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.

Integrated sensing and communication (ISAC) is a key technology for improving spectrum efficiency and enabling intelligent wireless networks, yet its deployment in short-packet transmission scenarios faces significant challenges such as finite block-length (FBL) effects, channel estimation uncertainty, and limited coverage. To address these issues, this paper investigates a short-packet ISAC system assisted by a hybrid simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and proposes a two-stage ISAC transmission paradigm. In Stage I, the hybrid STAR-RIS performs target direction-of-arrival estimation, and a closed-form expression of Cramér–Rao Bound (CRB) is derived to establish channel state information (CSI) uncertainty model based on CRB. Meanwhile, each user performs channel estimation locally and feeds results back to DFBS. In Stage II, the estimated CSI is utilized to jointly design resource allocation, and an optimization problem is formulated to maximize target illumination power under FBL and CSI uncertainty constraints. To tackle this strongly coupled non-convex problem, we develop a hierarchical solution strategy: the sensing duration is first determined via one-dimensional search, and then, an alternating optimization framework is employed to decouple the problem into DFBS beamforming and hybrid STAR-RIS coefficient optimization, where iterative algorithms based on semi-definite relaxation, semi-definite programming, and singular value decomposition are proposed to ultimately obtain a convergent optimal solution. Simulation results validate the fast convergence and superior performance of our proposed algorithm, reveal the inherent trade-off between the two stages under constrained resources, and demonstrate the importance of joint two-stage resource design assisted by hybrid STAR-RIS in enhancing short-packet ISAC system performance.

In this paper, we design a novel two-stage transmission framework in millimeter wave-ISAC systems with multiple communication users (CUs) and multiple target scenarios. In stage I, the dual-functional base station (DFBS) performs beam scanning with pilot signals, estimating target direction of arrival angles (DoAs) through the maximum likelihood estimation and multiple signal classification techniques, while the CU estimate DoAs via minimum mean square error and MUSIC techniques. Further, we derive the closed-form Cramér-Rao Bound (CRB) expressions for estimated CU/target DoAs and establish the relationship between channel station information (CSI) error and CRB. In stage II, the DFBS transmits ISAC signals and maximizes the minimum effective signal-to-interference-plus-noise ratio (SINR) of CU by jointly optimizing two stage resources, while meeting sensing performance requirements and accounting for the impact of imperfect CSI. Since the complex interactions and strong coupling among variables, the formulated problem is non-convex and difficult to be solved directly. To address this issue, we begin by employing one-dimensional search to determine the sensing duration of Stage I. Then, based on this result, the DFBS beamforming optimization design is carried out with S-procedure method, penalty-based and successive convex approximation algorithms to convert the original problem into a tractable convex optimization problem. Finally, simulations are executed to confirm the advantages and effectiveness of our developed scheme.

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, we consider the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted THz communications with three-side beam split. Except for the beam split at the base station (BS), we analyze the double-side beam split at the STAR-RIS for the first time. To relieve the double-side beam split effect, we first propose a time delayer (TD)-based fully-connected structure at the STAR-RIS. As a further advance, a low-hardware complexity and low-power consumption sub-connected structure is developed, where multiple STAR-RIS elements share one TD. Meanwhile, considering the practical scenario, we investigate a multi-STAR-RIS and multi-user communication system, and sum rate maximization problem is formulated by jointly optimizing the hybrid analog/digital beamforming, time delays at the BS as well as the double-layer phase shift coefficients, time delays and amplitude coefficients at the STAR-RISs. Based on this, we first allocate users for each STAR-RIS, and then derive the analog beamforming, time delays at the BS, and the double-layer phase shift coefficients, time delays at each STAR-RIS. Next, we develop an alternative optimization algorithm to calculate the digital beamforming at the BS and amplitude coefficients at the STAR-RISs. Finally, the numerical results verify the effectiveness of the proposed schemes.

