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

arXiv: https://arxiv.org/abs/2501.14566 · HTML: https://mlsanity.com/paper/2501.14566/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2025.

## Built on by (most cited first, 5 of 5)

- [Conformal Calibration: Ensuring the Reliability of Black-Box AI in Wireless Systems](https://mlsanity.com/paper/2504.09310/) (2025)
- [Reliable Wireless Indoor Localization via Cross-Validated Prediction-Powered Calibration](https://mlsanity.com/paper/2507.20268/) (2025)
- [Conformal Changepoint Localization and Root Cause Analysis with Corrupted Observations](https://mlsanity.com/paper/2607.26481/) (2026)
- [TRACE-CRC: Trajectory-Adaptive Conformal Risk Control for Multi-Step Channel State Information Prediction](https://mlsanity.com/paper/2608.27124/) (2026)
- [CALM-5G: Event-Level Conformal Risk Control for Predictive Service-Assurance Alarms](https://mlsanity.com/paper/2610.17962/) (2026)

## Abstract (arXiv)

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.