This paper proposes an integrated anti-jamming covert communication and sensing system assisted by reconfigurable intelligent surfaces (RIS). By jointly optimizing beamforming vectors and RIS phase shifts, the system maximizes the sum transmission rate while enhancing communication security, sensing accuracy, and anti-jamming capability. We present two comprehensive optimization schemes: a perfect scheme under ideal channel conditions and a robust scheme for practical scenarios. The perfect scheme jointly optimizes beamforming and phase shifts when perfect channel state information (CSI) is available, establishing a performance upper bound. The robust scheme addresses practical transmission challenges by transforming stochastic uncertainties from imperfect CSI and phase shift errors into deterministic constraints through statistical expectation analysis and worst-case formulations, ensuring reliable system performance under realistic conditions. Both schemes effectively solve the resulting non-convex problems through innovative mathematical reformulations using fractional programming, quadratic transformation techniques, and the alternating direction method of multipliers. Comprehensive simulation results demonstrate significant advantages of our proposed framework in communication reliability, sensing accuracy, and resilience against the jammer compared to conventional approaches.

By integrating the Internet of Things and sensing technologies, transportation systems can achieve higher management accuracy and efficiency. This paper explores a Reconfigurable Intelligent Surface (RIS) assisted integrated wireless sensing and power transfer (IWSPT) system in traffic scenarios with channel estimation errors and obstacles. Specifically, a transmitter deployed within transportation infrastructure optimizes the beamforming vector and RIS phase shifts cooperatively. The objective is to maximize the energy received by multiple energy harvesting devices (EHDs) under the constraint of beampattern thresholds for sensing in multiple directions. The coupled optimization variables in the proposed problem yield a non-convex result, so we propose a semi-infinite relaxation-based method for solving this optimization problem. We then introduce a low-complexity optimization algorithm to address the high computational complexity of the semi-infinite relaxation approach. The proposed algorithm significantly reduces the computational burden by leveraging Taylor expansion and successive convex approximation (SCA) techniques. Simulation results validate the effectiveness and robustness of the algorithm, highlighting its practical applicability in intelligent transportation systems.

In this paper, we propose a multi-SNR adaptive Semantic Communication (SemCom) System based on Recon figurable Intelligent Surface (RIS) to solve the problem of insufficient adaptability of traditional SemCom in dynamic chan nel environments. We firstly design a RIS-enhanced Semantic Communication (RISemCom) System that innovatively combines a programmable wireless environment with Deep Learning (DL) to achieve joint optimization of channel environment and se mantic feature extraction. Next, two training algorithms are proposed: Dynamic Random Environment Adaptive Multi-SNR (DREAMS) algorithm and Two-Stage Training (TST) algorithm. The DREAMS dynamically adjusts SNR values during training, allowing a single model to adapt to a wide range of SNR conditions while significantly reducing deployment complexity. The TST serves as a comparison baseline, providing a dedicated optimized model for each specific SNR environment. Numerical results are demonstrated to confirm that the DREAMS algorithm maintains excellent performance across a wide range of SNRs with a single model, and significantly improves the PSNR and SSIM metrics compared to traditional methods under low SNR conditions. The performance gain is particularly notable in challenging low SNR environments, proving the system's robustness in adverse channel conditions. This work not only expands the applicability of SemCom but also provides new insights for reliable communication in variable channel environments in future 6G networks.

In this paper, we investigate a millimeter-wave secure integrated sensing and communication system assisted by the sub-connected (SC) active reconfigurable intelligent surface (SC-ARIS), where the dual-function radar and communication station (DFBS) employs a hybrid precoding structure. We formulate an optimization problem to jointly design the DFBS hybrid precoding and SC-ARIS beamforming, aiming to maximize the secure energy efficiency while ensuring communication and sensing qualities. To address the above non-convex problem, we utilize alternating optimization technique to decouple it into two subproblems, where DFBS hybrid precoding and SC-ARIS beamforming are respectively optimized. For the first one, we first propose an iterative algorithm to solve the equivalent digital precoding based on constrained concave-convex procedure, Taylor expansion, semidefinite relaxation (SDR) and fractional programming techniques. Then, the hybrid precoding is obtained rely on the manifold optimization alternating minimization technique. For the later one, we propose an iterative algorithm based on the SDR. Considering a more realistic scenario, we extend to the imperfect eavesdropping channel, and propose a robust beamforming design scheme. Finally, simulation results show the effectiveness of the proposed schemes.

Intelligent reflecting surface (IRS) has been considered a promising technology to be applied to mobile edge computing (MEC) systems, especially when offloading links are blocked or weak. However, most existing works are restricted to narrow-band channel and ideal IRS reflection model, which is not practical and may lead to significant performance degradation. Thus, we consider an IRS-enhanced wideband MEC system with practical IRS reflection model. Our objective is to minimize the weighted latency of all devices by jointly optimizing the offloading data volume, edge computing resources, BS receiving vector, and IRS basic phase shift (BPS). Since the formulated problem is non-convex, we employ the block coordinate descent (BCD) technique to decouple it into two subproblems to alternatively optimize computing and communication resources. In particular, the computing resource optimization subproblem is solved based on Karush-Kuhn-Tucker (KKT) conditions and bisection search method. While the communication resource optimization subproblem is first transformed into a weighted sum-rate maximization problem based on LDR technique and KKT conditions. Then leveraging the equivalence between sum-rate maximization and MSE minimization, it is converted into a multi-variable problem that can be effectively solved using BCD technique. Simulation results show that the proposed schemes can reduce latency by 16% compared to baseline schemes when the number of IRS elements is 100, confirming the effectiveness of considering practical IRS reflection model for wideband MEC systems.

Emerging wireless applications pursue a paradigm shift towards the integrated system that is capable of secure data transmission and high-resolution sensing in near-field environments. Conventional far-field use-cases suffer from the fundamental limitations in security, spatial precision, and spectral coexistence. Against this backdrop, this paper investigates an integrated covert communications and sensing (ICCS) system operating in the near-field environment. Specifically, the transmitter (Alice) aims to covertly convey messages to legitimate receivers (Bobs), while circumventing the detection by the eavesdropper (Willie) as well as improving the sensing performance at the target. To elevate communication performance, we aim to maximize the achievable sum rate to jointly optimize the communication and sensing beamforming matrices at Alice. The optimization problem is subject to multiple constraints with coupled variables: the transmit power budget at Alice, the minimum communication rate requirements for Bob, the Cram$\acute {e}$r-Rao bound (CRB) constraint to ensure accurate parameter estimation in sensing, and the covertness constraint against Willie’s detection. Given the non-convex nature of the formulated problem, an efficient successive convex approximation and semidefinite relaxation algorithms are proposed. In addition, we provide a theoretical analysis to confirm the convergence behaviour of the proposed algorithm, which can achieve the near-optimal solution. Finally, the numerical results are presented to highlight the superiority of the proposed ICCS system over existing counterparts. These results numerically verify the effectiveness of the proposed approach in enhancing communication rates while maintaining sensing performance and covertness in the near-field regime.

Two nascent technologies, rate-splitting multiple access (RSMA) and reconfigurable intelligent surfaces (RIS), present promising avenues to enhance spectral and energy efficiencies within multi-antenna frameworks. However, passive RIS may encounter challenges in delivering substantial capacity gains due to the cumulative path loss effect. Active RIS (ARIS) equipped with low-cost amplifiers in the reflective elements emerges as a solution to mitigate the limitation. This paper investigates a multi-user multiple-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) framework, augmented by an ARIS and leveraging RSMA. The primary objective is to maximize the system's SE, subject to constraints imposed by the design of BS beamforming vectors, PS ratios, and RIS phase shifts. To address the inherent nonconvexity of this optimization problem, we propose an innovative approach that combines alternating optimization (AO) and semidefinite relaxation (SDR) algorithms. Simulations results demonstrate the significant advantages of our proposed design over established benchmarks.

