Ruoyan Ma

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

Integrated sensing and communication (ISAC) is a promising technique for establishing an effective Internet of Things (IoT) network, utilizing the same hardware and resource block for both sensing and communication (S&C) functions. In this study, we propose a hybrid demodulation scheme-enabled ISAC beamforming design to enhance sensing capabilities. Our study delves into the combined effects of diverse users’ demodulation schemes and sensing power constraints on the design of transmit beamforming for multiuser communication and multitarget sensing (MUMT). We formulate three optimization problems for beamforming design, which tackle user–target discrimination, user–target overlap, and a general hybrid scenario. In particular, in the hybrid scenario, we incorporate the type of communication receivers as an optimized variable and introduce additional sensing power constraints to enhance sensing performance in terms of the beampattern and time waveform without compromising communication performance. To effectively tackle these optimization challenges, we introduce a hierarchical convex relaxation framework considering user–target pairing. Simulation results illustrate that the proposed beamforming approach improves sensing performance while maintaining communication performance, surpassing traditional designs under different user–target directional distributions.

A symbiotic radio (SR) network is built to address the situations of limited spectrum and energy resources. Particularly, a hybrid SR network is proposed based on simultaneous wireless information and power transfer (SWIPT). Specifically, the backscatter devices (BDs) will have the capability to provide active communications for the primary receiver (PR) with the harvested energy from SWIPT. Within the network, the BDs and primary transmitter (PT) based transmissions will achieve symbiotic status. In detail, the whole network will operate in three phases, which are assisted by a reconfigurable intelligent surface (RIS). Further, an end-to-end S-parameter based model is introduced to reveal the hardware characteristics. Under these settings, a multi-objective problem with energy efficiency (EE) and sum rate maximization is proposed. Additionally, the constraints are related to the communication demands and physical restrictions. To resolve the problem, the -constraint approach is utilized to transform it into a single-objective problem. Then the coupled variables are separated into three parts to allow an iterative solution. Specifically, an element-wise approach based on the Sherman-Morrison transformation is adopted to optimize the subproblem for RIS with the double-inverse form. Simulation results confirm the significance of the hardware features and the effectiveness of the proposed scheme.

In the Internet of Everything (IoE) scenarios, the extensive deployment of devices may result in more stringent power and communication needs. Within this context, we utilize the reconfigurable intelligent surface (RIS) to support the simultaneous wireless information and power transfer (SWIPT) system, whereby the stable transmission of energy and information services can be guaranteed. Specifically, we construct the system model through electromagnetics (EM), which is based on the scattering-parameter (S-parameter) analysis, for revealing the crucial factors of the practical hardware. Relying on the model, the energy-efficient (EE) maximization problem constrained to the quality of services (QoS) is proposed for the users with the framework of co-located receiver (Rx). However, the problem is more intractable due to the introduced channel model. To resolve it, we propose an effective optimization scheme. First, the Neuman series approximation method is adopted to deconstruct the EM transfer model. Then the reformed problem, which includes the variables (i.e., the PS ratio, the active beamformer, and the reflection-coefficient matrix), can be addressed through the strategy of alternative optimization (AO). Further, the inner convex approximation (INCA) scheme and Dinkelbach’s algorithm are applied to tackle each sub-problem. In the numerical simulation, we demonstrate that the array configuration can influence not only the hardware properties of RIS but also the EE performance of the whole system. What’s more, the proposed scheme performs better for the tightly-coupled RIS owing to the awareness of the mutual-coupling (MC) effect.

The widespread deployment of the Internet of Things (IoT) is promoting interest in simultaneous wireless information and power transfer (SWIPT), the performance of which can be further improved by employing a reconfigurable intelligent surface (RIS). In this article, we propose a novel RIS-enhanced SWIPT system built on an electromagnetic-compliant framework. The mutual-coupling effects in the whole system are presented explicitly. Moreover, the reconfigurability of RIS is no longer expressed by the reflection-coefficient matrix but by the impedances of the tunable circuit. For comparison, both the no-coupling and the coupling-awareness cases are discussed. In particular, the energy efficiency (EE) is maximized by cooperatively optimizing the impedance parameters of the RIS elements as well as the active beamforming vectors at the base station (BS). For the coupling-awareness case, the considered problem is split into several subproblems and solved alternatively due to its nonconvexity. First, it is transformed into a more solvable form by applying the Neuman series approximation, which can be resolved iteratively. Then, an alternative optimization (AO) framework and semidefinite relaxation (SDR), successive convex approximation (SCA), and Dinkelbach’s algorithm are applied to solve each subproblem decomposed from it. Owning to the similarity between the two cases, the no-coupling one can be viewed as a reduced form of the coupling case and, thus, solved through a similar approach. Numerical results reveal the influence of mutual-coupling effects on the EE, especially in the RIS with closely spaced elements. In addition, physical beam designs are presented to demonstrate how the RIS assists SWIPT through various reflecting states in different conditions.