根据网络环境动态调整伪数据依赖，提升低标注数据下的通信系统性能
Context-Aware Doubly-Robust Semi-Supervised Learning
- 基于上下文感知的双重稳健学习，动态调节对伪数据的信任度
- 在低标注数据场景下，相比现有方法降低24%损失
- 适合资源受限、数据异构的下一代通信系统应用
人工智能在下一代通信系统中的广泛应用受到流量与网络条件异构性的挑战，亟需高度情境化、站点特定的数据。一种有前景的解决方案是结合真实数据与由网络数字孪生（NDT）生成的合成伪数据。然而，该方法的有效性取决于NDT的准确性，而其精度在不同情境下差异显著。为此，本文提出上下文感知双重稳健（CDR）学习，一种新型半监督学习框架，可根据不同情境下NDT的保真度水平自适应地调整对伪数据的依赖程度。在下行波束成形任务上评估表明，当标注数据稀缺时，相较于现有的双重稳健（DR）半监督学习，CDR实现了24%的损失下降。
原文摘要 · Abstract (English)
The widespread adoption of artificial intelligence (AI) in next-generation communication systems is challenged by the heterogeneity of traffic and network conditions, which call for the use of highly contextual, site-specific, data. A promising solution is to rely not only on real-world data, but also on synthetic pseudo-data generated by a network digital twin (NDT). However, the effectiveness of this approach hinges on the accuracy of the NDT, which can vary widely across different contexts. To address this problem, this paper introduces context-aware doubly-robust (CDR) learning, a novel semi-supervised scheme that adapts its reliance on the pseudo-data to the different levels of fidelity of the NDT across contexts. CDR is evaluated on the task of downlink beamforming where it outperforms previous state-of-the-art approaches, providing a 24% loss decrease when compared to doubly-robust (DR) semi-supervised learning in regimes with low labeled data availability.
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