Network energy resources are limited in communication systems, which may cause energy shortages in mobile devices at the user end. Active Reconfigurable Intelligent Surfaces (A-RIS) not only have phase modulation properties but also enhance the signal strength; thus, they are expected to solve the energy shortage problem experience at the user end in 6G communications. In this paper, a resource allocation algorithm for maximizing the sum of harvested energy is proposed for an active RIS-assisted Simultaneous Wireless Information and Power Transfer (SWIPT) system to solve the problem of low performance of harvested energy for users due to multiplicative fading. First, in the active RIS-assisted SWIPT system using a power splitting architecture to achieve information and energy co-transmission, the joint resource allocation problem is constructed with the objective function of maximizing the sum of the collected energy of all users, under the constraints of signal-to-noise ratio, active RIS and base station transmit power, and power splitting factors. Second, the considered non-convex problem can be turned into a standard convex problem by using alternating optimization, semi-definite relaxation, successive convex approximation, penalty function, etc., and then an alternating iterative algorithm for harvesting energy is proposed. The proposed algorithm splits the problem into two sub-problems and then performs iterative optimization separately, and then the whole is alternately optimized to obtain the optimal solution. Simulation results show that the proposed algorithm improves the performance by 45.2% and 103.7% compared to the passive RIS algorithm and the traditional without-RIS algorithm, respectively, at the maximum permissible transmitting power of 45 dBm at the base station.

With the rapid development of wireless communication networks and Internet of Things technology (IoT), higher requirements have been put forward for spectrum resource utilization and system performance. In order to further improve the utilization of spectrum resources and system performance, this paper proposes an intelligent reflecting surface (IRS)-assisted fair energy allocation algorithm for cognitive multiple-input single-output (MISO) wireless-powered networks. The goal of this paper is to maximize the minimum energy receiving power in the energy receiver, which is constrained by the signal-to-interference-plus-noise ratio (SINR) threshold of the information receiver in the secondary network, the maximum transmission power at the cognitive base station (CBS), and the interference power threshold of the secondary network on the main network. Due to the coupling between variables, this paper uses iterative optimization algorithms to optimize and solve different variables. That is, when solving the active beamforming variables, the passive beamforming variables are fixed; then, the obtained active beamforming variables are fixed, and the passive beamforming variables are solved. Through continuous iterative optimization, the system converges. The simulation results have verified the effectiveness of the proposed algorithm.

This study reveals for the first time the influence mechanism of the dual-layer structure in the native passivation film on the dielectric breakdown of Ta₂O₅, providing critical insights for enhancing the reliability of tantalum capacitors.

This paper presents a multi-modal robust integrated navigation framework for ring laser gyro-based inertial navigation systems and Global Navigation Satellite Systems, addressing signal denial, non-Gaussian heavy-tailed noise, and sensor soft faults in complex dynamic environments. The proposed method establishes a multi-source information quality assessment system that enables intelligent dynamic switching among four navigation modes through fuzzy comprehensive evaluation with a hysteresis comparison strategy. During GNSS signal outages, a scaled dot-product self-attention-enhanced Long Short-Term Memory network is introduced to simultaneously predict position and velocity increments, with the prediction covariance dynamically inflated via a time-decay factor and injected into the measurement noise matrix for effective uncertainty propagation control. At the filtering fusion level, an adaptive maximum correntropy unscented Kalman filter is proposed, whose kernel bandwidth is autonomously regulated by GNSS confidence scores, achieving an optimal balance between estimation accuracy and robustness. For fault detection, an adaptive fading factor constructed from innovation sequence statistics is embedded into the sequential probability ratio test likelihood ratio recursion, forming a dual-layer detection architecture with residual chi-square detection for rapid and accurate soft fault isolation. Simulation results demonstrate that the proposed method achieves horizontal positioning errors below 0.08 m (2D, relative accuracy) in GNSS-favorable regions, maintains horizontal errors within 2.0 m during 60-second complete signal outages, and attains soft fault detection rates exceeding 95% with false alarm rates below 5%. The method significantly enhances the adaptability, accuracy, and reliability of integrated navigation systems in complex environments.

The high peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signals poses a significant challenge for power-limited satellite transponders, leading to power amplifier nonlinearity and reduced system efficiency. This paper proposes a novel joint algorithm named Polar-SLM-CPM for efficient PAPR suppression in satellite coded OFDM (COFDM) systems. The core of this scheme is a deeply integrated design that synergistically combines polar coding, intelligent selective mapping (SLM), and adaptive continuous phase modulation (CPM). Unlike conventional approaches that treat these components separately, our method leverages the constant-envelope property of CPM for inherent PAPR limitation, employs a gradient-learning-optimized intelligent SLM mechanism for efficient low-PAPR sequence search, and utilizes capacity-approaching polar codes to guarantee transmission reliability. The synergistic operation is mathematically modeled and extensively evaluated via MATLAB simulations. Results demonstrate that the proposed algorithm achieves a substantial PAPR reduction of approximately 4.2 dB at a complementary cumulative distribution function (CCDF) of ${10}^{-3}$ while maintaining bit error rate (BER) performance comparable to conventional polar-coded OFDM under additive white Gaussian noise (AWGN) channels. Further analyses on synchronization, computational complexity (Big-O), parameter sensitivity, spectral efficiency trade-offs, and robustness in realistic nonlinear/phase-noise channels are provided, confirming the scheme’s practical viability. This work presents a balanced and effective solution for enhancing the power efficiency and signal integrity of next-generation integrated satellite communication and navigation systems employing COFDM-CPM waveforms.