Calibrating AI Models for Few-Shot Demodulation VIA Conformal Prediction

Kfir M. Cohen, Sangwoo Park, Osvaldo Simeone, Shlomo Shamai Shitz

Research output: Contribution to journalConference articlepeer-review

Abstract

Artificial Intelligent (AI) tools can be useful to address model deficits in the design of communication systems. However, conventional learning-based AI algorithms yield poorly calibrated decisions, unabling to quantify their outputs uncertainty. While Bayesian learning can enhance calibration by capturing epistemic uncertainty caused by limited data availability, formal calibration guarantees only hold under strong assumptions about the ground-truth, unknown, data generation mechanism. We propose to leverage the conformal prediction framework to obtain data-driven set predictions whose calibration properties hold irrespective of the data distribution. Specifically, we investigate the design of baseband demodulators in the presence of hard-to-model nonlinearities such as hardware imperfections, and propose set-based demodulators based on conformal prediction. Numerical results confirm the theoretical validity of the proposed demodulators, and bring insights into their average prediction set size efficiency.

Keywords

  • Calibration
  • Conformal Prediction
  • Demodulation

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Calibrating AI Models for Few-Shot Demodulation VIA Conformal Prediction'. Together they form a unique fingerprint.

Cite this