In this paper, we study the beamforming design in near-field integrated sensing and covert communication systems, where the transmitter (Alice) covertly sends information to a legitimate user (Bob) and senses the target concurrently while hiding from illegal eavesdropper (Willie). Considering a perfect Willie-involved CSI scenario, we propose a beamforming optimization problem to minimize the Cramér-Rao bound for sensing parameters under the conditions of total transmit power, the minimum communication rate and covertness constraint. For this optimization problem, the global optimal solution is obtained by using the semidefinite relaxation (SDR). Compared with the near-field integrated sensing and communication (ISAC) systems without covert constraint, the numerical results verify the effectiveness and feasibility of the proposed scheme.

Fluid antenna (FA) is capable of achieving a significant degree of spatial diversity within the limited space of a wireless device by adjusting the radiating elements to optimal positions. In this article, we explore the potential of deploying FAs on the overall performance of wireless powered communication network (WPCN). Specifically, each Internet of Things (IoT) device in WPCN is equipped with a single FA comprising multiple ports. The IoT device (ID) selects the optimal receive port for energy harvesting from the power beacon (PB), followed by choosing the optimal transmit port to send its data to the access point (AP). Our objective is to maximize the sum throughput of IDs by jointly optimizing port activation and time scheduling, subject to constraints on the received signal-to-noise ratio (SNR) of each individual ID and the total transmission time. To tackle this nonconvex problem, we first apply the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions to find the optimal solutions for time slots. Then, we introduce an efficient algorithm based on the alternating optimization (AO) method to iteratively achieve a locally optimal solution for port activation. Additionally, a low-complexity scheme is proposed to minimize computational overhead. Simulation results reveal that incorporating FAs into a WPCN markedly improves the overall system performance, and highlights the benefits of port selection for the FA in comparison to baseline methods.

We investigate the problem of maximizing anti-jamming sum throughput in an RIS-assisted Internet of Things (IoT) network. The network’s operation is divided into two stages: 1) IoT terminals first harvest energy from the wireless energy station (WES) and 2) they then transmit their information to the information receiver (IR) using a frequency division multiple access (FDMA) protocol. We consider three different design scenarios: 1) ideal hardware; 2) phase shift error (PSE); and 3) a combination of both PSE and transceiver hardware impairments (THIs). To address the nonconvexities of these designs, we employ novel techniques, such as the Lagrangian dual method, Karush–Kuhn–Tucker (KKT) conditions, quadratic transformation (QT), element-wise block coordinate descent (EBCD), complex circle manifold (CCM), and 1-D search to obtain the optimal solutions. Numerical results are provided to illustrate that the proposed approaches outperform existing benchmarks.

This article focuses on maximizing the anti-jamming sum throughput in a time division multiple access (TDMA)-based reconfigurable intelligent surfaces (RIS)-assisted wireless powered Internet of Things (WP-IoT) network. In this setup, multiple IoT devices harvest energy from wireless energy stations (WES) and then utilize the collected energy to upload their own data to an information receiver (IR). The network also includes a jammer that sends jamming signals to the IR, and a RIS is deployed to mitigate this jamming effect and enhance the sum throughput. This study addresses both an upper bound design and a robust design with fractional nonlinear energy harvesting model. The primary optimization goal is to maximize the anti-jamming sum throughput, with the constraints of RIS phase shifts and time scheduling. For both designs, closed-form expressions for time scheduling are derived using the Lagrangian duality and Karush-Kuhn-Tucker (KKT) conditions. The quadratic transformation (QT) technique is used to handle fractional functions within the optimization. Furthermore, the phase shifts are optimized iteratively using the element-wise block coordinate descent (EBCD) and Riemannian manifold optimization (RMO) algorithms. Simulation results are presented to validate the effectiveness of the proposed approaches.

This paper investigates a near-field integrated sensing and symbiotic radio (SR) communication system supported by a reconfigurable intelligent surface (RIS). In the near-field region, the base station (BS) leverages the RIS to realize symbiotic communication performance while simultaneously performing target sensing by analyzing echo signals. The BS antenna architecture encompasses both fully-digital and dynamic metasurface antenna (DMA) configurations. An optimization problem is developed to maximize the symbiotic transmission rate for the IoT devices, subject to constraints imposed by the Cram4er-Rao bound (CRB), the signal-to-noise ratio (SNR), the RIS phase shifts, the antenna parameters and system power. An alternating optimization (AO) framework with a semidefinite relaxation (SDR) is proposed to solve the problem, while for the Lorentz-constrained phase matrix of the frequency response of DMA surface elements, we propose to apply the Riemannian conjugate gradient (RCG) algorithm to solve it. Numerical results validate the efficiency of the proposed framework, demonstrating that the near-field approach enables accurate target localization. Furthermore, where the DMA configuration achieves higher symbiotic transmission rates with lower power consumption compared to fully-digital antennas.

In this paper, the sensing beam pattern gain under simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS)-enabled integrated sensing and communications (ISAC) systems is investigated, in which the dual-functional base station (DFBS) provides multiple targets sensing in the presence of environment clutters and communicates with multiple users simultaneously. However, multiple targets detection introduces new challenges, since the STAR-RIS cannot directly send sensing beams and detect targets, the DFBS is required to analyze the echoes of the targets. While the echoes reflected by different targets through STAR-RIS come from the same direction for the DFBS, making it difficult to distinguish them. To circumvent this issue, we first introduce the signature sequence (SS) modulation scheme to the STAR-RIS-enabled ISAC system, thus ensuring that DFBS can detect different targets through the SS modulated sensing beams. Next, via the joint beamforming design of DFBS and STAR-RIS, we develop a max-min sensing beam pattern gain problem, and meanwhile, considering the communication quality requirements, the interference limitations of multi-targets and clutters, the passive nature constraint of STAR-RIS, and the total transmit power limitation. Then, to tackle the complex non-convex problem, we propose an alternating optimization method to divide it into two sub-problems and iteratively solve them until convergence. For the former, by relaxing the rank-one constraint, the problem is transformed into the standard convex quadratic semi-definite program and can be solved through the semi-definite relaxation and semi-definite programming algorithms. For the latter, the penalty-based algorithm is used to convert the rank-one constraints as penalty terms to the objective function, and the successive convex approximation method is leveraged to solve it. Finally, simulation results are conducted to validate the benefits and efficiency of our proposed scheme.

In this paper, we propose a reconfigurable intelligent surface (RIS)-aided wireless powered anti-jamming communication network (WPAJCN), where the RIS is utilized to participate in downlink wireless power transfer (WPT), as well as uplink anti-jamming wireless information transfer (AJ-WIT). To evaluate the network anti-jamming performance, we maximize a sum anti-jamming throughput, with the constraints of downlink WPT and uplink AJ-WIT time scheduling, and unit-modulus RIS phase shifts. The formulated problem is not convex in terms of these two types of coupled variables, which cannot be directly solved. To address this problem, the Lagrange dual method and Karush-Kuhn-Tucker conditions are presented to transform its sum-of-logarithmic objective function into the logarithmically fractional counterpart, which reformulate the original problem into that with respect to RIS phase shift vectors and WPT time scheduling. Next, we propose to apply the Dinkelback algorithm to solve a non-linear fractional programming with respect to the downlink WPT and uplink AJ-WIT RIS phase shifts in an alternating fashion, each of which is derived into a semi-closed solution by utilizing theRiemannian Manifold Optimization(RMO). In addition, the optimal WPT time scheduling is obtained by numerical search. Finally, the numerical results are demonstrated to confirm the improved performance of the proposed approach compared to the benchmark counterparts, which highlights the that RIS can effectively enhance the uplink anti-jamming WIT capability as well as the downlink WPT efficiency.

