RAIL-5G: A Challenging Real-World Dataset and Benchmark for AI-Based 5G Indoor Localization
Reliable radio-based indoor localization is essential for industrial automation, yet existing methods are often evaluated in environments that do not fully reflect the severe multipath and non-line-of-sight (NLOS) conditions encountered in real deployments. This limits our understanding of how AI, i.e., modern geometric, unsupervised, and semi-supervised localization methods generalize to industrial 5G settings. We present RAIL-5G, a public real-world 5G NR channel impulse response (CIR) benchmark recorded in an operational railway maintenance facility. RAIL-5G covers an 811m$^2$ indoor area with eight distributed radio units, robot-mounted user equipment (UE) trajectories, high-accuracy ...
| Authors | Jonas Pirkl, Andreas Eidloth, Maximilian Kasparek et al. |
|---|---|
| Primary category | eess.SP |
| Published | 18 October 2026 |
| Last updated at source | 18 October 2026 |
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