发表机构
Chalmers University of Technology（查尔姆斯理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多步CSI预测中现有方法缺乏校准不确定性估计的问题，提出TRACE-CRC方法，通过轨迹自适应策略实现可靠轨迹级覆盖，且不确定性球更小，避免了覆盖不足问题。
AI 中文摘要
可靠预测时变信道状态信息（CSI）对高效无线通信至关重要。每个CSI帧是无线信道响应的矩阵值表示，一系列CSI帧构成时间信道轨迹。然而，现代基于深度学习的CSI预测器通常仅提供点预测，缺乏校准后的不确定性估计，这一问题在多步CSI预测中尤为突出——其目标是未来CSI矩阵的序列，若预测轨迹的任何部分不可靠，波束成形或调度等下游决策可能失效。我们提出结合轨迹自适应校准与误差轮廓的保形风险控制方法（TRACE-CRC），用于多步CSI预测中的轨迹感知不确定性量化。TRACE-CRC在预测的CSI矩阵周围构造Frobenius范数不确定性球，并控制至少一个未来帧未被覆盖的风险；该方法不独立校准每个未来步，而是整合未来步依赖的误差轮廓、轨迹难度分层及学习后测试（LTT）风险控制。实验表明，TRACE-CRC实现了可靠的轨迹级覆盖，且不确定性球远小于保守多步修正的情况，同时避免了紧凑分步及自适应保形基线方法的轨迹覆盖不足问题。
英文摘要
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.
CommentsPublished in Proceedings of Machine Learning Research (PMLR), volume 329, Conformal and Probabilistic Prediction with Applications (COPA 2026)
