in existing communication systems, our proposed channel estimation method does not require additional pilot transmission in IRS-aided wideband communication systems. In particular, we show that the average received signal power over all OFDM subcarriers at the user terminal can be represented as the prediction of a single-layer NN composed of multiple subnetworks with the same structure, such that the autocorrelation matrix of the wideband IRS channel can be recovered as their weights via supervised learning. To exploit the potential sparsity of the channel autocorrelation matrix, a progressive training method is proposed by gradually increasing the number of subnetworks until a desired accuracy is achieved, thus reducing the training complexity. Based on the estimates of IRS channel autocorrelation matrix, the IRS passive reflection is then optimized to maximize the average channel power gain over all subcarriers. Numerical results indicate the effectiveness of the proposed IRS channel autocorrelation matrix estimation and passive reflection design under wideband channels, which can achieve significant performance improvement compared to the existing IRS reflection designs based on user power measurements.

Optical intelligent reflecting surface (OIRS) has been considered a promising technology for visible light communication (VLC) by constructing visual line-of-sight propagation paths to address the signal blockage issue. However, the existing works on OIRSs are mostly based on perfect channel state information (CSI), whose acquisition appears to be challenging due to the passive nature of the OIRS. To tackle this challenge, this paper proposes a customized channel estimation algorithm for OIRSs. Specifically, we first unveil the OIRS spatial coherence characteristics and derive the coherence distance in closed form. Based on this property, a spatial sampling-based algorithm is proposed to estimate the OIRS-reflected channel, by dividing the OIRS into multiple subarrays based on the coherence distance and sequentially estimating their associated CSI, followed by an interpolation to retrieve the full CSI. Simulation results validate the derived OIRS spatial coherence and demonstrate the efficacy of the proposed OIRS channel estimation algorithm.

Reconfigurable intelligent surfaces can be densely deployed in the environment to create multi-reflection line-ofsight (LoS) links between base stations (BSs) and users, thereby significantly enhancing the BS coverage. However, conventional RISs can only achieve half-space reflection, which limits the LoS path diversity. In contrast, simultaneously transmitting and reflecting RISs (STAR-RISs) can split incident signals into reflected and transmitted signals pointing to different half spaces simultaneously, thereby creating more LoS paths. Hence, in this paper, we study a new multi-STAR-RIS-aided wireless communication system, where a multi-antenna base station (BS) transmits to a single-antenna user by exploiting the signal beam routing over a set of cascaded LoS paths each formed by multiple STAR-RISs. We aim to jointly optimize the active beamforming at the BS, the BS’s power allocation over different paths, the number of beam-routing paths, the selected STAR-RISs for each path, as well as their amplitude and phase shifts for transmission/reflection, such that the received signal power at the user is maximized. However, this problem is particularly difficult to be optimally solved as different paths may be intricately coupled at their shared STAR-RISs. To tackle this difficulty, we first derive the optimal solutions to this problem in closed-form for a given set of paths. The clique-based approach in graph theory is then applied to solve the remaining multi-path selection problem efficiently. Simulation results show that our proposed STAR-RIS-enabled beam routing can achieve much better performance than the conventional beam routing with reflection-only RISs.

Intelligent reflecting surface (IRS) can be mounted on an unmanned aerial vehicle (UAV) to enhance the coverage performance of base stations (BSs) by leveraging the UAV’s flexible and controllable deployment. However, the existing works on UAV-mounted IRSs have mainly focused on their location optimization, which may not unleash their full potential in performance enhancement in light of the UAV’s capability of three-dimensional (3D) posture control. Hence, in this paper, we consider the UAV-mounted IRS-assisted wireless communication from a BS to a remote user, aiming to jointly optimize its location and 3D orientation to maximize the user’s received signal-to-noise ratio (SNR) under the practical angle-dependent signal reflection model. However, this optimization problem is challenging to be optimally solved due to the intricate relationship between the user’s SNR and the IRS’s location and orientation. To tackle this challenge, we first prove that one-dimensional (1D) orientation suffices to achieve the optimal performance, thereby significantly simplifying the optimization problem. Next, we show that for any given IRS’s location, the optimal 1D orientation can be derived in closed form, based on which several useful insights are drawn. Furthermore, in some special cases regarding the UAV/IRS’s altitude and the BS-user distance, we also derive the UAV’s optimal location in closed form. Numerical results validate our theoretical analyses and demonstrate the superiority of the joint location and orientation optimization for the UAV-mounted IRS to its location/orientation optimization only.

Fluid antennas (FAs) and mobile antennas (MAs) are innovative technologies in wireless communications that are able to proactively improve channel conditions by dynamically adjusting the transmit/receive antenna positions within a given spatial region. In this paper, we investigate an MA-enhanced multiple-input single-output (MISO) secure communication system, aiming to maximize the secrecy rate by jointly optimizing the positions of multiple MAs. Instead of continuously searching for the optimal MA positions as in prior works, we propose to discretize the transmit region into multiple sampling points, thereby converting the continuous antenna position optimization into a discrete sampling point selection problem. However, this point selection problem is combinatory and thus difficult to be optimally solved. To tackle this challenge, we ingeniously transform this combinatory problem into a recursive path selection problem in graph theory and propose a partial enumeration algorithm to obtain its optimal solution without the need for high-complexity exhaustive search. To further reduce the complexity, a linear-time sequential update algorithm is also proposed to obtain a high-quality suboptimal solution. Numerical results show that our proposed algorithms yield much higher secrecy rates as compared to the conventional FPA and other baseline schemes.

Fluid antennas (FAs) and movable antennas (MAs) have attracted increasing attention in wireless communications recently. As compared to the conventional fixed-position antennas (FPAs), their geometry can be dynamically reconfigured, such that more flexible beamforming can be achieved for signal coverage and/or interference nulling. In this paper, we investigate the use of MAs to achieve uniform coverage for multiple regions with arbitrary number and width in the spatial domain. In particular, we aim to jointly optimize the MAs’ weights and positions within a linear array to maximize the minimum beam gain over the desired spatial regions. However, the resulting problem is non-convex and difficult to be optimally solved. To tackle this difficulty, we propose an alternating optimization (AO) algorithm to obtain a high-quality suboptimal solution, where the MAs’ weights and positions are alternately optimized by applying successive convex approximation (SCA) technique. Numerical results show that our proposed MA-based beam coverage scheme can achieve much better performance than conventional FPAs.

