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.

翻译：部署无线电频率(RF)本地化系统必然需要非三重努力,特别是最新的基于学习的品种。以前很少进行描述和比较如何在现实世界RF分布变换的情况下在实地部署已学的本地化网络的工作。本文介绍RadioBench:由最先进的八种本地化网络组成的一套8种本地化网络,用于研究和衡量其真实世界可部署性,使用5个新的行业级数据集。我们培训了10k个模型,以分析这些已学的本地化网络的内部运行情况,并发现它们在三个绩效轴线上的不同行为:(一) 学习,(二) 易于分销转移,(三) 本地化。我们利用从这一分析中获得的见解,建议最佳做法,以便在实际制约下部署基于学习的本地化。