  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.
