← 研踪 PaperTrailarXiv:2611.15359 · v1
MetaCom: Meta Codebook Learning for Robust V2X Semantic Communications
首次提交 2026-11-11 19:38
版本更新 2026-11-11 19:38
雷达观测 2026-11-13 11:11
摘要
Collaborative perception in Vehicle-to-Everything (V2X) networks faces dual challenges: strictly limited bandwidth and channel impairments. Existing static quantization schemes often suffer from feature manifold drift caused by highly non-stationary traffic environments. To address this, we propose MetaCom, a robust digital semantic communication framework featuring a Meta-Codebook Learning paradigm. First, we introduce a Differentiable Quantized Semantic Communications (DQSemCom) module trained with perturbations induced by the simulated digital modulation and channel via Gumbel-Softmax relaxation. Building on this, we design a Bi-Level Meta-Evolution Mechanism (Bi-MEM) that treats manifold drift as a sequence of rapid adaptation tasks, aligning the codebook via data-level smoothing and parameter-level meta-updates. Furthermore, we validate MetaCom under AWGN, Rayleigh, and Rician fading channels. Experimental results demonstrate that MetaCom achieves superior trade-offs between perception precision and communication overhead, outperforming state-of-the-art methods by a significant margin under low-SNR conditions.
作者备注：5 pages, 5 figures. Accepted by IEEE Communications Letters
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v1 · 2026-11-11 19:38 · 最新观测版本
MetaCom: Meta Codebook Learning for Robust V2X Semantic Communications
Collaborative perception in Vehicle-to-Everything (V2X) networks faces dual challenges: strictly limited bandwidth and channel impairments. Existing static quantization schemes often suffer from feature manifold drift caused by highly non-stationary traffic environments. To address this, we propose MetaCom, a robust digital semantic communication framework featuring a Meta-Codebook Learning paradigm. First, we introduce a Differentiable Quantized Semantic Communications (DQSemCom) module trained with perturbations induced by the simulated digital modulation and channel via Gumbel-Softmax relaxation. Building on this, we design a Bi-Level Meta-Evolution Mechanism (Bi-MEM) that treats manifold drift as a sequence of rapid adaptation tasks, aligning the codebook via data-level smoothing and parameter-level meta-updates. Furthermore, we validate MetaCom under AWGN, Rayleigh, and Rician fading channels. Experimental results demonstrate that MetaCom achieves superior trade-offs between perception precision and communication overhead, outperforming state-of-the-art methods by a significant margin under low-SNR conditions.
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