A novel integrated wireless powered sensing and communication (IWPSAC) framework is proposed. Specifically, a multi-antenna transmitter utilizes a radar signal for sensing targets while enabling multiple Internet of Things (IoT) devices to harvest energy from the signal, each of which employs the collected energy to upload information to an access point (AP). Our setup further considers a reconfigurable intelligent surface (RIS) to integrate sensing, wireless energy transfer (WET) and wireless information transfer (WIT) by optimizing the phase shifts. We formulate an optimization problem to maximize the weighted sum of the communication throughput and the beampattern gain by jointly designing the energy beamforming, transmission time scheduling and RIS phase shifts. The presence of multiple coupled variables in the formulated problem renders the optimization problem non-jointly convex. To address its non-convexity, we first derive a closed-form expression for the optimal RIS phase shifts in the WIT phase. Then, an alternating optimization (AO) algorithm is proposed to solve the tradeoff problem iteratively. Concretely, this involves alternating the design of the energy beamforming and the RIS phase shifts for sensing/WET by leveraging the semidefinite programming (SDP) relaxation method. To overcome the high complexity introduced by the SDP, we introduce a low complexity AO algorithm that derives the optimal solutions for energy beamforming, transmission time scheduling, and sensing/WET phase shift using successive convex approximation (SCA), Lagrangian duality methods, Karush-Kuhn-Tucker (KKT) conditions, and the element-wise block coordinate descent (EBCD) approach. Simulation results demonstrate the performance of the proposed algorithms and underscore the superior benefits of the RIS compared to baseline schemes.

Traditional grid-based compressed sensing algorithms usually suffer from the base mismatch effect in channel estimation problems. To address this, we propose a novel gridless uplink/downlink (UL/DL) channel estimation strategy for millimeter wave (mmWave) massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. By exploiting inherent sparsity in the angle-delay domain of the mmWave channel, we first formulate the UL channel estimation problem as a joint sparse signal recovery problem. Then, we introduce the reweighted atomic norm for enhancing angular resolution of the mmWave channel on continuous Fourier dictionaries; we suggest a novel reweighted atomic norm minimization (NRAM) algorithm to solve the channel estimation problem by leveraging the Hankel-Toeplitz block model with multiple measurement vectors (MMVs), and the original NRAM problem is approximated by the solution of a semi-definite programming (SDP) problem with structured sparsity, which is efficiently solved by a low-complexity alternating direction multiplier method (ADMM). Subsequently, in the frequency division duplex (FDD) system, we design a simplified DL channel estimation scheme by leveraging the angle-delay reciprocity of UL and DL channels. This scheme reconstructs the DL channel matrix using the angle and path delay estimated from the UL channel, along with the channel gain obtained through least squares (LS). Finally, simulation results validate that our proposed approach achieves superior channel estimation accuracy and reduces pilot overhead compared to conventional UL/DL channel estimation techniques.

Vehicle-to-Everything (V2X) is an important application scenario in 6G, where secure transmission is crucial in vehicular networks. Thus, this paper explores the application of an intelligent reflecting surface (IRS) in secure multipleinput multiple-output (MIMO) communication systems, which is subject to an eavesdropper equipped with multiple antennas. We formulate the secrecy rate maximization problem by jointly designing the transmit beamforming and the IRS phase-shift. Due to the coupling of the variables, the formulated problem is non-convex and thus we split the original problem into two sub-problems. For the two sub-problems, we first relax the sub-problem into a semi-definite program problem and solve it with the CVX tools. To further provide more insights into the calculation of the IRS phase-shift, we proposed the Riemannian manifold optimization (RMO) and majorization minimization (MM) algorithms to derive the closed-form solution of this subproblem. The numerical results validate that: 1) Through the proposed RMO and MM algorithms, the computation complexity is effectively reduced; and 2) the secure performance is significantly improved by the IRS.

The Internet-of-Things (IoT) has greatly facilitated our daily lives. Nevertheless, how to achieve higher spectral efficiency, large-scale device access, and lower latency for the next-generation IoT is still a challenge. Inspired by this, a non-orthogonal multiple access (NOMA) assisted cognitive radio (CR) IoT network is proposed in this paper, where the communication between the indoor secondary transmitter and secondary receivers is performed in the presence of an eavesdropper and under the constraint of secondary transmit power. In particular, we introduce simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) into the secondary network to assist the secondary transmitter to communicate with its receivers in different rooms. To characterize the reliability and security of the proposed system, we derive analytical approximate expressions for the outage probabilitys (OPs) and intercept probabilitys (IPs) by using Gaussian-Chebyshev quadrature. With the aim of providing a deeper understanding, we also explore the impacts of transmission signal-to-noise ratios (SNRs), power allocation coefficient and the number of STAR-RIS elements on the system performance. Presented numerical results show that: 1) the OPs of near and far users gradually decrease with SNRs until floors appear at high SNR, and the floors of near user is always lower than that of far user; 2) IPs increasing with SNRs and near user is always less than far user, which proves that near user has better security; 3) under appropriate parameters, the trade-off between reliability and security of the considered system can be arisen.

Reconfigurable intelligent surface (RIS), which can manipulate the wireless environment, has recently been integrated into physical-layer key generation (PKG) systems to establish randomness symmetric keys in static environments. However, few studies have jointly considered the optimization and randomization of the RIS elements to simultaneously achieve a high key generation rate (KGR) and communication performance. This study proposes a RIS-assisted communication and security-integrated protocol for dual-function integration of communication enhancement and PKG in static environments. The protocol partitions RIS elements for beamforming and random beams for parallel execution, utilizing a sparsity adaptive matching pursuit-based channel estimation algorithm to obtain individual channel state information. Subsequently, an optimization problem is formulated for KGR maximization while satisfying quality of service (QoS) requirements. The non-convex optimization problem is addressed through monotonicity analysis and triangle inequality. We validate the efficacy of the proposed protocol by developing a 4.9 GHz RIS-assisted PKG prototype system, comprising modular hardware and flexible software. The field trials demonstrate a 30.81 bit/s KGR for static environments with an average received power increase of 10.6 dB, achieving effective simultaneous integration of communication and security.

This article proposes an intelligent reflecting surface (IRS) assisted integrated sensing and communication (ISAC) system operating in the millimeter-wave band. Specifically, the ISAC system consists of a radar subsystem and a communication subsystem to detect multiple targets and communicate with the users simultaneously. The IRS is used to configure the radio propagation environment by changing the phase of the radio signal to enhance the communication transmission rate. In the proposed scheme, we first derive a closed-form solution for the radar signal covariance matrix to generate a radar beampattern in the angle of interest. Then, we jointly optimize the beamforming vector of the communication subsystem and the IRS phase shifts to enhance the communication transmission rate. To decouple the multiple variables to be optimized, the alternating optimization and quadratic transformation methods are applied to determine the communication beamforming vector and the IRS phase shifts. Specifically, we utilize the majorization minimization and the complex circle manifold methods to compute the IRS phase shifts. Simulation results verify the effectiveness of the proposed algorithm and demonstrate that an IRS can improve the performance of ISAC systems.

Integrated sensing and communication (ISAC) systems can suffer from malicious jamming attacks due to the open nature of wireless channels. Deploying aerial intelligent reflecting surface (AIRS) can flexibly configure the propagation environment of ISAC to address this threat. In this paper, we propose an anti-jamming scheme for ISAC via AIRS. Our goal is to maximize the achievable sum rate by jointly optimizing the transmitting beamforming at the dual-function base station, as well as the phase shift matrix and deployment of AIRS, while satisfying the echo signal-to-interference-plus-noise ratio requirement of target sensing. To handle this non-convex problem with multiple coupled variables, we decompose it into three sub-problems and solve them via the alternate optimization. We first introduce auxiliary variables to convert the transmit beamforming sub-problem into a convex counterpart and solve it via semi-definite relaxation. Then, the IRS phase-shift design is transformed into an equivalent rank-constrained problem, and the penalty-based method and the first-order Taylor expansion are leveraged to calculate the passive beamforming. Finally, with the optimized active and passive beamformings, we resort to successive convex approximation to optimize the AIRS deployment. Simulation results are presented to verify the feasibility and effectiveness of the proposed scheme.

