When communication signals are weak, the advantages of on-board edge intelligence cannot be fully utilized. To tackle this challenge, the introduction of a key technology in the sixth-generation mobile network (6G)—reconfigurable intelligent surface that can simultaneously transmit and reflect signals (star-RIS)—is proposed. In star-RIS-enhanced intelligent transportation system (ITS), intelligent vehicles use star-RIS to upload local training models and perform global model training on the roadside unit (RSU) side. In this paper, the goal is to minimize system delay and loss function of the learning model, and comprehensively considering constraints such as system bandwidth, star-RIS phase shift, vehicle transmission power and beamforming, and vehicle selection. Firstly, for the optimization of phase shift, transmission power and beamforming caused by the introduction of star-RIS, the block coordinate descent method and Lagrange dual algorithm are used to simplify the solution. Secondly, for system delay and global model training, federated learning (FL) algorithm based on double deep Q-network (DDQN) is utilized. This approach leverages policy optimization and data privacy protection to provide an intelligent resource optimization scheme for ITS. In addition, the effectiveness of the proposed algorithm is validated through extensive simulations and numerical analyses. The results show that the algorithm enhances the adaptability and service quality of the system significantly.

As in-vehicle services grow, the increasing size of cached content prolongs wait times for users. For electric vehicles, balancing efficient communication with reduced energy consumption remains a challenge. In this paper, a vehicle clustering cooperative caching model for urban Internet of Vehicles (IoV) systems is proposed. This model decreases system energy consumption and task delay by leveraging buses as regular mobile Roadside Units (RSUs) and pre-caching nodes. It includes a kinetic energy recovery scheme for vehicles and an Energy Harvesting (EH) mechanism for RSUs, both intended to further reduce energy consumption. Terahertz (THz) technology is harnessed for Vehicle-to-Vehicle (V2V) communication to accelerate caching tasks and reduce tasks delay. To address these challenges, we propose the Deep Deterministic Policy Gradient (DDPG)-based Power Splitting (DPS) algorithm to address the needs of information transmission and energy recharging of buses while in motion. The proposed (1+1)-Evolutionary Strategy (ES)-based joint Task Decomposition and Bandwidth Allocation (TDBA) algorithm, which transmits the decomposed task file segments in parallel on different paths, reduces the additional delay and energy consumption caused by frequent task switching. Furthermore, the proposed Time-Location Preference User Rated Recommendation (TLPURR) algorithm recommends appropriate content based on the vehicle user’s time and location preferences, reducing the delay and energy consumption of obtaining content from remote cloud resources. Simulation results demonstrate that our proposed algorithms have a significant improvement in the delay and energy consumption metrics compared to other algorithms.

With its low latency, high throughput, good deployment flexibility, and cost-effectiveness, the low earth orbit (LEO) constellation is regarded as a promising technology for seamless coverage. Unlike geosynchronous orbiting (GSO) satellites, the highly dynamic evolution of LEO constellation topology poses a significant challenge to the traditional scheme of allocating wireless resources. This paper proposes a multisatellite cooperative coverage resource allocation for the LEO constellation. A multi-objective optimization problem focusing on throughput and coverage time is proposed for the overall service quality of the LEO constellation. To solve the complex coupling between multi-domain resources, the optimization problem is decomposed into three key subproblems, which are the beam placement problem, the joint beam association with power allocation problem, and the beam hopping time slot allocation problem. For each subproblem, an adaptive algorithm is proposed that effectively exploits the payload of LEO satellites to ensure the coverage performance of the constellation network. Simulation results demonstrate that the proposed coverage scheme enables fast convergence, enhances network throughput utility, and guarantees the stability and fairness of the communication services.

Magnetic field sensors based on acoustic resonators and the ΔE effect in ferromagnetic thin films enable compact size, high sensitivity, wide bandwidth, chip-scale field detection. The sensing performance of the device strongly depends on the soft magnetic and magnetostrictive properties of ferromagnetic films. Here, we systematically investigate the relationship between the magnetic field response of ΔE effect-based delay line surface acoustic wave (SAW) devices and the boron (B) doping concentration in Fe-Ga-B magnetostrictive thin films. Structural characterizations reveal a transition from a polycrystalline to a fully amorphous phase at a B content of 9.69 at. %, accompanied by a sharp reduction in both coercivity and in-plane anisotropy field. This transition leads to a step-like improvement in sensor performance. By optimizing the B composition, we identify a doping range that significantly enhances the performance of SAW devices, with a maximum phase shift of 60.81° at a B content of 12.33 at. % and a peak phase sensitivity of 5.95°/Oe at a B content of 18.84 at. %, providing a magnetic material design guideline for advancing magnetoelectric acoustic devices.

This letter primarily focuses on efficient angular estimation in low signal-to-noise ratio (SNR) conditions, as well as small-sample scenarios, aiming to facilitate the application of Integrated Sensing and Communication (ISAC) techniques. We propose a novel algorithm derived from multiple signal classification (MUSIC), called SS-MUSIC, which incorporates the sufficient statistic theorem. Simulation results demonstrate that SS-MUSIC achieves lower estimation errors in low-SNR conditions compared with existing MUSIC-based approaches, without increased complexity, and also exhibits superior performance in multi-target and small-sample scenarios.

Magnetoelectric (ME) sensors have attracted considerable attention and are utilized in diverse fields due to their high sensitivity, low cost, and ease of miniaturization. However, the reliability and the service lifetime of the mechanically resonant ME sensors are unclear, which are critical for their practical implementation. In this study, a lifetime prediction for ME sensors composed of a sandwich structure with Metglas and quartz single crystals is conducted. The failure process of the sensors is accelerated by applying high magnetic fields and high-temperature environments under resonant conditions. Combining the inverse power-law model and Arrhenius model with the Weibull distribution, the service lifetime of ME sensors under normal operating conditions could be over 40 000 h. The underlying failure mechanism is further analyzed, providing significant guidance for the lifetime assessment and the applications of ME sensors.