发表机构
Inria Lille-Nord Europe; University of Luxembourg（欧洲北部欧洲研究所; 卢森堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对CSI仿真中加性噪声假设失效的问题，提出M_QTC模型，通过分位数映射、时间滤波和基于copula的子载波间重排序，将幅度误差降低8倍，并缩小89%的保真度差距。
AI 中文摘要
信道状态信息（CSI）已成为广泛应用于室内定位、活动识别和呼吸监测等场景的无线信道感知模态。由于在每种目标条件下收集标记数据不切实际，训练基于CSI的模型通常依赖于通过向记录的信道估计添加噪声或扰动（最常见的是加性高斯白噪声（AWGN））生成的模拟数据。这种做法假设天线与信道估计器之间的接收链路是线性和增益不变的。我们通过使用射频干扰作为受控扰动，在2个室内环境中的6个商用接收器上实证检验了这一假设。该假设不成立。自动增益控制在数字化之前以乘法方式压缩信道估计，产生任何加性噪声方差都无法再现的幅度分布。为了弥合由此产生的保真度差距，我们提出了M_QTC，一种测量校准模型，通过分位数映射、时间滤波和基于copula的子载波间重排序来学习每个子载波的分布变换。M_QTC将幅度误差降低了8倍，并在四个互补维度上缩小了89%的总体保真度差距。改进直接转移到下游任务中，在不同族系的5个分类器上，使用M_QTC模拟数据训练的分类器恢复了真实数据干扰检测性能的93%，而AWGN训练的分类器仍接近随机决策。
英文摘要
Channel State Information (CSI) has become a widely used wireless channel sensing modality for applications such as indoor localization, activity recognition, and respiration monitoring. Because collecting labeled data under every target condition is impractical, training CSI-based models often relies on simulated data produced by adding noise or perturbations to recorded channel estimates, most commonly additive white Gaussian noise (AWGN). This practice assumes that the receiver chain between the antenna and the channel estimator is linear and gain-invariant. We test this assumption empirically using RF jamming as a controlled perturbation on 6 commodity receivers across 2 indoor environments. The assumption does not hold. Automatic gain control compresses the channel estimate multiplicatively before digitization, producing amplitude distributions that no additive noise variance can reproduce. To close the resulting fidelity gap, we propose M_QTC, a measurement-calibrated model that learns the per-subcarrier distribution transformation through quantile mapping, temporal filtering, and copula-based cross-subcarrier reordering. M_QTC reduces amplitude error 8-fold and closes 89% of the aggregate fidelity gap across four complementary dimensions. The improvement transfers directly to downstream tasks, where 5 classifiers from different families trained on M_QTC-simulated data recover 93% of real-data jamming detection performance, while AWGN-trained classifiers remain near random decision.
Journal refMSWiM 2026 - 28th International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems
