Simultaneous wireless information and power transfer (SWIPT) has emerged as a pivotal technology in 6G, offering an efficient means of delivering energy to a large quantity of low-power devices while transmitting data concurrently. To address the challenges of obstructions, high path loss, and significant energy consumption associated with long-distance communication, this work introduces a novel alternating iterative optimization strategy. The proposed approach combines active simultaneous transmission and reflection of reconfigurable intelligent surfaces (STAR-RIS) with SWIPT to maximize spectrum efficiency and reduce overall system energy consumption. This method addresses the considerable energy demands inherent in SWIPT systems by focusing on reducing the power output from the base station (BS) while meeting key constraints: the communication rate for information receivers (IRs) and minimum energy levels for energy receivers (ERs). Given complex interactions between variables, the solution involves an alternating iterative optimization process. In the first stage of this approach, the passive beamforming variables are kept constant, enabling the use of semi-definite relaxation (SDR) and successive convex approximation (SCA) algorithms to optimize active beamforming variables. In the next stage, with active beamforming variables fixed, penalty-based algorithms are applied to fine-tune the passive beamforming variables. This iterative process continues, alternating between active and passive beamforming optimization, until the system converges on a stable solution. The simulation results indicated that the proposed system configuration, which leverages active STAR-RIS, achieves lower energy consumption and demonstrates improved performance compared to configurations utilizing passive RIS, active RIS, and passive STAR-RIS. This evidence suggests that the proposed approach can significantly contribute to advancing energy efficiency in 6G systems.

Driven by 5G/6G and the Internet of Things (IoT), wireless sensor networks (WSNs) are confronted with core challenges such as limited energy constraints, unbalanced resource allocation, and security vulnerabilities. To address these, WSNs are integrated with cognitive radio networks (CRNs) to alleviate spectrum scarcity, and reconfigurable intelligent surfaces (RIS) are adopted to enhance performance, but traditional passive RIS suffers from “double fading” (signal path loss from transmitter to RIS and RIS to receiver), which undermines WSNs’ energy efficiency and the physical layer security (PLS) (e.g., secrecy rate, SR) of primary users (PUs) in CRNs. This study leverages symmetry to develop an active RIS framework for WSN-integrated CRNs, constructing a tripartite collaborative model where symmetric beamforming and resource allocation improve WSN connectivity, reduce energy consumption, and strengthen PLS. Specifically, three symmetry types—resource allocation symmetry, beamforming structure symmetry, and RIS reflection matrix symmetry—are formalized mathematically. These symmetries reduce the degrees of freedom in optimization (e.g., cutting precoding complexity by ~50%) and enhance the directionality of green interference, while ensuring balanced resource use for WSN nodes. The core objective is to minimize total transmit power while satisfying constraints of PU SR, secondary user (SU) quality-of-service (QoS), and PU interference temperature, achieved by converting non-convex SR constraints into solvable second-order cone (SOC) forms and using an alternating optimization algorithm to iteratively refine CBS/PBS precoding matrices and active RIS reflection matrices, with active RIS generating directional “green interference” to suppress eavesdroppers without artificial noise, avoiding redundant energy use. Simulations validate its adaptability to WSN scenarios: 50% lower transmit power than RIS-free schemes (with four CBS antennas), 37.5–40% power savings as active RIS elements increase to 60, and a 40% lower power growth slope in multi-user WSN scenarios, providing a symmetry-aided, low-power solution for secure and efficient WSN-integrated CRNs to advance intelligent WSNs.