Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Calibrating AI Models for Few-Shot Demodulation via Conformal Prediction

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

TL;DR

The paper tackles calibrated, data-driven demodulation under limited pilot data and hardware nonlinearities. It adopts conformal prediction to convert probabilistic predictors into set-valued predictors with distribution-free calibration guarantees, introducing VB-CP, CV-CP, and their $K$-fold variants. Empirical results on a QPSK system with I/Q imbalance show that CP-based set predictors meet the target miscoverage while maintaining low inefficiency, with CV-based approaches outperforming VB in efficiency and Bayesian scoring further reducing set sizes. The work provides a principled framework for reliable uncertainty quantification in few-pilot demodulation, enabling robust integration of AI-based demodulators in practical communication systems.

Abstract

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.

Calibrating AI Models for Few-Shot Demodulation via Conformal Prediction

TL;DR

The paper tackles calibrated, data-driven demodulation under limited pilot data and hardware nonlinearities. It adopts conformal prediction to convert probabilistic predictors into set-valued predictors with distribution-free calibration guarantees, introducing VB-CP, CV-CP, and their -fold variants. Empirical results on a QPSK system with I/Q imbalance show that CP-based set predictors meet the target miscoverage while maintaining low inefficiency, with CV-based approaches outperforming VB in efficiency and Bayesian scoring further reducing set sizes. The work provides a principled framework for reliable uncertainty quantification in few-pilot demodulation, enabling robust integration of AI-based demodulators in practical communication systems.

Abstract

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.
Paper Structure (11 sections, 10 equations, 3 figures)

This paper contains 11 sections, 10 equations, 3 figures.

Table of Contents

  1. Introduction
  2. Problem Definition
  3. Channel Model
  4. Probabilistic Predictors
  5. Set Predictors
  6. Naïve Set Predictors from Probabilistic Predictors
  7. Conformal Prediction
  8. Nonconformity Scores
  9. Validation-Based Set Predictors
  10. Cross-Validation-Based Set Predictors
  11. Experiments and Conclusions

Figures (3)

  • Figure 1: QPSK demodulation with a demodulator trained using a limited number of pilots (gray symbols): (a) Constellation symbols (colored markers), optimal hard prediction (dashed lines), and model trained using the few pilots (solid lines). Accuracy and calibration of the trained predictor depend on the test input (gray square). (b) Probabilistic predictors obtained from the trained model (solid bars) and optimal predictive probabilities (dashed bars), with thick line indicating the hard prediction. (c) Set predictors output a subset of the constellation symbols for each input.
  • Figure 2: Coverage for naïve predictor, validation-based (VB) conformal predictor \ref{['eq: prediction set VB']}, cross-validation-based (CV) conformal predictor, \ref{['eq: prediction set CV classification']}, and the $K$-fold CV ($K$-CV) predictor as a function of the number of pilots $N$. The NC scores are evaluated either using frequentist learning (dashed lines) or Bayesian learning (solid lines).
  • Figure 3: Average set prediction size (inefficiency) for the same setting of Fig. \ref{['fig: coverage_vs_N_demod']}.