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

arXiv 2608.27124 ↗First posted 27 Aug 2026cs.LG stat.MLcited by 0

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Abstract (arXiv)

Reliable prediction of time-varying channel state information (CSI) is essential for efficient wireless communication. Each CSI frame is a matrix-valued representation of the wireless channel response, and a sequence of CSI frames forms a temporal channel trajectory. Modern deep learning-based CSI predictors, however, often provide only point predictions and lack calibrated uncertainty estimates. This limitation is particularly problematic in multi-step CSI prediction, where the target is a sequence of future CSI matrices, and downstream decisions such as beamforming or scheduling may fail if any part of the predicted trajectory is unreliable. We propose trajectory-adaptive calibration and error profiling with conformal risk control (TRACE-CRC), a method for trajectory-aware uncertainty quantification in multi-step CSI prediction. TRACE-CRC constructs Frobenius-norm uncertainty balls around predicted CSI matrices and controls the risk that at least one future frame is uncovered. Instead of calibrating each future step independently, TRACE-CRC combines future-step-dependent error profiling, trajectory difficulty stratification, and learn-then-test (LTT) risk control. Empirically, TRACE-CRC achieves reliable trajectory-level coverage with substantially smaller uncertainty balls than conservative multi-step corrections, while avoiding the trajectory undercoverage of compact stepwise and adaptive conformal baselines.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 29 of its 41 references that are in the map. First posted on arXiv 27 Aug 2026.

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References · 29

  1. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 792
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  7. 2021Distribution-Free, Risk-Controlling Prediction Setscited by 288
  8. 2021Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Controlcited by 271
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  11. 2020Conformal prediction for time seriescited by 53
  12. 2024Fast yet Safe: Early-Exiting with Risk Controlcited by 30
17 more
  1. 2022Copula Conformal Prediction for Multi-step Time Series Forecastingcited by 29
  2. 2023Non-Exchangeable Conformal Risk Controlcited by 29
  3. 2020Massive MIMO Channel Prediction: Kalman Filtering vs. Machine Learningcited by 21
  4. 2023Forking Uncertainties: Reliable Prediction and Model Predictive Control with Sequence Models via Conformal Risk Controlcited by 20
  5. 2024Localized Adaptive Risk Controlcited by 17
  6. 2024Kernel-based Optimally Weighted Conformal Time-Series Predictioncited by 15
  7. 2024Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Riskscited by 14
  8. 2025Conformal Calibration: Ensuring the Reliability of Black-Box AI in Wireless Systemscited by 12
  9. 2019Addressing the curse of mobility in massive MIMO with Prony-based angular-delay domain channel predictionscited by 10
  10. 2023A Comparison of Neural Networks for Wireless Channel Predictioncited by 8
  11. 2024Online conformal inference for multi-step time series forecastingcited by 8
  12. 2024Aligning Model Properties via Conformal Risk Controlcited by 7
  13. 2024LinFormer: A Linear-based Lightweight Transformer Architecture For Time-Aware MIMO Channel Predictioncited by 6
  14. 2025Calibrating Wireless AI via Meta-Learned Context-Dependent Conformal Predictioncited by 5
  15. 2022Calibrating AI Models for Few-Shot Demodulation via Conformal Predictioncited by 4
  16. 2025Conformal Robust Beamforming via Generative Channel Modelscited by 1
  17. 2026MIMO Channel Prediction via Deep Learning-based Conformal Bayes Filtercited by 1

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