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 investigate the energy efficiency (EE) problem under reconfigurable intelligent surface (RIS)-aided secure cell-free networks, where multiple legitimate users and eavesdroppers (Eves) exist. We formulate a max-min security EE optimization problem by jointly designing the distributed active beamforming and artificial noise at base stations as well as the passive beamforming at RISs under practical constraints. To deal with it, we first divide the original optimization problem into two sub-ones, and then propose an iterative optimization algorithm to solve each sub-problem based on the fractional programming, constrained concave-convex procedure (CCCP) and semi-definite programming (SDP) techniques. After that, these two sub-problems are alternatively solved until convergence, and the final solutions are obtained. Next, we extend to the imperfect channel state information of the Eves’ links, and investigate the robust security EE beamforming optimization problem by bringing the outage probability constraints. Based on this, we first transform the uncertain outage probability constraints into the certain ones by the Bernstein-type inequality and sphere boundary techniques, and then propose an alternatively iterative algorithm to obtain the solutions of the original problem based on the S-procedure, successive convex approximation, CCCP, and SDP techniques. Finally, the simulation results are conducted to show the effectiveness of the proposed schemes.

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, we investigate the energy efficiency (EE) problem in downlink reconfigurable intelligent surface (RIS)-aided secure cell-free networks. First, we formulate a max-min secure EE (SEE) optimization problem via jointly optimizing the distributed beamforming at base stations and phase shifts at RISs under the constraint of each base station transmit power. To deal with it, we divide the original optimization problem into two sub-ones and propose an alternative scheme. Specifically, we develop an iterative optimization algorithm to solve each sub-one based on the fractional programming, constrained convex-convex procedure and semi-definite programming techniques. After that, these two sub-ones are alternatively solved until convergence, and then the final solutions are obtained. Finally, the simulation results show the effectiveness of the proposed algorithm.

Reconfigurable intelligent surface (RIS)-aided terahertz (THz) communications have been regarded as a promising candidate for future 6G networks because of its ultra-wide bandwidth and ultra-low power consumption. However, there exists the beam split problem, especially when the base station (BS) or RIS owns the large-scale antennas, which may lead to serious array gain loss. Therefore, in this paper, we investigate the beam split and beamforming design problems in the THz RIS communications. Specifically, we first analyze the beam split effect caused by different RIS sizes, shapes and deployments. On this basis, we apply the fully connected time delayer phase shifter hybrid beamforming (FC-TD-PS-HB) architecture at the BS and deploy distributed RISs to cooperatively mitigate the beam split effect. We aim to maximize the achievable sum rate by jointly optimizing the hybrid analog/digital beamforming, time delays at the BS and reflection coefficients at the RISs. To solve the formulated problem, we first design the analog beamforming and time delays based on different RISs’ physical directions, and then it is transformed into an optimization problem by jointly optimizing the digital beamforming and reflection coefficients. Next, we propose an alternatively iterative optimization algorithm to deal with it. Specifically, for given the reflection coefficients, we propose an iterative algorithm based on the minimum mean square error technique to obtain the digital beamforming. After, we apply Lagrangian dual reformulation (LDR) and multidimensional complex quadratic transform (MCQT) methods to transform the original problem to a quadratically constrained quadratic program, which can be solved by alternating direction method of multipliers (ADMM) technique to obtain the reflection coefficients. Finally, the digital beamforming and reflection coefficients are obtained via repeating the above processes until convergence. Simulation results verify that the proposed scheme can effectively alleviate the beam split effect and improve the system capacity.

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.

In this paper, we investigate the physical layer security in the reconfigurable intelligent surface (RIS)-aided cell-free networks. A maximum weighted sum secrecy rate problem is formulated by jointly optimizing the active beamforming (BF) at the base stations and passive BF at the RISs. To handle this non-trivial problem, we adopt the alternating optimization to decouple the original problem into two sub-ones, which are solved using the semidefinite relaxation and continuous convex approximation theory. To decrease the complexity for obtaining overall channel state information (CSI), we extend the proposed framework to the case that only requires part of the RIS’ CSI. This is achieved via deliberately discarding the RIS that has a small contribution to the user’s secrecy rate. Based on this, we formulate a mixed integer non-linear programming problem, and the linear conic relaxation is used to obtained the solutions. Meanwhile, we also study the system performance under the imperfect CSI. Finally, the simulation results show that the proposed schemes can obtain a higher secrecy rate than the existing ones.

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.

