TRACE-CRC解决了多步CSI预测中的不确定性量化问题，比传统方法更可靠且不确定性球更小。
TRACE-CRC是一种用于多步信道状态信息预测的轨迹感知不确定性量化方法。该方法在预测的CSI矩阵周围构建Frobenius范数不确定性球，控制至少一个未来帧未被覆盖的风险。TRACE-CRC结合了未来步相关的误差分析、轨迹难度分层和先学习后测试风险控制。实验表明，TRACE-CRC实现了可靠的轨迹级覆盖率，不确定性球比保守的多步校正方法小得多，同时避免了紧凑步进和自适应保形基线的轨迹覆盖不足问题。
TRACE-CRC: Trajectory-Adaptive Conformal Risk Control for Multi-Step Channel State Information Prediction
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.