He Geng

Xinwei Yue, He Geng, Jingjing Zhao et al.

This paper introduces a fluid antenna system (FAS) into reconfigurable intelligent surface (RIS) assisted non-orthogonal multiple access (NOMA) communication networks, where the non-orthogonal users are equipped with planar fluid antennas. Specifically, we formulate a sum rate maximization problem for FAS-RIS-NOMA networks, which jointly optimizes the fluid ports, the RIS deployment, and the phase shift matrix. To solve the resulting non-convex optimization problem involving highly coupled variables, an iterative algorithm based on alternating optimization is employed to decompose the original problem into three subproblems. Exhaustive search is employed for optimizing the fluid ports, particle swarm optimization is used for the RIS deployment, and semidefinite relaxation with successive convex approximation is adopted for optimizing the phase shift matrix. Finally, the simulation results show that: 1) compared with traditional antenna systems and orthogonal multiple access, the FAS-RIS-NOMA networks achieve higher system throughput under high signal-to-noise ratio conditions; and 2) by increasing the number of RIS elements and enlarging the FAS size, the sum rate of FAS-RIS-NOMA networks can be significantly enhanced.

Shuliang Liu, Qi Zheng, Jesse Jiaxi Xu et al. · tsinghua

Vision-language models demand watermarking solutions that protect intellectual property without compromising multimodal coherence. Existing text watermarking methods disrupt visual-textual alignment through biased token selection and static strategies, leaving semantic-critical concepts vulnerable. We propose VLA-Mark, a vision-aligned framework that embeds detectable watermarks while preserving semantic fidelity through cross-modal coordination. Our approach integrates multiscale visual-textual alignment metrics, combining localized patch affinity, global semantic coherence, and contextual attention patterns, to guide watermark injection without model retraining. An entropy-sensitive mechanism dynamically balances watermark strength and semantic preservation, prioritizing visual grounding during low-uncertainty generation phases. Experiments show 7.4% lower PPL and 26.6% higher BLEU than conventional methods, with near-perfect detection (98.8% AUC). The framework demonstrates 96.1\% attack resilience against attacks such as paraphrasing and synonym substitution, while maintaining text-visual consistency, establishing new standards for quality-preserving multimodal watermarking