The integration of reconfigurable intelligent surfaces (RIS) and simultaneous wireless information and power transfer (SWIPT) present a promising solution for sustainable and efficient wireless communications in large-scale IoT networks, especially within energy-constrained smart agriculture applications. However, most existing works assume ideal energy harvesting (EH) models and continuous RIS phase shifts that limit their practical relevance. This paper addresses these limitations by proposing a total transmit power minimization framework for a multi-user RIS-assisted MISO power-splitting (PS) SWIPT system. First, a practical logistic non-linear energy harvesting (NL-EH) model is adopted to better reflect the realistic behavior of RF energy conversion circuits. Second, a discrete phase shift (DPS) model with finite quantization levels is employed to account for practical RIS hardware constraints. Third, an alternating optimization algorithm is developed for the resulting non-convex optimization problem through joint optimization of the base station’s beamforming vectors, RIS reflection matrix, and PS ratio. Techniques such as Zero-Forcing (ZF), Semidefinite Relaxation (SDR), and Gaussian randomization (GR) are leveraged to address the associated sub-problems, while an alternate one-dimensional search strategy is used for RIS phase shift optimization. Finally, numerical simulations are conducted to validate the proposed framework in terms of performance and convergence. The results demonstrate robustness under imperfect channel state information (ICSI) and varying system parameters for next-generation IoT-enabled smart farming use-case.

This paper investigates the effectiveness of employing multiple reconfigurable intelligent surfaces (RIS) for simultaneous wireless information and power transfer (SWIPT) in a vehicle-to-infrastructure (V2I) system. The optimal RIS is selected for transmission based on instantaneous signal-to-noise ratio (SNR) values, with the objective of optimizing the SWIPT system employing the power-splitting (PS) protocol and nonlinear energy harvesting (NL-EH). A unified objective is proposed to maximize information rate and harvested energy via joint optimization of transmit power and power splitting factor. Nonconvexity is addressed via an iterative algorithm, supported by closed-form expressions obtained through Karush-Kuhn-Tucker (KKT) conditions. Monte-Carlo simulations are performed to validate the accuracy of the analytical expressions. Additionally, a deep neural network (DNN) framework is introduced for realtime optimization prediction, achieving superior SWIPT performance over single RIS configurations with reduced complexity and faster execution.

The conventional reflection-only Reconfigurable Intelligent Surface (RIS) extends its 180-degrees coverage to 360-degrees with Simultaneously Transmitting and Reflecting-RIS(STAR-RIS). When integrated with the Simultaneous Wireless Information and Power Transfer (SWIPT) system, it enhances performance by directing energy and information signals to energy receiver (ER) and information receiver (IR), with Energy Splitting (ES) and Mode Switching (MS) operational protocol. Considering a practical system with discrete phase shift (DPS) model and non-linear energy harvesting (EH) model, we aim to maximize the weighted rate-energy (WRE) function, with certain QoS constraints by optimizing power parameters. An alternating optimizing (AO) algorithm solves the complicated WRE maximization problem. Numerical results highlight the impact of energy split ratio and element division on the performance, with the critical selection of DPS level (quantization bits) balancing device bulkiness (phase control bits) and system efficiency.

Distributed Reconfigurable Intelligent Surfaces (D-RIS) in a Simultaneous Wireless Information and Power Transfer (SWIPT) framework is investigated in this paper to enhance spectral efficiency (SE) and energy harvesting (EH) for an Internet-of-Things (IoT) node utilizing a power-splitting (PS) protocol with non-linear EH model. Two types of RIS selection strategies, namely Exhaustive RIS Approach (ERA) and Optimal RIS Approach (ORA) are analyzed and compared. Algorithmic solutions based on divide-and-conquer strategy are developed to optimize the PS ratio and transmission power, ensuring improvements in distinct objective of SE and EH. The analysis is performed through a comprehensive case study of five different scenarios, focusing on various factors such as RIS surface placement, node-to-RIS distance, RIS size, and their combined impact on system performance. Numerical simulations provide insights into the system's behavior under different configurations, revealing that the ERA strategy consistently outperforms the ORA in achieving better SE and EH outcomes. These results are viable in designing IoT systems using distributed RISs in data and energy domains.

This paper addresses the challenge of balancing conflicting goals, namely data rate and energy harvesting (EH) in Simultaneous Wireless Information and Power Transfer (SWIPT) systems, while incorporating Reconfigurable Intelligent Surface (RIS) technology. We formulate a weighted optimization objective to address this issue, seeking to simultaneously maximize data rate, EH, and minimize transmit power utilization. The proposed approach involves optimizing time switching (TS) ratios and transmit power using a practical phase-dependent amplitude model for each RIS element’s reflectivity. To address this complex optimization problem involving ratio of concave-convex problem, the paper introduces fractional programming-based modified Dinkelbach Algorithm providing upper and lower bounds, which are then compared with Quadratic transform-related algorithms and solutions based on Karush-Kuhn-Tucker (KKT) conditions. Numerical findings highlight the effectiveness of the proposed algorithms in enhancing the overall performance of SWIPT systems with RIS technology.

Future generation of Wireless Communications encompasses massive connectivity of energy-starved and heavy-data driven billions of Internet-of-Things (IoT) devices. In this vein, Reconfigurable Intelligent Surface (RIS) holds great promise while providing improved performance and efficiency in terms of energy, cost and spectrum. Simultaneous Wireless Information and Power Transmission (SWIPT) in conjunction with RIS makes a great partnership to suffice the IoT demands. This paper examines a SWIPT-IoT system that utilizes power-splitting (PS) and non-linear energy harvesting (EH) model to achieve more data rates in constraint environment. The IoT node receives both energy and information from the base station via RIS. We present a combined problem that aims to optimize the individual objectives of rate, EH, and transmit power, while taking into account various sets of quality-of-service (QoS)-based constraints. We introduce a set of iterative optimization algorithm that utilize a divide-and-conquer approach to effectively solve the aforementioned problems. Based on our computational results, we confer that in order to reap the benefits of PS-based SWIPT-IoTs, it is imperative to increase the size of RIS and position them in optimal proximity to both the base station and the user.