Reliable Narrowband Interference Detection via Backward Conformal Prediction

TL;DR

This paper introduces a backward conformal prediction framework for reliable narrowband interference detection in WiFi, ensuring controlled miscoverage levels aligned with operational constraints.

Contribution

It develops a novel backward conformal prediction method that fixes prediction-set size and estimates miscoverage, improving reliability in interference detection.

Findings

BCP provides reliable miscoverage estimates with accuracy close to uncalibrated baselines.

Simulation results demonstrate BCP's effectiveness in WiFi interference detection.

The framework adapts to operational budgets, enhancing practical deployment.

Abstract

Narrowband interference can severely degrade the performance of WiFi links by concentrating significant power on a small portion of the channel. Machine learning (ML) detectors trained on baseband I/Q samples can identify the affected subcarriers with high accuracy, surpassing model-based detectors that rely on hand-crafted statistics. The predictive probabilities produced by such detectors are, however, typically poorly calibrated, and downstream mitigation modules generally operate under strict resource budgets that limit the number of candidate interference states that can be acted upon. Conformal prediction (CP) provides a distribution-free framework for constructing prediction sets that control the probability of excluding the true output, i.e., the miscoverage level, at a prescribed level. However, this target miscoverage level must be fixed in advance, while the resulting prediction-set size remains uncontrolled, which is misaligned with operationally constrained settings. To address this issue, we develop a backward conformal prediction (BCP) framework in which the prediction-set size is fixed by the operational budget and the corresponding per-input miscoverage level is estimated from calibration data with provable reliability guarantees. We instantiate the framework for narrowband interference detection in WiFi systems and show through simulations that BCP yields reliable miscoverage estimates whose accuracy approaches that of an uncalibrated baseline as the calibration set grows.