RSTA让车与车通信在不同环境和信道下仍能精准协作感知。
Domain-Generalized Adaptive Semantic Communication for Collaborative Perception
- 通过跨域原型对齐和跨信道梯度一致训练编码器，提升传输稳定性。
- 部署时仅更新0.21%参数，跨天气任务提升7.2 [email protected]，跨数据集任务+5.5。
- 无需同步、适应性强，适合真实复杂车联网场景应用。
我们提出RSTA，一种领域泛化的语义通信框架，可在无源条件下实现车载网（V2X）协作感知，应对观测域漂移和未见无线信道条件。在V2X中，接收的语义标记同时受传输前域偏移和传输中信道干扰影响；现有方法仅解决单一问题，导致适应过程被同时偏离域且物理退化的标记误导。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级部署解码适配器，仅允许在语义相关性与信道保真度均高的标记上反向传播梯度。理论任务鲁棒性分解将每个损失项与特定退化源关联，使每个算法组件可归因于可度量的误差模式。在AWGN上训练，测试于未见过的瑞利衰落信道，RSTA在四个V2X基准上实现：跨天气任务较预部署领域泛化提升+7.2 [email protected]，跨数据集任务提升+5.5，部署时仅更新0.21%参数，且无需代理间同步开销。
原文摘要 · Abstract (English)
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 [email protected] 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.
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