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arXiv Machine Learning By Kiarash Rezaei, Mehdi Sattari, Javad Aliakbari, Tommy Svensson, Paolo Monti, Carlos Natalino

TRACE-CRC: Trajectory-Adaptive Conformal Risk Control for Multi-Step Channel State Information Prediction

Read the original on arXiv Machine Learning →

The paper introduces TRACE-CRC, a trajectory‑adaptive conformal risk control method for multi‑step channel state information (CSI) prediction. It builds Frobenius‑norm uncertainty balls around predicted CSI matrices and controls the risk that any future frame is uncovered, using future‑step‑dependent error profiling, trajectory difficulty stratification, and learn‑then‑test risk control. Experiments show that TRACE‑CRC delivers reliable trajectory‑level coverage with smaller uncertainty balls than conservative multi‑step corrections and avoids undercoverage seen in stepwise and adaptive baselines.

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