The incredible potentiality of reconfigurable intelligent surface (RIS) in addressing power supply and obstacle environment of Internet of Medical Things (IoMT) has been capturing our interest. Considering the nettlesome "double-fading" effect introduced by passive RIS, we investigate an active RIS-enhanced IoMT system in this article, where the wireless power transfer (WPT) from power station (PS) to IoMT devices and the wireless information transfer (WIT) from IoMT devices to the access point (AP) are both implemented with the assistance of active RIS. Aiming to maximize the sum throughput of the considered IoMT system, a joint design of time schedules and reflecting coefficient matrices of the active RIS is proposed. Trapped by the non-convex and obstinate optimization problem, we explore the semi-definite programming (SDP) relaxation and successive convex approximation (SCA) techniques based on alternating optimization (AO) algorithm. Simulation results verify our solution approach to the intractable optimization problem and showcase the boosted spectrum and energy efficiency of the active RIS-enhanced IoMT system.

Wireless sensing and wireless energy are enablers to pave the way for smart transportation and a greener future. In this paper, an intelligent reflecting surface (IRS) assisted integrated sensing and wireless power transfer (ISWPT) system is investigated, where the transmitter in transportation infrastructure networks sends signals to sense multiple targets and simultaneously to multiple energy harvesting devices (EHDs) to power them. Recognizing the inherent tradeoff between energy harvesting and sensing performance, we propose to jointly optimize the system performance via optimizing the beamforming and IRS phase shift. However, the coupling of optimization variables makes the formulated problem non-convex. Thus, an alternative optimization approach is introduced and based on which two algorithms are proposed to solve the problem. Specifically, the first algorithm involves the semi-positive definite programming techniques, and the second algorithm is based on the successive convex approximations and majorization minimization to design the closed form solutions of the optimization variables, which can effectively reduce the computational complexity. Our simulation results validate the proposed algorithms and demonstrate the advantages of using IRS to assist wireless power transfer in ISWPT systems. This research contributes to the integration of wireless sensing and wireless energy in intelligent transportation systems and underscores the optimization of system performance through the introduction of IRS.

In this paper, we investigate an active simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted integrated sensing and communications (ISAC) system, where the dual-functional base station (DFBS) operates in full-duplex (FD) mode to provide communication services and performs targets sensing simultaneously. Meanwhile, we consider multiple targets and multiple users scenario as well as the self-interference at the FD DFBS. Through jointly optimizing the DFBS and active STAR-RIS beamforming under different work modes, our purpose is to achieve the maximum communication sum-rate, while satisfying the minimum radar signal-to-interference-plus-noise ratio (SINR) constraint, the active STAR-RIS hardware constraints and the total power constraint of DFBS and active STAR-RIS. To tackle the complex non-convex optimization problem formulated, an efficient alternating optimization algorithm is proposed. Specifically, the fractional programming method is first leveraged to turn the original problem into a more tractable one, and subsequently the transformed problem is decomposed into several sub-problems. Next, we develop a derivation method to obtain the closed-form expression of the radar receiving beamforming, and then the DFBS transmit beamforming is optimized under the radar SINR requirement and total power constraints. After that, the active STAR-RIS reflection and transmission beamforming are optimized by majorization minimization, complex circle manifold and convex optimization techniques. Finally, the proposed schemes are conducted through numerical simulations to show their benefits and efficiency.

In this paper, an anti-jamming design for integrated sensing and communication (ISAC) via aerial intelligent reflecting surface (AIRS) is proposed. We aim to maximize the achievable sum rate by jointly optimizing the active and passive beamformings, as well as the deployment of AIRS, while satisfying the echo signal-to-interference-plus-noise ratio requirement of target sensing. To address the non-convex problem, we decompose it into three sub-problems and solve them via the alternate optimization. First, the active beamforming is calculated via semi-definite relaxation. Then, the penalty-based method and the first-order Taylor expansion are leveraged to solve the passive beamforming. With the optimized active and passive beamformings, we resort to successive convex approximation to optimize the AIRS deployment. Numerical results validate the effectiveness of the proposed scheme.

This article studies secure communication in an Internet of Things (IoT) network, where the confidential signal is sent by an active refracting reconfigurable intelligent surface (RIS)-based transmitter, and a passive reflective RIS is utilized to improve the secrecy performance of users in the presence of multiple eavesdroppers. Specifically, we aim to maximize the weighted sum secrecy rate by jointly designing the power allocation, transmit beamforming (BF) of the refracting RIS, and the phase shifts of the reflective RIS. To solve the nonconvex optimization problem, we propose a linearization method to approximate the objective function into a linear form. Then, an alternating optimization (AO) scheme is proposed to jointly optimize the power allocation factors, BF vector, and phase shifts, where the first one is found using the Lagrange dual method, while the latter two are obtained by utilizing the penalty dual decomposition method. Moreover, considering the demands of green and secure communications, by applying Dinkelbach’s method, we extend our proposed scheme to solving a secrecy energy maximization problem. Finally, simulation results demonstrate the effectiveness of the proposed design.

Intelligent reflecting surface (IRS) is a promising technique to extend the network coverage and improve spectral efficiency. This paper investigates an IRS-assisted terahertz (THz) multiple-input multiple-output (MIMO)-nonorthogonal multiple access (NOMA) system based on hybrid precoding with the presence of eavesdropper. Two types of sparse RF chain antenna structures are adopted, i.e., sub-connected structure and fully connected structure. First, cluster heads are selected for each beam, and analog precoding based on discrete phase is designed. Then, users are clustered based on channel correlation, and NOMA technology is employed to serve the users. In addition, a low-complexity forced-zero method is utilized to design digital precoding in order to eliminate inter-cluster interference. On this basis, we propose a secure transmission scheme to maximize the sum secrecy rate by jointly optimizing the power allocation and phase shifts of IRS subject to the total transmit power budget, minimal achievable rate requirement of each user, and IRS reflection coefficients. Due to multiple coupled variables, the formulated problem leads to a non-convex issue. We apply the Taylor series expansion and semidefinite programming to convert the original non-convex problem into a convex one. Then, an alternating optimization algorithm is developed to obtain a feasible solution of the original problem. Simulation results verify the convergence of the proposed algorithm, and deploying IRS can bring significant beamforming gains to suppress the eavesdropping.

In this paper, we introduce an intelligent reflecting surface (IRS)-assisted wireless powered heterogeneous network (WPHN) consisting of two heterogeneous groups of devices. Specifically, one group of devices, i.e., energy-harvesting devices (EHDs), are charged by external energy supplies, while the other group of devices, i.e., non-energy-harvesting devices (NEHDs), are powered by internal energy supplies. An IRS aims to participate in the wireless energy transfer (WET) in downlink and the wireless information transfer (WIT) in the uplink. A sum throughput maximization problem is formulated subject to the constraints of individual energy consumption, transmission time scheduling, and IRS phase shifts. To cope with the non-convexity of the problem, we first derive the optimal IRS phase shifts of the uplink WIT independently. Next, the semi-definite programming (SDP) relaxation is adopted to recast this non-convex problem into the convex one, which can be numerically solved. Then, a novel low-complexity scheme is developed to gain more insights and mitigate the computational complexity induced by the SDP relaxation. In particular, the dual problem and Karush-Kuhn-Tucker conditions are first utilized to obtain the optimal transmission time scheduling. Then, we propose a method based on Riemannian manifold optimization to compute the optimal IRS phase shifts of the downlink WET in closed-form. Finally, simulation results are presented to verify the optimality of our proposed scheme, and highlight the benefits induced by the IRS to coordinate these heterogeneous devices.

