论文标题
基准在分销转移下学习的无线电定位
Benchmarking Learnt Radio Localisation under Distribution Shift
论文作者
论文摘要
部署射频（RF）本地化系统总是需要非平凡的努力，尤其是对于最新的基于学习的品种。在表征和比较如何在现实世界的RF分布变化下将学到的本地化网络部署在现场上的几乎没有工作。在本文中，我们介绍了放射线座：一套由最先进的八个学识渊博的本地网络网，用于研究和基准测试其现实世界的可部署性，利用五个新型的行业级数据集。我们训练10K模型来分析这些学识渊博的本地网络的内部工作，并在三个性能轴上揭示它们的不同行为：（i）学习，（ii）分配转移的倾向以及（iii）本地化。我们利用从此分析中获得的见解来推荐最佳实践，以在实际限制下可部署基于学习的RF定位。
Deploying radio frequency (RF) localisation systems invariably entails non-trivial effort, particularly for the latest learning-based breeds. There has been little prior work on characterising and comparing how learnt localiser networks can be deployed in the field under real-world RF distribution shifts. In this paper, we present RadioBench: a suite of 8 learnt localiser nets from the state-of-the-art to study and benchmark their real-world deployability, utilising five novel industry-grade datasets. We train 10k models to analyse the inner workings of these learnt localiser nets and uncover their differing behaviours across three performance axes: (i) learning, (ii) proneness to distribution shift, and (iii) localisation. We use insights gained from this analysis to recommend best practices for the deployability of learning-based RF localisation under practical constraints.