Intelligent reflecting surface (IRS) is deemed as a promising technology to improve the spectral and energy efficiency of wireless communications cost-effectively. In this paper, we investi-gate a double-IRS aided wideband terahertz (THz) communication system and the beam-squint effects at the two IRSs over their double-reflection line-of-sight (LoS) link. To gain useful insights into such beam-squint effects, we first analyze the performance loss incurred by applying the conventional narrowband cooperative passive beamforming (CPB) at the two IRSs in the considered wideband system, which unveils that signal nulling may frequently occur over frequency in the case of large-size IRSs. To resolve this issue, we propose in this paper a new max-min CPB design, aiming to maximize the minimum end-to-end channel power gain over the frequency band. However, this problem is non-convex and difficult to be optimally solved. To tackle this difficulty, we propose to combine the alternating optimization (AO) and alternating direction method of multipliers (ADMM) algorithms to obtain a high-quality suboptimal solution. Numerical results show that the proposed max-min CPB design can achieve much better performance than the conventional narrowband CPB design.

Aerial-ground interference has been deemed as the main challenge to realize the cellular-connected unmanned aerial vehicle (UAV) communications. Due to the strong line-of-sight (LoS)-dominant aerial-ground channels, UAVs could cause/suffer severe interference to/from a large number of co-channel base stations (BSs) in their uplink/downlink communications. In this paper, we propose a new cooperative interference cancellation (CIC) scheme with hybrid linear and nonlinear processing for the UAV's uplink communication to mitigate its strong interference to co-channel BSs, by leveraging the local cooperation between each co-channel BS and its adjacent BSs. In particular, the helping BSs quantize and forward their received signal from the UAV to the co-channel BS, which can combine these quantized signals with its own received signal to decode its served terrestrial user's message via either linear or nonlinear interference cancellation, which achieves the best rate performance of the UAV and terrestrial users under different conditions. To exploit their complementary benefits, we aim to select an optimal subset of co-channel BSs to perform the linear/nonlinear interference cancellation and optimize the UAV's transmit power over its assigned resource blocks (RBs) to maximize the weighted sum rate of the UAV and all co-channel BSs. As this problem is non-convex and difficult to be optimally solved, we propose an alternating optimization (AO) algorithm to obtain a high-quality suboptimal solution. Numerical results show that our proposed CIC scheme achieves better performance than other baseline CIC schemes.

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.

Both passive and active intelligent reflecting surfaces (IRSs) can be deployed in complex environments to enhance wireless network coverage by creating multiple blockage-free cascaded line-of-sight (LoS) links. In this paper, we study a multi-passive/active-IRS (PIRS/AIRS) aided wireless network with a multi-antenna base station (BS) in a given region. First, we divide the region into multiple non-overlapping cells, each of which may contain one candidate location that can be deployed with a single PIRS or AIRS. Then, we show several trade-offs between minimizing the total IRS deployment cost and enhancing the signal-to-noise ratio (SNR) performance over all cells via direct/cascaded LoS transmission with the BS. To reconcile these trade-offs, we formulate a joint multi-PIRS/AIRS deployment problem to select an optimal subset of all candidate locations for deploying IRS and also optimize the number of passive/active reflecting elements deployed at each selected location to satisfy a given SNR target over all cells, such that the total deployment cost is minimized. However, due to the combinatorial optimization involved, the formulated problem is difficult to be solved optimally. To tackle this difficulty, we first optimize the reflecting element numbers with given PIRS/AIRS deployed locations via sequential refinement, followed by a partial enumeration to determine the PIRS/AIRS locations. Simulation results show that our proposed algorithm achieves better cost-performance trade-offs than other baseline deployment strategies.

Intelligent reflecting surface (IRS) can be densely deployed in complex environments to create cascaded line-of-sight (LoS) links between base stations (BSs) and users, which significantly enhance the signal coverage for both wireless information transfer and wireless power transfer (WPT). In this paper, we consider the WPT from a multi-antenna BS to multiple energy users (EUs) by exploiting the signal beam routing via multi-IRS reflections. First, we present a baseline beam routing scheme with each IRS serving at most one EU, where the BS transmits wireless power to all EUs simultaneously while the signals to different EUs undergo disjoint sets of multi-IRS reflection paths. Under this setup, we aim to tackle the joint beam routing and resource allocation optimization problem by jointly optimizing the reflection paths for all EUs, the active/passive beamforming at the BS/each involved IRS, as well as the BS’s power allocation for different EUs to maximize the minimum received signal power among all EUs. Next, to further improve the WPT performance, we propose two new beam routing schemes, namely dynamic beam routing and subsurface-based beam routing, where each IRS can serve multiple EUs via different time slots and different subsurfaces, respectively. In particular, we prove that dynamic beam routing outperforms subsurface-based beam routing in terms of minimum harvested power among all EUs. In addition, we show that the optimal performance of dynamic beam routing is achieved by assigning all EUs with orthogonal time slots for WPT. A clique-based optimization approach is also proposed to solve the joint beam routing and resource allocation problems for the baseline beam routing and proposed dynamic beam routing schemes. Numerical results are finally presented, which demonstrate the superior performance of the proposed dynamic beam routing scheme to the baseline scheme.

The codebook-based analog beamforming is appealing for future terahertz (THz) communications since it can generate high-gain directional beams with low-cost phase shifters via low-complexity beam training. However, conventional beamforming codebook design based on array response vectors for narrowband communications may suffer from severe performance loss in wideband systems due to the “beam squint” effect over frequency. To tackle this issue, we propose in this paper a new codebook design method for analog beamforming in wideband THz systems. In particular, to characterize the analog beamforming performance in wideband systems, we propose a new metric termed wideband beam gain, which is given by the minimum beamforming gain over the entire frequency band given a target angle. Based on this metric, a wideband analog beamforming codebook design problem is formulated for optimally balancing the beamforming gains in both the spatial and frequency domains, and the performance loss of conventional narrowband beamforming in wideband systems is analyzed. To solve the new wideband beamforming codebook design problem, we divide the spatial domain into orthogonal angular zones each associated with one beam, thereby decoupling the codebook design into a zone division sub-problem and a set of beamforming optimization sub-problems each for one zone. For the zone division sub-problem, we propose a bisection method to obtain the optimal boundaries for separating adjacent zones. While for each of the per-zone-based beamforming optimization sub-problems, we further propose an efficient augmented Lagrange method (ALM) to solve it. Numerical results demonstrate the performance superiority of our proposed codebook design for wideband analog beamforming to the narrowband beamforming codebook and also validate our performance analysis.

Although the multi-antenna or so-called multiple-input multiple-output (MIMO) transmission is an enabling technology for past generations of wireless communication systems, its application to visible light communication (VLC) still faces a critical challenge due to the strong spatial correlation of VLC channels, which makes it difficult to achieve sufficient spatial multiplexing gain. This paper proposes to use optical intelligent reflecting surfaces (OIRS) to tackle this challenge. Firstly, we characterize the extremely near-field channel condition in the optical frequency range and reveal a peculiar “inter-element interference (IEI) free” property of the OIRS-reflected channel, where the OIRS reflecting elements can be individually configured to align with one pair of transmitter and receiver antennas without causing interference to each other. Next, we characterize the OIRS-assisted MIMO VLC capacities under different power constraints at the transmitter antennas, and then proceed to maximize them by jointly optimizing the OIRS element alignment and transmitter emission power. In particular, we propose two algorithms for the OIRS optimization, namely, location-aided interior-point algorithm and log-det-based alternating optimization algorithm, to balance the performance versus complexity trade-off; while the optimal transmitter emission power is derived in closed form. Numerical results are provided to validate the capacity improvement of OIRS-assisted MIMO VLC against the VLC without OIRS and demonstrate the superior performance of the proposed algorithms compared to baseline schemes.

Intelligent reflecting surface (IRS) has been recognized as a transformative technology for enabling smart and reconfigurable radio environment cost-effectively by leveraging its controllable passive reflection. In this paper, we study an IRS-assisted coverage enhancement problem for a given region, aiming to optimize the passive reflection of the IRS for improving the average communication performance in the region by accounting for both deterministic and random channels in the environment. To this end, we first derive the closed-form expression of the average received signal power in terms of the deterministic base station (BS)-IRS-user cascaded channels over all user locations, and propose an IRS-aided coverage enhancement framework to facilitate the estimation of such deterministic channels for IRS passive reflection design. Specifically, to avoid the exorbitant overhead of estimating the cascaded channels at all possible user locations, a location selection method is first proposed to select only a set of typical user locations for channel estimation by exploiting the channel spatial correlation in the region. To estimate the deterministic cascaded channels at the selected user locations, conventional IRS channel estimation methods require additional pilot signals, which not only results in high system training overhead but also may not be compatible with the existing communication protocols. To overcome this issue, we further propose a single-layer neural network (NN)-enabled IRS channel estimation method in this paper, based on only the average received signal power measurements at each selected location corresponding to different IRS random training reflections, which can be offline implemented in current wireless systems. Based on the estimated channels, the IRS passive reflection is then optimized to maximize the average received signal power over the selected locations. Numerical results demonstrate that our proposed scheme can significantly improve the coverage performance of the target region and outperform the existing power-measurement-based IRS reflection designs.

One main challenge for implementing intelligent reflecting surface (IRS) aided communications lies in the difficulty to obtain the channel knowledge for the base station (BS)-IRS-user cascaded links, which is needed to design high-performance IRS reflection in practice. Traditional methods for estimating IRS cascaded channels are usually based on the additional pilot signals received at the BS/users, which increase the system training overhead and also may not be compatible with the current communication protocols. To tackle this challenge, we propose in this paper a new single-layer neural network (NN)-enabled IRS channel estimation method based on only the knowledge of users' individual received signal power measurements corresponding to different IRS random training reflections, which are easily accessible in current wireless systems. To evaluate the effectiveness of the proposed channel estimation method, we design the IRS reflection for data transmission based on the estimated cascaded channels in an IRS-aided multiuser communication system. Numerical results show that the proposed IRS channel estimation and reflection design can significantly improve the minimum received signal-to-noise ratio (SNR) among all users, as compared to existing power measurement based designs.

Intelligent reflecting surface (IRS) can be densely deployed in complex environment to create cascaded line-of-sight (LoS) paths between the base station (BS) and multiple users via tunable single/multiple signal reflections, thereby significantly enhancing the BS's coverage performance. To achieve this goal, we present an optimization framework for multi-IRS deployment in this paper and study its efficient design. In particular, we assume that a set of candidate locations for deploying IRSs are given in an area of interest, and show that there exists a fundamental trade-off between maximizing the BS's coverage in the area and minimizing the total cost in multi-IRS deployment design. Specifically, the more IRSs deployed over those candidate locations, the smaller number of IRS reflections on average required for achieving a LoS link between the BS and any user location in the area, which helps reduce the cascaded path loss and thus enhance the communication performance. To optimally characterize this trade-off, we formulate the multi-IRS deployment problem based on graph theory and propose a new successive removal algorithm to efficiently solve this problem by iteratively removing IRSs from the candidate locations while satisfying a given communication performance constraint. Simulation results are provided to show the efficacy of the proposed design approach and algorithm for multi-IRS deployment.

Intelligent reflecting surface (IRS) has been widely recognized as an efficient technique to reconfigure the electro-magnetic environment in favor of wireless communication performance. In this paper, we propose a new application of IRS for device-free target sensing via joint location and orientation estimation. In particular, different from the existing works that use IRS as an additional anchor node for localization/sensing, we consider mounting IRS on the sensing target, thus estimating the IRS's location and orientation as that of the target by leveraging IRS's controllable signal reflection. To this end, we first propose a three-dimensional beam training method to acquire essential angle information between the IRS and the sensing transmitter as well as a set of distributed sensing receivers. Next, based on the estimated angle information, we formulate two optimization problems to estimate the location and orientation of the IRS/target, respectively, which are solved by invoking the Taylor-series expansion and manifold optimization. Simulation results show that the proposed method can achieve high estimation accuracy and draw useful insights into the performance of target-mounted IRS sensing systems.

Intelligent reflecting surface (IRS) has been widely recognized as an efficient technique to reconfigure the electromagnetic environment in favor of wireless communication performance. In this paper, we propose a new application of IRS for device-free target sensing via joint location and orientation estimation. In particular, different from the existing works that use IRS as an additional anchor node for localization/sensing, we consider mounting IRS on the sensing target, whereby estimating the IRS’s location and orientation as that of the target by leveraging IRS’s controllable signal reflection. To this end, we first propose a tensor-based method to acquire essential angle information between the IRS and the sensing transmitter as well as a set of distributed sensing receivers. Next, based on the estimated angle information, we formulate two optimization problems to estimate the location and orientation of the IRS/target, respectively, and obtain the locally optimal solutions to them by invoking two iterative algorithms, namely, gradient descent method and manifold optimization. In particular, we show that the orientation estimation problem admits a closed-form solution in a special case that usually holds in practice. Furthermore, theoretical analysis is conducted to draw essential insights into the proposed sensing system design and performance. Simulation results verify our theoretical analysis and demonstrate that the proposed methods can achieve high estimation accuracy which is close to the theoretical bound.

Intelligent reflecting surface (IRS) can be densely deployed in complex environment to create cascaded line-of-sight (LoS) paths between multiple base stations (BSs) and users via tunable IRS reflections, thereby significantly enhancing the coverage performance of wireless networks. To achieve this goal, it is vital to optimize the deployed locations of BSs and IRSs in the wireless network, which is investigated in this paper. Specifically, we divide the coverage area of the network into multiple non-overlapping cells and decide whether to deploy a BS/IRS in each cell given a total number of BSs/IRSs available. We show that to ensure the network coverage/communication performance, i.e., each cell has a direct/cascaded LoS path with at least one BS, as well as such LoS paths have the average number of IRS reflections less than a given threshold, there is a fundamental trade-off with the deployment cost or the number of BSs/IRSs needed. To optimally characterize this trade-off, we formulate a joint BS and IRS deployment problem based on graph theory, which, however, is difficult to be optimally solved due to the combinatorial optimization involved. To circumvent this difficulty, we first consider a simplified problem with given BS deployment and propose the optimal as well as an efficient suboptimal IRS deployment solution to it, by applying the branch-and-bound method and iteratively removing IRSs from the candidate locations, respectively. Next, an efficient sequential update algorithm is proposed for solving the joint BS and IRS deployment problem. Numerical results are provided to show the efficacy of the proposed design approach and optimization algorithms for the joint BS and IRS deployment. The trade-off between the network coverage performance and the number of deployed BSs/IRSs with different cost ratios is also unveiled.

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 surface (IRS) has emerged as a promising technique to enhance wireless communication performance cost-effectively. The existing literature has mainly considered IRS being deployed near user terminals to improve their performance. However, this approach may incur a high cost if IRSs need to be densely deployed in the network to cater to random user locations. To avoid such high deployment cost, in this paper we consider a new IRS aided wireless network architecture, where IRSs are deployed in the vicinity of each base station (BS) to assist in its communications with distributed users regardless of their locations. Besides significantly enhancing IRSs’ signal coverage, this scheme helps reduce the IRS-associated channel estimation overhead as compared to conventional user-side IRSs, by exploiting the nearly static BS-IRS channels over short distance. For this scheme, we propose a new two-stage transmission protocol to achieve IRS channel estimation and reflection optimization for uplink data transmission efficiently. In addition, we propose effective methods for solving the user-IRS association problem based on long-term/statistical channel knowledge and the selected user-IRS-BS cascaded channel estimation problem. Finally, all IRSs’ passive reflections are jointly optimized with the BS’s multi-antenna receive combining to maximize the minimum achievable rate among all users for data transmission. Numerical results show that the proposed co-site-IRS empowered BS scheme can achieve significant performance gains over the conventional BS without co-site IRS and existing schemes for IRS channel estimation and reflection optimization, thus enabling an appealing low-cost and high-performance BS design for future wireless networks.

Intelligent reflecting surface (IRS) is envisioned to play a significant role in future wireless communication systems as an effective means of reconfiguring the radio signal propagation environment. In this paper, we study a new multi-IRS aided massive multiple-input multiple-output (MIMO) system, where a multi-antenna BS transmits independent messages to a set of remote single-antenna users using orthogonal beams that are subsequently reflected by different groups of IRSs via their respective multi-hop passive beamforming over pairwise line-of-sight (LoS) links. We aim to select optimal IRSs and their beam routing path for each of the users, along with the active/passive beamforming at the BS/IRSs, such that the minimum received signal power among all users is maximized. This problem is particularly difficult to solve due to a new type of path separation constraints for avoiding the IRS-reflected signal induced interference among different users. To tackle this difficulty, we first derive the optimal BS/IRS active/passive beamforming solutions based on their practical codebooks given the reflection paths. Then we show that the resultant multi-beam multi-hop routing problem can be recast as an equivalent graph-optimization problem, which is however NP-complete. To solve this challenging problem, we propose an efficient recursive algorithm to partially enumerate the feasible routing solutions, which is able to effectively balance the performance-complexity trade-off. Numerical results demonstrate that the proposed algorithm achieves near-optimal performance with low complexity and outperforms other benchmark schemes. Useful insights into the optimal multi-beam multi-hop routing design are also drawn under different setups of the multi-IRS aided massive MIMO network.

Intelligent reflecting surface (IRS) is envisioned to play a significant role in future wireless communication systems thanks to its powerful capability of enabling smart and reconfigurable radio environment. In this paper, we study the multi-IRS aided downlink communication in a massive multiple-input multiple-output (MIMO) system, where a multi-antenna BS simultaneously serves multiple remote single-antenna users with orthogonal beams reflected by multiple IRSs. By exploiting the line-of-sight (LoS) link between each pair of selected IRSs, a multi-hop cascaded LoS link can be established between the BS and each user via their cooperative beam routing. Under this setup, we optimize the selected IRSs and their beam routing path for each user, along with the BS/IRS active/passive beamforming such that the minimum received signal power among all users is maximized, subject to a new multi-beam routing path separation constraint for avoiding the inter-user/route interference. To tackle this problem, we first derive the optimal BS/IRS active/passive beamforming in closed-form for any given beam routes and show the beam routing optimization is NP-complete by recasting it as an equivalent graph-optimization problem. To solve this challenging problem, we then propose an efficient recursive algorithm to partially enumerate the feasible solutions, which effectively balances the performance-complexity trade-off by tuning its design parameter. Numerical results demonstrate that the proposed algorithm can achieve near-optimal performance with low enumeration complexity and also outperform other benchmark schemes.

Intelligent reflecting surface (IRS) is deemed as a promising solution to improve the spectral and energy efficiency of wireless communications cost-effectively. In this paper, we consider a wireless network where multiple base stations (BSs) serve their respective users with the aid of distributed IRSs in the downlink communication. Specifically, each IRS assists in the transmission from its associated BS to user via passive beamforming, while in the meantime, it also randomly scatters the signals from other co-channel BSs, thus resulting in additional signal as well as interference paths in the network. As such, a new IRS-user/BS association problem arises pertaining to optimally balance the passive beamforming gains from all IRSs among different BS-user communication links. To address this new problem, we first derive a tractable lower bound of the average signal-to-interference-plus-noise ratio (SINR) at the receiver of each user, termed average-signal-to-average-interference-plus-noise ratio (ASAINR), based on which two ASAINR balancing problems are formulated to maximize the minimum ASAINR among all users by optimizing the IRS-user associations without and with BS transmit power control, respectively. We also characterize the scaling behavior of user ASAINRs with the increasing number of IRS reflecting elements to investigate the different effects of IRS-reflected signal versus interference power. Moreover, to solve the two ASAINR balancing problems that are both non-convex optimization problems, we propose an optimal solution to the problem without BS power control and low-complexity suboptimal solutions to both problems by applying the branch-and-bound method and exploiting new properties of the IRS-user associations, respectively. Numerical results verify our performance analysis and also demonstrate significant performance gains of the proposed solutions over benchmark schemes.

Intelligent reflecting surface (IRS) is a revolutionizing approach for achieving low-cost yet spectral and energy efficient wireless communications. By properly tuning its massive reflecting elements, IRS is able to construct favorable channels and thereby significantly improve the wireless communication performance in various setups. In this paper, we consider the general wireless network consisting of multiple base stations (BSs), users and IRSs, and investigate their joint association optimization in the downlink communication. Specifically, each IRS assists in the communication from its associated BS to user and in the meanwhile randomly scatters the signals from the other non-associated BSs. As such, the joint BS-IRS-user association is more involved as compared to the BS-user association in conventional wireless networks without IRS. To address this new problem, we first derive the average signal-to-interference-plus-noise ratio (SINR) of each user in closed-form and then formulate the joint association problem to maximize the users' utility in the downlink communication. Both the optimal and low-complexity suboptimal solutions are proposed for the formulated problem. Numerical results demonstrate significant performance gains of the proposed solutions over benchmark schemes.

The line-of-sight (LoS) dominant air-ground channels have posed critical interference issues in cellular-connected unmanned aerial vehicle (UAV) communications. In this paper, we propose a new base station (BS) cooperative beamforming (CB) technique for the cellular downlink to mitigate the strong interference caused by the co-channel terrestrial transmissions to the UAV. Besides the conventional CB by cooperatively transmitting the UAV's message, the serving BSs of the UAV exploit a novel CB-based interference transmission scheme to effectively suppress the terrestrial interference to the UAV. Specifically, the co-channel terrestrial users' messages are shared with the UAV's serving BSs and transmitted via CB so as to cancel their resultant interference at the UAV's receiver. To optimally balance between the CB gains for UAV signal enhancement and terrestrial interference cancellation, we formulate a new problem to maximize the UAV's receive signal-to-interference-plus-noise ratio (SINR) by jointly optimizing the power allocations at all of its serving BSs for transmitting the UAV's and co-channel terrestrial users' messages. First, we derive the closed-form optimal solution to this problem in the special case of one serving BS for the UAV and draw useful insights. Then, we propose an algorithm to solve the problem optimally in the general case. As the optimal solution requires centralized implementation with exorbitant message/channel information exchanges among the BSs, we further propose a distributed algorithm that is amenable to practical implementation, based on a new divide-and-conquer approach, whereby each co-channel BS divides its perceived interference to the UAV into multiple portions, each to be canceled by a different serving BS of the UAV with its best effort. Numerical results show that the proposed centralized and distributed CB schemes with interference transmission and cancellation (ITC) can both significantly improve the UAV's downlink performance as compared to the conventional CB without applying ITC.

The line-of-sight (LoS) dominant air-ground channels have posed critical interference issues in cellular-connected unmanned aerial vehicle (UAV) communications. In this paper, we propose a new base station (BS) cooperative beamforming (CB) technique for the cellular downlink to mitigate the strong interference caused by the co-channel terrestrial transmissions to the UAV. Besides the conventional CB by cooperatively transmitting the UAV's message, the serving BSs of the UAV exploit a novel CB-based interference transmission scheme to effectively suppress the terrestrial interference to the UAV. Specifically, the co-channel terrestrial users' messages are shared with the UAV's serving BSs and transmitted via CB so as to cancel their resultant interference at the UAV receiver. To optimally balance between the CB gains for UAV signal enhancement and terrestrial interference cancellation, we formulate a new problem to maximize the UAV's receive signal-to-interference- plus-noise ratio (SINR) by jointly optimizing the power allocations at all of its serving BSs for transmitting the UAV's and co-channel terrestrial users' messages. First, we derive the closed-form optimal solution to this problem in the special case of one serving BS for the UAV and draw useful insights. Then, we propose an algorithm to solve the problem optimally in the general case. Simulation results show that the proposed CB scheme with interference transmission and cancellation (ITC) significantly improves the UAV's downlink SINR as compared to the conventional CB without applying ITC.

This paper investigates a new scenario of spectrum sharing between unmanned aerial vehicle (UAV) and terrestrial wireless communication, in which a cognitive/secondary UAV transmitter communicates with a ground secondary receiver (SR), in the presence of a number of primary terrestrial communication links that operate over the same frequency band. We exploit the UAV’s mobility in three-dimensional (3D) space to improve its cognitive communication performance while controlling the co-channel interference at the primary receivers (PRs), such that the received interference power at each PR is below a prescribed threshold termed as interference temperature (IT). First, we consider the quasi-stationary UAV scenario, where the UAV is placed at a static location during each communication period of interest. In this case, we jointly optimize the UAV’s 3D placement and power control to maximize the SR’s achievable rate, subject to the UAV’s altitude and transmit power constraints, as well as a set of IT constraints at the PRs to protect their communications. Second, we consider the mobile UAV scenario, in which the UAV is dispatched to fly from an initial location to a final location within a given task period. We propose an efficient algorithm to maximize the SR’s average achievable rate over this period by jointly optimizing the UAV’s 3D trajectory and power control, subject to the additional constraints on UAV’s maximum flying speed and initial/final locations. Finally, numerical results are provided to evaluate the performance of the proposed designs for different scenarios, as compared to various benchmark schemes. It is shown that in the quasi-stationary scenario the UAV should be placed at its minimum altitude while in the mobile scenario the UAV should adjust its altitude along with horizontal trajectory, so as to maximize the SR’s achievable rate in both scenarios.

The line-of-sight (LoS) air-to-ground channel brings both opportunities and challenges in cellular-connected unmanned aerial vehicle (UAV) communications. On one hand, the LoS channels make more cellular base stations (BSs) visible to a UAV as compared to the ground users, which leads to a higher macro-diversity gain for UAV-BS communications. On the other hand, they also render the UAV to impose/suffer more severe uplink/downlink interference to/from the BSs, thus requiring more sophisticated inter-cell interference coordination (ICIC) techniques with more BSs involved. In this paper, we consider the uplink transmission from a UAV to cellular BSs, under spectrum sharing with the existing ground users. To investigate the optimal ICIC design and air-ground performance trade-off, we maximize the weighted sum-rate of the UAV and existing ground users by jointly optimizing the UAV's uplink cell associations and power allocations over multiple resource blocks. However, this problem is non-convex and difficult to be solved optimally. We first propose a centralized ICIC design to obtain a locally optimal solution based on the successive convex approximation (SCA) method. As the centralized ICIC requires global information of the network and substantial information exchange among an excessively large number of BSs, we further propose a decentralized ICIC scheme of significantly lower complexity and signaling overhead for implementation, by dividing the cellular BSs into small-size clusters and exploiting the LoS macro-diversity for exchanging information between the UAV and cluster-head BSs only. Numerical results show that the proposed centralized and decentralized ICIC schemes both achieve a near-optimal performance, and draw important design insights based on practical system setups.

The peculiar line-of-sight (LoS) propagation in air-to-ground channel provides both opportunities and challenges for the emerging cellular-connected unmanned aerial vehicle (UAV) communications. On one hand, the LoS channels make more cellular base stations (BSs) visible to a UAV as compared to ground users, which leads to a higher macro-diversity gain as compared to the ground users. On the other hand, the LoS channels also render the UAV to generate/receive more severe uplink/downlink interference to/from the BSs, thus requiring more sophisticated inter-cell interference coordination (ICIC) techniques with more BSs involved. To draw essential insight, this paper studies the uplink transmission from a UAV to cellular BSs. To mitigate the UAV's interference effect, we aim to maximize the sum-rate of the UAV and all ground users in its resulted ICIC region by jointly optimizing the UAV's cell association, resource block (RB) allocation, and transmit power. We first propose a centralized ICIC design that achieves the optimal performance. As the centralized ICIC requires global information of the network and substantial information exchange among an excessively large number of BSs, we further propose a decentralized ICIC scheme of significantly lower complexity and overhead for implementation. Specifically, we divide the cellular BSs into clusters, each with a dedicated cluster head for collecting information from its cluster BSs and exchanging information with the UAV by exploiting the LoS-induced macro-diversity. Numerical results show that the proposed decentralized ICIC scheme achieves a performance close to the optimal centralized design, and also outperforms the traditional ICIC scheme for cellular networks with terrestrial interference only.

This paper considers a new frequency-domain artificial noise (AN)-aided transmit design for service integration in a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. In this system, two sorts of service messages are combined and provided simultaneously at each frequency subchannel: one multicast message intended for all receivers and one confidential message intended for only one receiver. The confidential message is kept perfectly secure from all the other receivers. To characterize the tradeoff between the secrecy rate and multicast rate, our goal is to maximize the weighted sum of the two rates over all subchannels by jointly designing the input covariances for the multicast message, confidential message and AN at each subcarrier. This problem is nonconvex by nature and challenging to solve. To make it tractable, we recast this weighted sum rate maximization (WSRM) problem into a primal decomposable form, which is amenable to alternating optimization (AO). By this means, we can obtain a locally optimal solution to the WSRM problem. Numerical results are finally presented to show the efficacy of our proposed method and reveal some insights into our considered scheme.

This study considers a cellular multiple‐input single‐output system overheard by multiple eavesdroppers, in the presence of multiple pairs of single‐antenna device‐to‐device (D2D) nodes working as an underlay. All D2D nodes are permitted to access the cellular channel for their own communications. A novel eavesdropping scenario, termed as simultaneous eavesdropping, is studied, where the eavesdroppers intend to simultaneously overhear the cellular communication as well as D2D communications. Assuming imperfect channel state information (CSI) at the transmitter, the authors goal is to design the robust transmit covariance of confidential message and artificial noise, such that the worst‐case sum secrecy rate is maximised. This worst‐case sum secrecy rate maximisation problem is challenging to solve due to its non‐convexity and semi‐infiniteness incurred by the imperfect CSI. As a compromise, the authors develop a tractable approximation to its objective secrecy rate function for deriving a computationally efficient lower bound. Then a successive convex approximation (SCA) algorithm is proposed to solve the approximated problem in an iterative manner with provable convergence guarantee. Then it is proved that the SCA algorithm always yields a beam‐forming solution to the confidential message transmission. Numerical results illustrate that the proposed scheme outperforms the traditional one.

