Conformal Prediction for Trustworthy Detection of Railway Signals

Andéol, Léo; Fel, Thomas; Grancey, Florence De; Mossina, Luca

doi:10.48550/arxiv.2301.11136

Cited by 1 publication

(1 citation statement)

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“…CP is a class of post-hoc calibration methods that transform standard probabilistic model into a set predictor that is guaranteed to contain the true target with probability no smaller than a predetermined coverage level [21], [22]. CP is experiencing a renaissance [23], [24], [25], [26], with novel applications in [27], [28], [29], [30]. Online CP alleviates the limitation of conventional CP of requiring a separate calibration data at the cost of providing time-averaged, rather than ensemble, reliability guarantees [9], [10], [31], [32].…”

Section: B Related Workmentioning

confidence: 99%

Guaranteed Dynamic Scheduling of Ultra-Reliable Low-Latency Traffic via Conformal Prediction

Cohen

Park

Simeone

et al. 2023

IEEE Signal Process. Lett.

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The dynamic scheduling of ultra-reliable and lowlatency communication traffic (URLLC) in the uplink can significantly enhance the efficiency of coexisting services, such as enhanced mobile broadband (eMBB) devices, by only allocating resources when necessary. The main challenge is posed by the uncertainty in the process of URLLC packet generation, which mandates the use of predictors for URLLC traffic in the coming frames. In practice, such prediction may overestimate or underestimate the amount of URLLC data to be generated, yielding either an excessive or an insufficient amount of resources to be pre-emptively allocated for URLLC packets. In this letter, we introduce a novel scheduler for URLLC packets that provides formal guarantees on reliability and latency irrespective of the quality of the URLLC traffic predictor. The proposed method leverages recent advances in online conformal prediction (CP), and follows the principle of dynamically adjusting the amount of allocated resources so as to meet reliability and latency requirements set by the designer.

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