In this paper, we investigate a wireless powered communication network (WPCN) in which a multi-antenna hybrid access point (HAP) communicates with multiple Internet-of-Things (IoT) devices, assisted by an active simultaneously transmitting and reflecting reconfigurable intelligent surface (aSTAR-RIS). In the energy transfer (ET) phase, the IoT devices harvest energy from the HAP with a nonlinear energy harvesting (EH) model, and subsequently transmit information signals to the HAP during the information transmission (IT) phase. To explore its full potential, the aSTAR-RIS employs energy splitting (ES), mode switching (MS), and time switching (TS) protocols. A sum rate maximization problem is formulated for each protocol, which jointly optimize the beamforming at the HAP, allocation of time slots and transmitting power for the IoT devices, and the adaptation of the aSTAR-RIS coefficients. To address the optimization problem with multiple coupled variables and complex non-convex constraints, we firstly decompose it into several subproblems. Specifically, to optimize the coefficients of the aSTAR-RIS in the IT phase, we develop a fractional programming-based successive convex approximation algorithm to handle the fractional objective function and the minimum rate constraints. Moreover, to obtain the coefficients of the aSTAR-RIS during the ET phase, we design a penalty-based SCA algorithm to address the binary constraints in the MS protocol and the rank-one constraints. Numerical results demonstrate that 1) employing the aSTAR-RIS in WPCNs can realize the extraordinary sum rate gain in comparison with the benchmarks of the active RIS and the passive STAR-RIS; 2) among the three operation protocols, the ES demonstrates the best performance, with the MS following closely behind, while the TS is the least effective; 3) as the minimum required data rate for each IoT device decreases, the performance gap among the three protocols becomes narrower.

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.

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.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) enhanced simultaneous wireless information and power transfer (SWIPT) system is investigated, in which a multi-antenna access point (AP) provides communication service and power supply for multiple single-antenna information decoding receivers (IDRs) and energy harvesting receivers (EHRs), respectively. To fully investigate the potential of STAR-RIS, three types of STAR-RIS protocols are employed in the SWIPT systems, namely the energy splitting (ES), the mode switching (MS), and the time switching (TS) protocols. The weighted sum power maximization problem is formulated for each STAR-RIS protocol to maximize the sum power received at the EHRs by jointly optimizing the beamforming at the AP and STAR-RIS. The non-convex problem for each STAR-RIS protocol is handled by the proposed low-complexity Gaussian randomization-based and high-precision penalty-based joint optimization algorithms to provide a more comprehensive range of algorithmic choices. Furthermore, we demonstrated that the AP only needs to send the information beam to achieve the best power supply and communication service in adopting any STAR-RIS protocol. Finally, numerical results demonstrate that: 1) the proposed schemes can achieve higher sum received power compared to the benchmarks; 2) the sum power received at the EHRs of the three STAR-RIS protocols can be ranked as ES > TS > MS; and 3) the performance gap in sum power received between penalty-based and Gaussian random algorithms depends on the number of STAR-RIS elements.

In this paper, we study a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) supported simultaneous wireless information and power transfer (SWIPT) system, in which a multi-antenna access point (AP) provides power supply and communication services for multiple energy harvesting receivers (EHRs) and information decoding receivers (IDRs) respectively. To maximize the performance of the STAR-RIS, we utilize the energy splitting (ES) operation protocol in this paper. By optimizing the AP beamforming and the STAR-RIS transmission and reflection coefficients, we create a weighted sum-power maximization problem. We demonstrate that the AP can service both the IDRs and the EHRs by merely delivering information signals, which simplifies the initial optimization challenge. Then, the initial problem is split into two subproblems, and an alternative optimization method is utilized to produce high-precision suboptimal solutions to the optimization problem. Finally, we testify that the sum power received by EHRs is remarkably enhanced by the STAR-RIS-aided SWIPT systems compared with benchmark schemes in numerical results.

Abstract In order to improve the performance of the mobile edge computing (MEC) system, the intelligent reflecting surface (IRS) has recently been included. This paper investigates the computation performance of an IRS‐aided MEC system, where an access point services multi‐MEC devices by utilizing two distributed IRSs to operate a partial offloading strategy. Through collaboratively constructing the cooperative passive beamforming at the two IRSs, the central processing unit (CPU) frequency, the offloading time allocation, and the transmit power of users, an optimization problem is constructed to maximize the sum computation rate of the proposed system. To tackle this non‐convex issue, the authors first illustrate that the design of passive beamforming is independent to optimize other variables and propose to solve it alternatively. Then the joint optimization problem of other parameters is proved to be a convex problem and is resolvable by adopting the Lagrange dual approach. Compared with the benchmark schemes, the simulation outcomes demonstrated that the performance of the proposed system is vastly improved by deploying two distributed IRSs.