CSI Simulation: Why Additive Noise Fails and How to Fix It

2026-07-02

AI Summary

Traditional AWGN channel simulation fails to account for the multiplicative compression effect introduced by the receiver’s automatic gain control (AGC), leading to a severe mismatch between simulated and real-world CSI amplitude distributions. This work is the first to identify and characterize this nonlinear distortion and proposes the M_QTC calibration framework, which jointly models CSI amplitude statistics through quantile mapping, temporal filtering, and Copula-based subcarrier reordering. Experimental results demonstrate that M_QTC reduces amplitude estimation error by a factor of eight and closes 89% of the fidelity gap relative to empirical measurements. Furthermore, classifiers trained on data generated with M_QTC recover 93% of their true performance in interference detection tasks, substantially outperforming those trained using conventional AWGN-simulated data.

Abstract

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.

Problem

• Channel State Information
• additive noise
• fidelity gap
• automatic gain control
• CSI simulation

Innovation

• Channel State Information
• additive noise failure
• automatic gain control
• distribution transformation
• fidelity gap