This paper proposes an intelligent reflecting surface (IRS) assisted integrated sensing and communication (ISAC) system operating at the millimeter-wave (mmWave) band. Specifically, the ISAC system combines communication and radar operations and performs on the same hardware platform, detecting and communicating simultaneously with multiple targets and users. The IRS dynamically controls the amplitude or phase of the radio signal via the reflecting elements to reconfigure the radio propagation environment and enhance the transmission rate of the ISAC system in the mmWave band. By jointly designing the radar signal covariance (RSC) matrix, the beamforming vector of the communication system, and the IRS phase shift, the ISAC system transmission rate can be improved while matching the desired waveform for radar. The problem is non-convex due to multivariate coupling, and thus we decompose it into two separate subproblems. First, a closed-form solution of the RSC matrix is derived from the radar desired waveform. Next, the quadratic transformation (QT) technique is applied to the subproblem, and then alternating optimization (AO) is applied to determine the communication beamforming vector and the IRS phase shift. Also, we derive a closed-form solution for the formulated problem, effectively decreasing computational complexity. Finally, the simulations verify the effectiveness of the algorithm and demonstrate that the IRS can improve the performance of the ISAC system.

This article investigates intelligent reflecting surface (IRS)-assisted wireless-powered Internet of Things (IoT) networks. Specifically, multiple IoT devices first collect energy radiated from a power station (PS), then each device uses its harvested energy to support data transmission to an access point (AP) via frequency-division multiple access (FDMA). In addition, an IRS aims to improve wireless energy transfer (WET) and wireless information transfer (WIT) capabilities using passive reflection beamformers. The system sum throughput, as a performance metric, is maximized evaluate the overall performance of the considered model, which is subject to the constraints of IRS phase shifts, transmission time scheduling, and bandwidth allocation. This problem is not convex with respect to multiple coupled variables, and cannot be directly solved. To circumvent this nonconvexity, the transmission time scheduling and the bandwidth allocation are optimally designed in the closed form by the Lagrange dual method and the Karush–Kuhn–Tucker (KKT) conditions. Moreover, an alternating optimization (AO) algorithm is used to optimally design the IRS’s phase shifts during the WET and WIT phases in an alternating fashion. Specifically, we propose elementwise block coordinate decent (EBCD) and complex circle manifold (CCM) algorithms to iteratively derive the optimal phase shifts in the closed form. We also characterize the convergence behavior of the proposed algorithms. Finally, numerical results are presented to validate the performance of the proposed scheme, where the benefits of the IRS are highlighted in terms of sum throughput, transmission time scheduling, and energy harvesting, compared with the benchmark schemes.

This paper investigates the robust beamforming design in a secrecy multiple-input single-output (MISO) network aided by the intelligent reflecting surface (IRS) with simultaneous wireless information and power transfer (SWIPT). Specifically, by considering that the energy receivers (ERs) are potential eavesdroppers (Eves) and imperfect channel state information (CSI) of the direct and cascaded channels can be obtained, we investigate the max-min fairness robust secrecy design. The objective is to maximize the minimum robust information rate among the legitimate information receivers (IRs). To solve the formulated non-convex design problem in bounded and probabilistic CSI error models, we utilize the alternating optimization (AO) and successive convex approximation (SCA) methods to obtain an approximate problem. Then, an iteration-based algorithm framework was proposed, where the unit modulus constraint (UMC) of the IRS is handled by the penalty dual decomposition (PDD) method. Moreover, a stochastic SCA method is proposed to handle the outage constrained design with statistical CSI. Finally, simulation results validate the promising performance of the proposed design.

This paper proposes a new transmission policy for intelligent reflecting surface (IRS) empowered wireless powered internet of things systems. Particularly, an energy station (ES) wirelessly charges for multiple IoT devices during downlink wireless energy transfer (WET) and then these devices deliver their own message to an access point (AP) during uplink wireless information transfer (WIT). Also, an IRS is deployed to improve energy harvesting and data transmission capabilities. To enhance self-sustainability of the IRS, the IRS harvests energy from the ES based on the harvest-then-transmit protocol. In this paper, we maximize the sum throughput via optimizing the phase shifts of the IRS, the transfer time scheduling as well as the power splitting ratio. Due to the non-convexity of the formulated problem, we divide the problem into two sub-problems, each of which can be handled separately. Then, we adopt an alternating optimization (AO) algorithm with the semidefinite programming (SDP) relaxation. Also, we consider a special case where the circuit power consumption of IoT devices can be neglected. In this case, we derive a closed form solution for the optimal transmission time slots, power allocation and phase shift by the Lagrange dual method. Finally, numerical evaluations validate effectiveness of the proposed scheme, which significantly benefits from the IRS in improving network throughput.

Terahertz (THz) communication has been regarded as one promising technology to enhance the transmission capacity of future Internet-of-Things (IoT) users due to its ultrawide bandwidth. Nonetheless, one major obstacle that prevents the actual deployment of THz lies in its inherent huge attenuation. Intelligent reflecting surface (IRS) and multiple-input-multiple-output (MIMO) represent two effective solutions for compensating the large path loss in THz systems. In this article, we consider an IRS-aided multiuser THz MIMO system with orthogonal frequency-division multiple (OFDM) access, where the sparse radio frequency chain antenna structure is adopted for reducing the power consumption. The objective is to maximize the weighted sum rate via jointly optimizing the hybrid analog/digital beamforming at the base station (BS) and reflection matrix at the IRS. Since the analog beamforming and reflection matrix need to cater all users and subcarriers, it is difficult to directly solve the formulated problem, and thus, an alternatively iterative optimization algorithm is proposed. Specifically, the analog beamforming is designed by solving a MIMO capacity maximization problem, while the digital beamforming and reflection matrix optimization are both tackled using semidefinite relaxation (SDR) technique. Considering that obtaining perfect channel state information (CSI) is a challenging task in IRS-based systems, we further explore the case with the imperfect CSI for the channels from the IRS to users. Under this setup, we propose a robust beamforming and reflection matrix design scheme for the originally formulated nonconvex optimization problem. Finally, simulation results are presented to demonstrate the effectiveness of the proposed algorithms.

Bistatic backscatter communication is emerged as a promising technique to significantly enlarge the lifetime of Internet of Things (IoT) network due to its inherently low-power passive component. However, the effective communication range is limited to only several meters. This article studies the tag circuit shunt network, and propose three modes, namely series mode, parallel mode, and mixed mode, to adjust circuit load impedance of the tag to extend the communication range as well as address the integrated circuit (IC) power supply problem. Specifically, we formulate the bit error rate (BER) minimization problems for the three modes by changing the reflection coefficients, subject to power supply constraint. The resulting problems are shown to be nonconvex fractional optimization problems, which are hard to be solved optimally in general. We first obtain a globally optimal solution to the series mode problem by exploiting the hidden monotonic structure based on monotonic optimization theory. Subsequently, we propose a low-complexity iterative suboptimal algorithm for the three modes based on the successive convex approximation (SCA) techniques. Numerical results show that when the direct link is available, the mixed mode outperforms the parallel mode and series mode, and can adaptively adjust the reflection coefficient to satisfy the requirement of IC power supply. In contrast, when the direct link is unavailable, the series mode is the best choice in terms of IC power supply. In addition, traditional on-off keying modulation is shown to be suitable for a low IC power supply, whereas a shunt network is necessary for high of power supply. Furthermore, the performance of SCA-based method closely approaches the optimal solution while with much lower complexity.

In this article, we study a robust beamforming design for multiuser multiple-input–multiple-output secrecy networks with simultaneous wireless information and power transfer (SWIPT). In this system, an access point, multiple Internet-of-Things (IoT) devices under the nonlinear energy harvesting (EH) model with a help of one cooperative jammer (CJ). We employ artificial noise (AN) generation to facilitate efficient wireless energy transfer and secure transmission. To achieve EH fairness, we aim to maximize the minimum harvested energy among users subject to secrecy rate constraint and total transmit power constraint in the presence of channel estimation errors. By incorporating a norm-bounded channel uncertainty model, the original robust problem is transformed into a two-layer optimization problem, where the inner layer problem is reformulated as semidefinite programming (SDP) and the outer layer problem is solved by a one-dimensional (1-D) line search algorithm. In addition, in order to reduce computational complexity, we propose an algorithm based on sequential parametric convex approximation (SPCA). Finally, simulation results show that the proposed SPCA method achieves the same performance as the two-layer algorithm with much lower complexity.

This paper studies an intelligent reflecting surface (IRS) aided wireless powered sensor network (WPSN). Specifically, a power station (PS) provides wireless energy to multiple internet of thing (IoT) devices which supports them to deliver their own messages to an access point (AP). Moreover, we deploy an IRS to enhance the performance of the WPSN by intelligently adjusting the phase shift of each reflecting element. To evaluate the performance of the IRS assisted WPSN, we maximize its sum throughput to jointly optimize the phase shift matrices and the transmission time allocations. Due to the non-convexity of the formulated optimization problem, we first derive the optimal phase shifts of the wireless information transfer (WIT) in closed-form. Consequently, a semi-definite programming (SDP) relaxed approach is considered to jointly design the phase shift matrix of the wireless energy transfer (WET) and the transmission time allocations. In addition, we propose a low complexity scheme to gain insights and reduce the computational complexity incurred by the SDP relaxed scheme. Specifically, the optimal solutions of the phase shifts and the transmission time allocation are derived in closed-form by the Majorization-Minimization (MM) algorithm, the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions. Finally, numerical results are presented to validate the proposed schemes and confirm the beneficial role of the IRS in comparison to the benchmark schemes, where the proposed IRS assisted scheme achieves almost 100% higher sum throughput, in comparison to the counterpart without IRS.

This paper aims to investigate the benefit of using intelligent reflecting surface (IRS) in multi-user multiple-input single-output (MU-MISO) systems, in the presence of eavesdroppers. We maximize the weighted sum secrecy rate by jointly designing the secure beamforming (BF), the artificial noise (AN), as well as the phase shift of the IRS. An alternating optimization (AO) method is proposed to deal with the formulated non convex problem. In particular, the secure beamforming and AN jamming matrix are optimally designed via the successive convex approximation (SCA) approach for given phase shift, which can be derived by considering the alternating direction method of multiplier (ADMM) and element-wise block coordinate decent (EBCD) methods. Finally, simulation results are presented to show the benefit of the IRS in terms of improving the secrecy performance, when compared to other methods.

Massive machine-type communications (mMTC) is a new key feature of 5G cellular and is expected to be further improved in future evolutions of the cellular standards. Data collection from machine-type communication devices (MTCDs), which can be achieved by various approaches, is important to operation of mMTC networks. This work studies data collection for mMTC networks enabled by unmanned aerial vehicle (UAV) stations moving in the air. Consider the limitation in battery lifetime at both the MTCDs and the UAV station, the UAV trajectory design problem is investigated from an energy efficiency perspective. In a generalized model where the target MTCDs are grouped into multiple clusters, the UAV station travels across the clusters and collect data from each cluster while hovering above the cluster. The corresponding MTCD clustering strategy, UAV hovering strategy and UAV flying strategy all have impacts on the energy consumption of the system, which results in a strongly coupled energy minimization problem that is difficult to solve. The sub-problems obtained through decomposition are decoupled in the proposed solution approach. Clustering of the MTCDs is done by a greedy learning clustering (GLC) algorithm. A novel modeling technique based on the idea of artificial energy map (AEM) is proposed to find the optimal hovering position within a cluster. The flying strategy that minimizes the energy consumption is equivalently transformed into a classic travelling salesman problem that is readily solved by the genetic algorithm (GA). Through alternating iterative optimization of the clustering and hovering strategies, the communication energy consumption and the UAV hovering energy consumption are monotonically decreasing until convergence.

In this work, we study the buffer-aided relaying mechanism in a UAV-enabled mobile relaying system assisting the terrestrial communications. Optimal UAV trajectory design against a randomly located eavesdropper is investigated from the physical layer (PHY) security perspective considering the wireless channel dynamics as the UAV relay moves in the air. Specifically, we maximize the sum secrecy rate by optimizing the discrete trajectory anchor points based on the information causality and UAV mobility constraints. To make the non- convex problem tractable, the increments of the trajectory anchor points are optimized instead through an iterative updating procedure, and successive convex approximation technique is applied for progressive optimization. The convergence of the proposed iterative optimization technique is proved by introducing additional rate bound constraints and employing the squeeze principle. Simulation results show that the proposed optimal trajectory finding algorithm is effective and fast converging. Simulation results also reveal that the distribution of the eavesdropper location has a significant impact on the PHY security performance.

This paper investigates a secure wireless-powered integrated service system with full-duplex self-energy recycling. Specifically, an energy-constrained information transmitter (IT), powered by a power station (PS) in a wireless fashion, broadcasts two types of services to all users: a multicast service intended for all users and a confidential unicast service subscribed to by only one user while protecting it from any other unsubscribed users and an eavesdropper. Our goal is to jointly design the optimal input covariance matrices for the energy beamforming, the multicast service, the confidential unicast service, and the artificial noises from the PS and the IT, such that the secrecy-multicast rate region (SMRR) is maximized subject to the transmit power constraints. Due to the non-convexity of the SMRR maximization (SMRRM) problem, we employ a semidefinite programming-based two-level approach to solve this problem and find all of its Pareto optimal points. In addition, we extend the SMRRM problem to the imperfect channel-state information case, where a worst-case SMRRM formulation is investigated. Moreover, we exploit the optimized transmission strategies for the confidential service and energy transfer by analyzing their own rank-one profile. Finally, numerical results are provided to validate our proposed schemes.

This paper investigates a two-tier heterogeneous networks (HetNets), where millimeter wave (mmWave) frequency is employed at the macro base station (MBS), and the small cell BSs (SBSs) consider orthogonal frequency division multiple access (OFDMA). Subarray structure based hybrid analog/digital precoding scheme is studied to reduce the hardware cost and energy consumption. Our goal is to maximize the energy efficiency (EE) of the HetNets with limited wireless backhaul capacity and all users' quality of service (QoS) constraints. Due to nonconvexity of the mixed integer nonlinear fraction programming (MINLFP), the formulated problem cannot be solved directly. In order to circumvent this issue, we propose a two-loop iterative resource allocation algorithm. Specifically, we reformulate the outer-loop problem into a difference of convex programming (DCP) by employing integer relaxation and Dinkelback method. In addition, the first-order approximation is adopted to linearize this inner-loop DCP problem into a convex optimization framework. Lagrange dual method is adapted to achieve the optimal power allocation. Furthermore, the convergence of the proposed iterative algorithm is analyzed. Numerical results are presented to demonstrate our proposed algorithms.

Millimeter wave band (mmWave) integrates with a wide variety of signals under manifold communication standards due to its high-capacity feature, which enables mmWave beam sensing to serve a valuable function in discriminating different signals. In this paper, we propose a novel frame structure consisting of variant beam sensing process and data transmission process. In the beam sensing process, multi-power-level beam sensing method is conducted in every direction to discriminate multi-users under multiple standards. The sensing duration varies with the number of directions. Several performance metrics are correspondingly proposed to quantify the beam sensing for multiple mmWave users, such as the probability of correct detection and the false alarm probability. In the second process, the signal with the biggest received signal-to-noise ratio (SNR) is given priority to communicate. On this base, sensing-throughput tradeoff is analyzed to balance the time division between two processes for throughput maximization. Finally, numerical evaluations and simulations are conducted to verify the correctness of the proposed methods.

In this paper, we study a multi-user multiple-inputmultiple- output secrecy simultaneous wireless information and power transfer (SWIPT) channel which consists of one transmitter, one cooperative jammer (CJ), multiple energy receivers (potential eavesdroppers, ERs), and multiple co-located receivers (CRs). We exploit the dual of artificial noise (AN) generation for facilitating efficient wireless energy transfer and secure transmission. Our aim is to maximize the minimum harvested energy among ERs and CRs subject to secrecy rate constraints for each CR and total transmit power constraint. By incorporating norm-bounded channel uncertainty model, we propose a iterative algorithm based on sequential parametric convex approximation to find a near-optimal solution. Finally, simulation results are presented to validate the performance of the proposed algorithm outperforms that of the conventional AN-aided scheme and CJaided scheme.

Wireless energy transfer over radio frequency has been recognized as a promising alternative solution to powering the low power low complexity wireless equipments in future cellular networks. In this work, simultaneous wireless information and power transfer (SWIPT) operation for secure multi-user multipleinput multiple-output (MIMO) broadcast system is investigated with imperfect channel state information at the transmitter. The corresponding robust secure beamforming problem is studied, where the transmit power is to be minimized subject to the secrecy rate outage probability constraint for legitimate information users, and the harvested energy outage probability constraint for energy harvesting receivers. The original problem is shown to be non-convex due to the presence of the probabilistic constraints. These outage constraints are then transformed into deterministic forms by using the Bernstein-type inequalities. Based on successive convex approximation (SCA), a low-complexity approach, which reformulates the original problem as second order cone programming (SOCP), is proposed. Simulation results show that the proposed scheme outperforms the conventional method with lower complexity.

In this paper, an energy harvesting scheme for a multi-user multiple-input-multiple-output (MIMO) secrecy channel with artificial noise (AN) transmission is investigated. Joint optimization of the transmit beamforming matrix, the AN covariance matrix, and the power splitting ratio is conducted to minimize the transmit power under the target secrecy rate, the total transmit power, and the harvested energy constraints. The original problem is shown to be non-convex, which is tackled by a two-layer decomposition approach. The inner layer problem is solved through semi-definite relaxation, and the outer problem is shown to be a single-variable optimization that can be solved by one-dimensional (1-D) line search. To reduce computational complexity, a sequential parametric convex approximation (SPCA) method is proposed to find a near-optimal solution. Furthermore, tightness of the relaxation for the 1-D search method is validated by showing that the optimal solution of the relaxed problem is rank-one. Simulation results demonstrate that the proposed SPCA method achieves the same performance as the scheme based on 1-D search method but with much lower complexity.

This paper investigates a wireless powered sensor network, where multiple sensor nodes are deployed to monitor a certain external environment. A multiantenna power station (PS) provides the power to these sensor nodes during wireless energy transfer phase, and consequently the sensor nodes employ the harvested energy to transmit their own monitoring information to a fusion center during wireless information transfer (WIT) phase. The goal is to maximize the system sum throughput of the sensor network, where two different scenarios are considered, i.e., PS and the sensor nodes belong to the same or different service operator(s). For the first scenario, we propose a global optimal solution to jointly design the energy beamforming and time allocation. We further develop a closed-form solution for the proposed sum throughput maximization. For the second scenario in which the PS and the sensor nodes belong to different service operators, energy incentives are required for the PS to assist the sensor network. Specifically, the sensor network needs to pay in order to purchase the energy services released from the PS to support WIT. In this case, this paper exploits this hierarchical energy interaction, which is known as energy trading. We propose a quadratic energy trading-based Stackelberg game, linear energy trading-based Stackelberg game, and social welfare scheme, in which we derive the Stackelberg equilibrium for the formulated games, and the optimal solution for the social welfare scheme. Finally, numerical results are provided to validate the performance of our proposed schemes.

We investigate multiple‐input single‐output secured cognitive radio networks relying on simultaneous wireless information and power transfer (SWIPT), where a multiantenna secondary transmitter sends confidential information to multiple single‐antenna secondary users (SUs) in the presence of multiple single‐antenna primary users (PUs) and multiple energy‐harvesting receivers (ERs). In order to improve the security of secondary networks, we use the artificial noise (AN) to mask the transmit beamforming. Optimization design of AN‐aided transmit beamforming is studied, where the transmit power of the information signal is minimized subject to the secrecy rate constraint, the harvested energy constraint, and the total transmit power. Based on a successive convex approximation (SCA) method, we propose an iterative algorithm which reformulates the original problem as a convex problem under the perfect channel state information (CSI) case. Also, we give the convergence of the SCA‐based iterative algorithm. In addition, we extend the original problem to the imperfect CSI case with deterministic channel uncertainties. Then, we study the robust design problem for the case with norm‐bounded channel errors. Also, a robust SCA‐based iterative algorithm is proposed by adopting the ‐Procedure. Simulation results are presented to validate the performance of the proposed algorithms.

Considering simultaneous wireless information and power transfer (SWIPT), we study a multiple-input- single-output (MISO) secrecy channel which consists of a multi-antenna trans- mitter and a cooperative jammer (CJ), multiple multi-antenna energy receivers (ERs), i.e., potential eavesdroppers, and multiple single-antenna co-located receivers (CRs). Both transmitter and CJ send the intend signal with artificial noise (AN) and jamming signal to interfere with the ERs. All receivers (CRs and ERs) adopt a power splitter to decode information and harvest power simultaneously. We exploit AN and CJ to facilitate efficient wireless energy transfer and secure transmission. Our aim is to maximize the minimum harvested energy among all ERs and CRs subject to the total power constraints at the transmitter and CJ while guaranteeing the minimum secrecy rate for each CR above its requirement. By incorporating norm-bounded channel uncertainty model, we propose a joint design of robust secure transmission. The original problem is solved by a two- step approach. In the first step, the proposed problem is reformulated as a sequence of semidefinite programs (SDPs). In the second step, the proposed problem can be handled by one-dimensional search to attain the optimal solution. Simulation results indicate that the performance of the proposed scheme outperforms that of separated AN-aided or CJ-aided scheme.

For large‐scale wireless sensor networks (WSNs) with thousands of sensors, cooperative self‐localisation is a key task and has caused extensive concerns. In this study, the authors propose a message passing algorithm for cooperative self‐localisation of mobile WSNs by using belief propagation (BP) and variational message passing (VMP) on factor graphs. The sensors locate themselves through two steps: a prediction operation accounting for the sensors’ mobility and a correction operation accounting for ranging measurements between neighbouring sensors. All the messages for computing and transmitting are restricted to be Gaussian to reduce communication overhead and computational complexity. According to the linear state‐transition model and the non‐linear ranging model, BP and VMP methods are employed to perform prediction and correction, respectively. Simulation results show that when the standard deviations of the prior distributions is small, the positioning accuracy of the proposed algorithm is comparable with that of sum‐product algorithm over a wireless network (SPAWN) with much low communication overhead and computational complexity.

In this paper, an energy harvesting scheme for a multi-user multiple-input-multiple-output secrecy channel with artificial noise (AN) transmission is investigated. Joint optimization of the transmit beamforming matrix, the AN covariance matrix, and the power splitting ratio is conducted to minimize the transmit power under the target secrecy rate, the total transmit power, and the harvested energy constraints. The original problem is shown to be non-convex, which is tackled by a two-layer decomposition approach. The inner layer problem is solved through semi-definite relaxation, and the outer problem, on the other hand, is shown to be a single-variable optimization that can be solved by 1-D line search. To reduce computational complexity, a sequential parametric convex approximation method is proposed to find a near-optimal solution. This paper is then extended to the imperfect channel state information case with norm-bounded channel errors. Furthermore, tightness of the relaxation for the proposed schemes is validated by showing that the optimal solution of the relaxed problem is rank-one. Simulation results demonstrate that the proposed SPCA method achieves the same performance as the scheme based on 1-D but with much lower complexity.

In this paper, we study an energy harvesting scheme for a multiple-input-single-output secrecy channel under imperfect channel state information case with either deterministic and statistical channel uncertainties. The system consists of one multi-antenna transmitter, several multi-antenna energy receivers (ERs) and one single-antenna co-located receiver (CR) who adopts a power splitter to decode information and harvest power simultaneously. We consider the artificial noise (AN) embedded information-bearing signal to interfere potential eavesdroppers (i.e., ERs) and capture the harvested power. We perform joint optimization for the masked beamforming matrix, the AN covariance matrix and the power splitting ratio, such that the transmit power is minimized to satisfy the target secrecy rate of the CR, the total transmit power and the energy harvesting constraints for the CR and the ERs. By incorporating norm-bounded channel uncertainty model, we propose a robust joint design method to obtain the optimal solution. Also, a suboptimal algorithm for the outage constrained robust optimization problem is proposed by adopting the Bernstein-type inequality. Furthermore, the tightness of the relaxation for the proposed schemes are verified by showing that the optimal solution of the relaxed problem is rank-one. Finally, simulation results are presented to validate the performance of our proposed schemes.

In this paper, we study simultaneous wireless information and power transfer (SWIPT) for multiuser multipleinput- single-output (MISO) secrecy multicasting channels with imperfect channel state information. First, a robust secure beamfoming design is considered, where the transmit power is minimized subject to the secrecy rate outage probability constraint for legitimate users and the harvested energy outage probability constraint for energy harvesting receivers. The original problem is non-convex due to the presence of the probabilistic constraints. By utilizing Bernstein-type inequalities, we transform the outage constraints into the deterministic forms. In order to identify a local optimal rank-one solution, we propose an efficient approach based on a constrained concave convex procedure method to convert the original problem into a sequence of convex programming problems. Finally, simulation results are provided to validate the performance of our proposed design methods.

In this study, the authors consider simultaneous wireless information and power transfer (SWIPT) in a multiple‐input‐single‐output (MISO) downlink system, where power splitting scheme is considered for each user of this system. Since channel state information of each user cannot be available at the transmitter, robust beamforming for SWIPT in the MISO downlink system is presented by incorporating with different types of channel uncertainty models. The authors first formulate the robust power minimisation problem subject to the signal‐to‐inference‐plus‐noise ratio (SINR) and energy harvesting (EH) constraints by incorporating two Gaussian channel uncertainties. The original problem is not convex in terms of channel uncertainties, and cannot be solved efficiently. The authors employ the well‐known Bernstein‐type inequality and Gaussian error function to make probability based constraints tractable, respectively, in order to recast the original problem as the convex form. Moreover, the robust power minimisation problem with the probability based SINR and EH constraints is formulated by incorporating random distribution with known error mean and covariance matrix. By exploiting conditional value‐at‐risk functional and semi‐definite relaxation, this optimisation problem is relaxed as the convex form. Finally, numerical results are provided to validate the performance of these proposed robust schemes.

For localisation algorithms of wireless sensor networks (WSNs), the communication overhead and the computational complexity are two main bottlenecks that should be considered beside the positioning accuracy. In this study, the authors focus on cooperative localisation in WSNs and propose a low‐complexity distributed cooperative localisation algorithm by employing variational message passing (VMP) on factor graphs. In order to decrease the communication overhead, Gaussian parametric message representation is adopted. With regard to the non‐Gaussian messages caused by the non‐linear ranging model, they approximate them to Gaussian messages by exploiting second‐order Taylor expansion to reduce the computational complexity. Simulation results show that the proposed algorithm performs quite similar to sum‐product algorithm over a wireless network and Gaussian VMP algorithm based on minimising Kullback–Leibler divergence with lower computational complexity.

In this study, the authors study simultaneous wireless information and power transfer for multiuser multiple‐input–single‐output secure multicasting channels with imperfect channel state information. First, a robust secure beamforming design is considered, where the transmit power is minimised subject to the secrecy rate outage probability constraint for legitimate users and the harvested energy outage probability constraint for energy harvesting receivers. The original problem is non‐convex due to the presence of the probabilistic constraints. By utilising Bernstein‐type inequalities, the authors transform the outage constraints into the deterministic forms. In order to identify a local optimal rank‐one solution, the authors propose an efficient approach based on a constrained concave convex procedure method to convert the original problem into a sequence of convex programming problems. Finally, simulation results are provided to validate the performance of the proposed design methods.

In this study, the authors study the robust transmit beamforming and receive power splitting design for simultaneous wireless information and power transfer in multiuser multiple‐input–single‐output interference channel with imperfect channel‐state information at the transmitter. Following the worst‐case model, they minimise the average total transmit power subject to a set of energy harvesting constraints and signal‐to‐interference‐and‐noise ratio constraints. On the basis of the Lagrangian multiplier method, they propose a robust design method based on tight bounds that is able to achieve an approximate optimum. To reduce the complexity, they transform this original problem into a relaxed semi‐definite programming problem based on loose bounds, which can be solved efficiently. It is shown from simulation results that their proposed methods outperform the non‐robust scheme.

In this paper, we investigate simultaneous wireless information and power transfer systems for multiuser multiple-input single-output secure broadcasting channels. Considering imperfect channel state information, we introduce a robust secure beamforming design, where the transmit power is minimized subject to the secrecy rate outage probability constraint for legitimate users and the harvested energy outage probability constraint for energy harvesting receivers. The original problem is non-convex due to the presence of the probabilistic constraints. With the aid of Bernstein-type inequalities, we transform the outage constraints into the deterministic forms. Based on a successive convex approximation (SCA) method, we propose a low-complexity approach, which reformulates the original problem as a second-order cone programming problem. Also, we prove the convergence of the SCA-based iterative algorithm. Simulation shows that the proposed scheme outperforms the conventional method with lower complexity.

In this paper, we focus on maximizing weighted sum energy efficiency (EE) for a multi-cell multi-user channel. In order to solve this non-convex problem, we first decompose the original problem into a sequence of parallel subproblems which can be optimized separately. For each subproblem, a base station employs dirty paper coding to maximize the EE for users within the cell while regulating interference induced to other cells. Since each subproblem can be transformed to a convex multiple-access channel problem, the proposed method provide a closed-form power allocation. Then, based on the optimal covariance matrix, a locally optimal solution is obtained to maximize the sum EE. Finally, simulation results show that our algorithm based on the non-linear precoding achieves close to 20 percent gain than the conventional linear precoding method.

This paper studies the simultaneous wireless information and power transfer (SWIPT) in a multiuser broadcast (BC) multiple-input multiple-output (MIMO) system, in which a base station sends messages to several information decoding (ID) users, and transmits wireless power to multiple energy harvesting (EH) user at the same time. We aim to maximize the sum-rate of the ID users while maintaining a minimum EH constraint for each EH user. Firstly, an optimal rate-energy (R-E) boundary is characterized by using a BC-multiple access channel (MAC) duality derived from dirty paper coding (DPC). Since the complexity of the DPC is quite high due to continuously encoding and decoding at the transceivers, we then propose a sub- optimal algorithm using a weighted minimum mean square error (WMMSE) approach, which has lower complexity and iteratively converges a local optimal point. Finally, the performance comparisons and convergence properties are illustrated by simulation results.

We address a new robust optimization problem in a multiuser multiple-input single-output broadcasting system with simultaneous wireless information and power transmission. Assuming that perfect channel- state information (CSI) for all channels is not available at the BS, the uncertainty of the CSI is modeled by an norm-bounded uncertainty set. To optimally design transmit beamforming weights and receive power splitting, an average total transmit power minimization problem is investigated subject to the individual harvested power constraint and the received signal-to-interference-plus-noise ratio constraint at each user. The original design problem is reformulated to a relaxed semidefinite program, and then two different approaches based on convex programming are proposed, which can be solved efficiently by the interior point algorithm. Interestingly, we show that the semidefinite relaxation (SDR) is tight. Numerical results are provided to validate the robustness of the proposed algorithms.

In this paper, we investigate a multiuser downlink distributed antenna system with simultaneous wireless information and power transmission under the assumption of imperfect channel state information at the distributed antenna (DA) port. To optimally design robust transmit beamforming vectors and receive power splitting factors, our design objective is to maximize the average worst-case signal-to-interference-plus-noise ratio while simultaneously achieving the individual energy harvesting (EH) constraint for each user and the per-DA port power constraint. We solve this non- convex problem by reformulating it into a two-stage problem. Simulation results are shown to validate the robustness and effectiveness of the proposed algorithms.