In this article, we consider an edge cache-assisted millimeter-wave cloud radio access network (C-RAN). Each remote radio head (RRH) in the C-RAN has a local cache, which can prefetch and store the files requested by the actuators. Multiple RRHs form a cluster to cooperatively serve the actuators, which acquire their required files either from the local caches or from the central processor via multicast fronthaul links. For such a scenario, we formulate a beamforming design problem to minimize the secure transmission delay under transmit power constraint of each RRH. Due to the difficulty of directly solving the formulated problem, we divide it into two independent ones: 1) minimizing the fronthaul transmission delay by jointly optimizing the transmit and receive beamforming and 2) minimizing the maximum access transmission delay by jointly designing cooperative beamforming among RRHs. An alternatively iterative algorithm is proposed to solve the first optimization problem. For the latter, we first design the analog beamforming based on the channel state information of the actuators. Then, with the aid of successive convex approximation and $S$ -procedure techniques, a semidefinite program (SDP) is formulated, and an iterative algorithm is proposed through SDP relaxation. Finally, the simulation results are provided to verify the performance of the proposed schemes.

In this paper, we investigate the downlink secure beamforming (BF) design problem of cloud radio access networks (C-RANs) relying on multicast fronthaul, where millimeter-wave and microwave carriers are used for the access links and fronthaul links, respectively. The base stations (BSs) jointly serve users through cooperating hybrid analog/digital BF. We first develop an analog BF for cooperating BSs. On this basis, we formulate a secrecy rate maximization (SRM) problem subject both to a realistic limited fronthaul capacity and to the total BS transmit power constraint. Due to the intractability of the non-convex problem formulated, advanced convex approximated techniques, constrained concave convex procedures and semi-definite programming (SDP) relaxation are applied to transform it into a convex one. Subsequently, an iterative algorithm of jointly optimizing multicast BF, cooperative digital BF and the artificial noise (AN) covariance is proposed. Next, we construct the solution of the original problem by exploiting both the primal and the dual optimal solution of the SDP-relaxed problem. Furthermore, a per-BS transmit power constraint is considered, necessitating the reformulation of the SRM problem, which can be solved by an efficient iterative algorithm. We then eliminate the idealized simplifying assumption of having perfect channel state information (CSI) for the eavesdropper links and invoke realistic imperfect CSI. Furthermore, a worst-case SRM problem is investigated. Finally, by combining the so-called S-Procedure and convex approximated techniques, we design an efficient iterative algorithm to solve it. Simulation results are presented to evaluate the secrecy rate and demonstrate the effectiveness of the proposed algorithms.

The number of wireless devices is growing rapidly on a daily basis echoing the increasing number of applications of the Internet of Thing. Facing massive connections and unavoidable interference, how to provide a green communication is a concerning matter. In this regard, nonorthogonal multiple-access (NOMA) is a natural communications technology that can scale with the massive number of simultaneous connections for a limited bandwidth. In this paper, we aim to maximize the energy efficiency (EE) for an NOMA-based cloud radio access network, where sub-6 GHz and millimeter wave bands are used in fronthaul and access links, respectively. In particular, we formulate the power optimization problem to maximize the EE of the system subject to the fronthaul capacity and transmit power constraints. To address this nonconvex problem, we first convert the fractional objective function into a subtractive form. A two-layer algorithm is then proposed. In the outer loop, the ℓ1-norm technique is adopted to transform the nonconvex fronthaul capacity constraint into a convex one, whereas in the inner loop, the weighted minimum mean square error approach is applied. Simulation results indicate that the proposed NOMA scheme can obtain higher EE as well as throughput when compared with orthogonal multiple-access methods.

In this paper, we investigate the energy-efficient resource allocation problem in an uplink non-orthogonal multiple access (NOMA) millimeter wave system, where the fully-connected-based sparse radio frequency chain antenna structure is applied at the base station (BS). To relieve the pilot overhead for channel estimation, we propose a codebook-based analog beam design scheme, which only requires to obtain the equivalent channel gain. On this basis, users belonging to the same analog beam are served via NOMA. Meanwhile, an advanced NOMA decoding scheme is proposed by exploiting the global information available at the BS. Under predefined minimum rate and maximum transmit power constraints for each user, we formulate a max-min user energy efficiency (EE) optimization problem by jointly optimizing the detection matrix at the BS and transmit power at the users. We first transform the original fractional objective function into a subtractive one. Then, we propose a two-loop iterative algorithm to solve the reformulated problem. Specifically, the inner loop updates the detection matrix and transmit power iteratively, while the outer loop adopts the bi-section method. Meanwhile, to decrease the complexity of the inner loop, we propose a zero-forcing (ZF)-based iterative algorithm, where the detection matrix is designed via the ZF technique. Finally, simulation results show that the proposed schemes obtain a better performance in terms of spectral efficiency and EE than the conventional schemes.