面向协同感知的域泛化自适应语义通信
Domain-Generalized Adaptive Semantic Communication for Collaborative Perception
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中文总结 AI 辅助
提出域泛化语义通信框架RSTA，通过跨域原型对齐等技术解决车路协同感知中观测域偏移与信道损坏耦合退化问题，在多基准测试中取得AP指标提升且部署开销极低。
中文摘要 AI 辅助
我们提出RSTA，一种域泛化语义通信框架，可在观测域偏移和未知无线信道条件下实现无数据源的车路协同感知。在车路协同（V2X）中，接收的语义令牌会受到传输前域漂移和传输中信道损坏的耦合退化影响；现有方法仅处理单一退化源，导致适应过程被同时偏离域且物理退化的令牌误导。RSTA通过跨域原型对齐和跨信道梯度一致性训练预部署语义编码器以保障传输稳定性，并通过可靠性门控熵最小化更新轻量型部署中解码器适配器，该方法将梯度限制在语义相关性和信道保真度均排名较高的令牌上。理论任务鲁棒性分解将每个损失项与不同退化源关联，使各算法组件基于可测量误差模式。在加性高斯白噪声（AWGN）上训练、在未知瑞利衰落信道上测试时，RSTA在跨天气任务的预部署域泛化上实现7.2的AP@0.7提升，在四个车路协同基准的跨数据集任务上实现5.5的提升，部署中仅更新0.21%的参数，且无智能体间同步开销。
英文摘要
We propose RSTA, a domain-generalized semantic communication framework enabling source-free V2X collaborative perception under both observation-domain shift and unseen wireless channel conditions. In V2X, received semantic tokens suffer coupled degradation from pre-transmission domain drift and in-transit channel corruption; existing methods address only one source, leaving adaptation misled by tokens that are simultaneously off-domain and physically degraded. RSTA trains a pre-deployment semantic encoder for transmission stability via cross-domain prototype alignment and cross-channel gradient consistency, and updates a lightweight in-deployment decoder adapter through reliability-gated entropy minimization that restricts gradients to tokens ranked high in both semantic relevance and channel fidelity. A theoretical task robustness decomposition links each loss term to a distinct degradation source, grounding each algorithmic component in a measurable error mode. Trained on AWGN and tested on unseen Rayleigh fading, RSTA achieves +7.2 AP@0.7 over pre-deployment domain generalization on cross-weather tasks and +5.5 on cross-dataset tasks across four V2X benchmarks, updating only 0.21\% of parameters in-deployment with zero inter-agent synchronization overhead.
发表机构
- Tsinghua University（清华大学）
机构由 AI 辅助整理，请以论文原文为准。
