Calibrating Wireless AI via Meta-Learned Context-Dependent Conformal Prediction

TL;DR

This paper presents a meta-learning based approach to calibrate AI in wireless networks, ensuring reliable predictions across varying contexts without needing current environment data.

Contribution

It introduces ML-WCP, a novel zero-shot calibration method that adapts conformal prediction to different network contexts using meta-learning techniques.

Findings

Effective calibration across diverse network conditions

No current environment data needed for calibration

Improved reliability of AI predictions in wireless networks

Abstract

Modern software-defined networks, such as Open Radio Access Network (O-RAN) systems, rely on artificial intelligence (AI)-powered applications running on controllers interfaced with the radio access network. To ensure that these AI applications operate reliably at runtime, they must be properly calibrated before deployment. A promising and theoretically grounded approach to calibration is conformal prediction (CP), which enhances any AI model by transforming it into a provably reliable set predictor that provides error bars for estimates and decisions. CP requires calibration data that matches the distribution of the environment encountered during runtime. However, in practical scenarios, network controllers often have access only to data collected under different contexts -- such as varying traffic patterns and network conditions -- leading to a mismatch between the calibration and runtime distributions. This paper introduces a novel methodology to address this calibration-test distribution shift. The approach leverages meta-learning to develop a zero-shot estimator of distribution shifts, relying solely on contextual information. The proposed method, called meta-learned context-dependent weighted conformal prediction (ML-WCP), enables effective calibration of AI applications without requiring data from the current context. Additionally, it can incorporate data from multiple contexts to further enhance calibration reliability.