This paper considers a cellular multiple-input single- output (MISO) system overheard by multiple eavesdroppers, in the presence of one pair of single- antenna device-to-device (D2D) nodes working as an underlay. The D2D nodes are permitted to access the cellular channel for their own communications. We assume that the channel state information (CSI) on all links is imperfect, and more specifically, the CSI error follows an arbitrary distribution with only the first and second moments available at the transmitter. Our goal is to design the covariances of confidential message and artificial noise, as well as the transmit power at the D2D transmitter, such that the total consumed power is minimized subject to a sequence of worst-case outage constraints on the received signal- to-interference-plus-noise ratio (SINR) at each receiver. The worst-case outage constraints are imposed to satisfy SINR outage requirement under arbitrarily distributed CSI uncertainty. The resulting problem is challenging to solve due to its inherently complex structure. However, we reveal its hidden convexity by carrying out a duality-based reformulation. Then it is proved that the proposed method always yield a single- stream beamforming solution. The complexity analysis of our proposed method is also presented. Finally, the efficacy of the proposed design is demonstrated by simulations.

This paper considers a two-receiver multiple-input multiple-output (MIMO) Gaussian broadcast channel model with integrated services. Specifically, two sorts of service messages are combined and served simultaneously: one multicast message intended for both receivers and one confidential message intended for only one receiver and kept perfectly secure from the other receiver. Our goal is to jointly design the transmit covariances of the multicast message and confidential message, such that the secrecy capacity region is maximized. This maximization problem is a biobjective optimization problem, but can be converted into a general scalar optimization problem via our proposed method of scalarization. Nonetheless, the equivalent scalar problem is nonconvex by nature. To circumvent the nonconvex issue, a provably convergent difference-of-concave (DC) approach is introduced to solve it in an iterative fashion. In view of the high computational complexity of the DC approach, a power splitting method is also devised for fast implementation of service integration. The security performance and computational efficiency of our proposed algorithms are finally demonstrated by numerical results.

This paper considers a cellular multiple-input multiple-output multiple-eavesdropper (MIMOME) channel, with a pair of single-antenna device-to-device (D2D) nodes working as an underlay. A novel eavesdropping scenario is studied in this paper, where the eavesdroppers intend to simultaneously overhear the cellular communication and the D2D communication. Our goal is to jointly optimize the covariance of confidential message and artificial noise, as well as the transmit power at the D2D transmitter, such that the sum secrecy rate is maximized, while satisfying the quality of service constraint on the D2D communication. This sum secrecy rate maximization (SSRM) problem is non-convex by nature. To handle it, an equivalent reformulation of this SSRM problem is introduced, wherein the resulting problem becomes primal decomposable and thus can be iteratively solved using an alternating optimization (AO) algorithm. Also, we prove that the AO algorithm is bound to converge to a stationary point of the primal SSRM problem. Furthermore, we extend the SSRM problem to a more general case with multiple pairs of D2D nodes. Again, the resulting problem is shown to be solvable by the AO algorithm, with provable convergence to the stationary point. Finally, numerical results are presented to verify the efficacy of our proposed method.

Consider a cellular multiple-input single-output (MISO) channel, in the presence of multiple eavesdroppers (Eves) and one pair of single-antenna device-to-device (D2D) nodes working as an underlay. A novel eavesdropping scenario, termed as selective eavesdropping, is studied in this paper, where Eves arbitrarily select one target from the cellular receiver and the D2D receiver to overhear, but their selection is unknown to any other nodes. Since Eves' two sorts of selection would lead to two different secrecy rates, we define the achievable secrecy rate as the smaller one of the two rates. With imperfect channel state information on all links, our interest lies in the robust design of transmit covariances at the cellular transmitter, such that the worst-case achievable secrecy rate is maximized. This worst-case secrecy rate maximization problem is nonconvex by nature. To deal with it, we develop a convex approximation to seek a computationally efficient lower bound. In particular, the solution can be efficiently computed by successively solving a sequence of convex optimization problems. Then it is proved that the obtained lower bound is attainable by simply utilizing single-stream beamforming. Numerical results are finally presented to demonstrate the efficacy of our proposed methods.

This paper considers an energy-efficient transmit design in a multiple-input single-output (MISO) wiretap channel. In particular, a transmitter sends one confidential message to a legitimate receiver, which must be kept perfectly secure from multiple external single-antenna eavesdroppers. Assuming statistical eavesdroppers' channel state information (ECSI) at the transmitter, we aim to design the transmit beamformer, such that the outage secrecy energy efficiency (SEE) is maximized, subject to the outage-constrained secrecy rate and transmit power constraints. The resultant problem is intractable to solve even after introducing a semidefinite relaxation (SDR) reformulation. To handle it, an equivalent parametric reformulation, based on the fractional programming and difference-of-concave programming theories, is proposed to recast this problem as a convex problem. By this means, the maximum outage SEE can be found in an iterative fashion. Moreover, we also give an approach to constructing a rank-one covariance matrix from our proposed method, implying the feasibility of transmit beamforming to achieve the obtained SEE performance. Numerical results are presented to show the effectiveness of our proposed method.

