Satellite communication (SatCom), as an effective complement to terrestrial networks, has attracted considerable attention from both academia and industry owing to its wide coverage and high flexibility. However, the inherent openness of satellite links renders them highly vulnerable to eavesdropping, thereby posing significant security challenges. In this paper, we propose a satellite covert precision wireless communication (CPWC) system, where multiple intelligent reflecting surfaces (IRSs) cooperate to assist satellite transmissions, ensuring that confidential information is delivered to legitimate users while remaining undetectable to wardens. To further enhance covertness, an orthogonal frequency division multiplexing (OFDM)-based random subcarrier selection (RSCS) method is developed to concentrate the signal energy at the intended receiver. Under a practical satellite-terrestrial channel model, we derive closed-form covertness constraints for the CPWC system based on relative entropy and detection error probability. Under the relative-entropy constraint and the satellite power constraint, we maximize the covert rate by an alternating-optimization (AO) based semidefinite relaxation (SDR) iterative algorithm and obtain a high-quality feasible solution. Using this solution as a warm start, we further impose the detection-error-probability constraint and refine the beamformer through a sequential quadratic programming (SQP) based algorithm. Numerical results demonstrate the effectiveness of the proposed CPWC system, where the detection-error-probability-based scheme outperforms the second-order cone programming (SOCP) benchmark, the random-phase-shift design, the one-bit IRS quantized scheme, and the SDR baseline without precise communication (PC) in terms of covert rate.
Haoyang Wu, Yunfan Bai, Mei Shen et al.· 0 citations
The integration of Large Vision-Language Models (LVLMs) with 3D scene understanding has shown great promise. However, existing 3D Visual Question Answering (3D QA) paradigms face severe bottlenecks. Directly feeding dense, unconstrained multi-view video streams or full point clouds into LLMs incurs prohibitive computational overhead and attention dilution, rendering models highly susceptible to spatial disorientation and visual hallucinations. To address these challenges, we propose Opti3D, a training-free, plug-and-play structured evidence construction framework that retrieves compact query-relevant visual evidence for off-the-shelf Video LLMs. Specifically, Opti3D constructs a global Bird’s-Eye View (BEV) map to preserve macroscopic spatial layout and further organizes local object observations through explicit geometry-aware cross-view deduplication. Instead of treating every sampled frame as an independent visual input, Opti3D groups redundant 2D observations that correspond to the same physical instance and then selects compact, viewpoint-informative local evidence using a multi-dimensional view-quality score. In this way, complex 3D roaming videos are converted into a structured visual evidence set containing a global BEV context and non-redundant local instance views. Extensive experiments on challenging 3D QA benchmarks demonstrate that Opti3D achieves competitive performance among training-free or frozen-backbone Video LLM baselines, while remaining below specialized 3D LLMs trained with task-specific 3D-language supervision. The results show that explicit geometry-aware view deduplication reduces redundant visual inputs and provides more reliable evidence for viewpoint-ambiguous questions without requiring full supervision or task-specific training.
Hui-Hui Liu, Haoyang Wu· IEEE Access· 0 citations
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