Anna Li

The upcoming sixth-generation (6G) is envisioned to transform wireless communications through groundbreaking performance targets that far exceed previous generations. Among the emerging technologies, the fluid antenna system (FAS) has gained significant attention as a flexible and reconfigurable antenna architecture. FAS can dynamically alter the position and shape of the radiating elements to exploit additional spatial degrees of freedom. In this paper, a comprehensive overview of FAS is first provided, including its fundamental principles and implementation methods. Then, the impact of key system parameters— such as the number of ports, the physical size of FAS, and the channel environment—on the performance of FAS is thoroughly investigated. Finally, open research challenges and future directions are highlighted for integrating FAS with other advanced 6G technologies. Ultimately, this paper aims to serve as a foundational reference for researchers seeking to unlock the full potential of fluid antennas in 6G wireless systems.

In this letter, to realize a motor-based pinching antenna systems (PASS), a pinching antenna array (PAA)-assisted downlink transmission system is investigated, where the pinching antennas are clustered on a array located on the waveguide. The closed-form expression for the antenna gain of the PAA in line-of-sight (LoS) scenarios with randomly deployed users are derived. To comprehensively evaluate the performance of PAA-assisted networks, we propose a priority-oriented (PO) case, where the PAA is configured to maximize the achievable rate of prioritized users. The achievable rate of both prioritize users and non-prioritize users are derived. The simulation outcomes highlight several important findings: i) The proposed PAA system achieves substantially better performance compared to traditional PA networks by leveraging enhanced antenna gain; ii) User distribution has a pronounced impact on the achievable sum rate in the PO case.

Radar-based gesture recognition and person identification offer a natural, convenient, and privacy-preserving approach to human-computer interaction. However, most existing research focuses predominantly on learning for a single task, which requires separate models for each task. This separation increases the complexity of the deployment and the computational overhead. To address these challenges, this study introduces a multi-task learning framework that simultaneously performs gesture recognition and person identification using a miniature radar sensor. By leveraging radar’s capacity to capture fine-grained spectral and spatial motion patterns, the framework incorporates micro-Doppler and range-Doppler processing, alongside a multi-branch architecture to enhance modality-specific feature representation. It enables unified learning of shared and task-specific features within a single network architecture. The proposed model, MTxLSTM, integrates CNN and the recent xLSTM to mitigate task interference, improve generalization, and improve gesture recognition through person-specific nuances while enhancing person identification by leveraging contextual gesture information. Experimental results reveal that MTxLSTM outperforms existing multi-task learning frameworks and state-of-the-art models, achieving 99.21% in gesture recognition and 98.59% in person identification with moderate model complexity and inference speed. This study concurrently executes gesture recognition and person identification using a miniature radar sensor, and marking the first application of xLSTM in radar sensing technology.