This paper considers a cellular multiple-input single- output (MISO) system overheard by multiple eavesdroppers, in the presence of one pair of device- to-device (D2D) nodes working as an underlay to the cellular network. A novel eavesdropping scenario is studied in this paper, where the eavesdroppers intend to simultaneously overhear the cellular communication as well as the D2D communication. Assuming imperfect channel state information (CSI) at the transmitter, our goal is to design the input covariance matrix of confidential message such that the worst-case sum secrecy rate is maximized, while satisfying the quality of service (QoS) requirement in the D2D communication. Although this worst-case sum secrecy rate maximization (SSRM) problem is non-convex, we show that it can be handled by solving a sequence of semidefinite programming (SDP) problems. Moreover, we give complexity analysis of our proposed optimization method and prove that transmit beamforming is an optimal strategy for the confidential message transmission. Numerical results are presented to verify the efficacy of our proposed method.

This paper considers an optimal artificial noise (AN)-aided transmit design for multi-user MISO systems in the eyes of service integration. Specifically, two sorts of services are combined and served simultaneously: one multicast message intended for all receivers and one confidential message intended for only one receiver. The confidential message is kept perfectly secure from all the unauthorized receivers. This paper considers a general case of imperfect channel state information (CSI), aiming at a joint and robust design of the input covariances for the multicast message, confidential message and AN, such that the worst-case secrecy rate region is maximized subject to the sum power constraint. To this end, we reveal its hidden convexity and transform the original worst-case robust secrecy rate maximization (SRM) problem into a sequence of semidefinite programming. Numerical results are presented to show the efficacy of our proposed method.

This paper considers an artificial noise (AN)-aided transmit design for multi-user MISO systems in the eyes of service integration. Specifically, we combine two sorts of services, and serve them simultaneously: one multicast message intended for all receivers and one confidential message intended for only one authorized receiver. The confidential message is kept perfectly secure from all the unauthorized receivers. Assuming imperfect channel state information (CSI) of unauthorized receivers at the transmitter, our goal is to jointly design the input covariances of the multicast message, confidential message and AN such that the outage secrecy rate is maximized for a given outage probability, while keeping the outage probability of multicast message for each user below a certain threshold. Due to the intrinsical complexity of this problem, a safe and convex albeit suboptimal reformulation, based on two advanced convex restriction approaches, is applied to generate a tractable approximation for this problem. By this means, a computationally efficient lower bound on the outage secrecy rate can be determined. We also prove the feasibility of beamforming to achieve the obtained secrecy rate. Numerical results are presented to verify the efficacy of our proposed method.

This paper considers an energy-efficient transmit design in a three-node MISO wiretap channel in the eyes of service integration. Specifically, we combine two sorts of services, and serve them simultaneously: one multicast message intended for both receivers and one confidential message intended for only one authorized receiver. The confidential message must be kept perfectly secure from the unauthorized receiver. Our goal is to jointly design the input covariance matrices of the multicast message and confidential message such that the secrecy energy efficiency (SEE) is maximized, subject to the multicast rate, secrecy rate and total transmit power constraints. Due to the nonconvexity of this problem, an equivalent parametric reformulation, based on the fractional programming theory, is proposed to recast this problem as a sequence of semidefinite programs. By this means, the maximum SEE can be found via a root search algorithm. Moreover, we also give an approach to constructing a rank-one optimal covariance matrix of the confidential message from our proposed algorithm, which implies the feasibility of transmit beamforming to achieve the maximum SEE. Numerical results are finally presented to verify the efficacy of our proposed method.

This letter considers a two-receiver multiple-input single-output Gaussian broadcast channel model with integrated services. Specifically, two sorts of service messages are combined and served simultaneously: one multicast message intended for both receivers and one confidential message intended for only one receiver. The confidential message is kept perfectly secure from the unauthorized receiver. Our goal is to jointly design the input covariances for the multicast message and confidential message, such that the secrecy capacity region is maximized. This secrecy capacity region maximization (SCRM) problem is a nonconvex vector maximization problem. To deal with this issue, we reformulate the SCRM problem into a provably equivalent scalar optimization problem and propose a searching method to find its overall Pareto optimal points. Further, for implementation efficiency, transmit beamforming is proved to be Pareto optimal. However, since the two service messages are coupled in our optimization problem, it is difficult to deduce closed-form expressions of the Pareto optimal beamformers. A suboptimal transmit design is accordingly proposed to analytically obtain beamformers for both service messages. Numerical results illustrate that the performance gap between the Pareto optimal design and our proposal is negligible.

This letter considers a two-receiver multiple-input multiple-output Gaussian broadcast channel model with integrated services. Specifically, we combine two sorts of service messages, and serve them simultaneously: One multicast message intended for both receivers and one confidential message intended for only one receiver. The confidential message is kept perfectly secure from the unauthorized receiver. DueAN54-B6010-A001 to the coupling of service messages, it is intractable to seek capacity-achieving transmit covariance matrices. Accordingly, we propose a suboptimal precoding scheme based on the generalized singular value decomposition (GSVD). The GSVD produces several virtual orthogonal subchannels between the transmitter and the receivers. Subchannel allocation and power allocation between multicast message and confidential message are jointly optimized to maximize the secrecy rate in this letter, subject to the quality of multicast service constraints. Since this problem is inherently complex, a difference-of-concave algorithm, together with an exhaustive search, is exploited to handle the power allocation and subchannel allocation, respectively. Numerical results are presented to illustrate the efficacy of our proposed strategies.

In this paper, we consider the transmit design for multi-input multi-output (MIMO) wiretap channel including a malicious jammer. We first transform the system model into the traditional three-node wiretap channel by whitening the interference at the legitimate user. Additionally, the eavesdropper channel state information (ECSI) may be fully or statistically known, even unknown to the transmitter. Hence, some strategies are proposed in terms of different levels of ECSI available to the transmitter in our paper. For the case of unknown ECSI, a target rate for the legitimate user is first specified. And then an inverse water-filling algorithm is put forward to find the optimal power allocation for each information symbol, with a stepwise search being used to adjust the spatial dimension allocated to artificial noise (AN) such that the target rate is achievable. As for the case of statistical ECSI, several simulated channels are randomly generated according to the distribution of ECSI. We show that the ergodic secrecy capacity can be approximated as the average secrecy capacity of these simulated channels. Through maximizing this average secrecy capacity, we can obtain a feasible power and spatial dimension allocation scheme by using one dimension search. Finally, numerical results reveal the effectiveness and computational efficiency of our algorithms.