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MIMO Channel Prediction via Deep Learning-based Conformal Bayes Filter

Dongwon Kim, Jinu Gong, Joonhyuk Kang

TL;DR

A DL-based conformal Bayes filter that integrates DL-based prediction, conformal quantile regression (CQR), and Bayesian filtering is proposed that significantly improves DL-based prediction and outperforms the KF-based method.

Abstract

Channel prediction has emerged as an effective solution for acquiring accurate channel state information (CSI) in the presense of channel aging. Existing methods have inherent limitations, with conventional Kalman filter (KF)-based approach being vulnerable to model mismatch and deep learning (DL)-based approaches producing overconfident predictions. To address these issues, we propose a DL-based conformal Bayes filter (DCBF) that integrates DL-based prediction, conformal quantile regression (CQR), and Bayesian filtering. The proposed framework enables principled fusion of calibrated priors and observations, yielding reliable channel predictions with the calibrated uncertainty. Simulation results demonstrate that DCBF significantly improves DL-based prediction and outperforms the KF-based method.

MIMO Channel Prediction via Deep Learning-based Conformal Bayes Filter

TL;DR

A DL-based conformal Bayes filter that integrates DL-based prediction, conformal quantile regression (CQR), and Bayesian filtering is proposed that significantly improves DL-based prediction and outperforms the KF-based method.

Abstract

Channel prediction has emerged as an effective solution for acquiring accurate channel state information (CSI) in the presense of channel aging. Existing methods have inherent limitations, with conventional Kalman filter (KF)-based approach being vulnerable to model mismatch and deep learning (DL)-based approaches producing overconfident predictions. To address these issues, we propose a DL-based conformal Bayes filter (DCBF) that integrates DL-based prediction, conformal quantile regression (CQR), and Bayesian filtering. The proposed framework enables principled fusion of calibrated priors and observations, yielding reliable channel predictions with the calibrated uncertainty. Simulation results demonstrate that DCBF significantly improves DL-based prediction and outperforms the KF-based method.
Paper Structure (8 sections, 16 equations, 5 figures, 1 table, 1 algorithm)

This paper contains 8 sections, 16 equations, 5 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Conformal Bayes Filter-based Channel Prediction
  4. Deep Quantile Channel Predictor
  5. Conformal Quantile Regression
  6. Posterior mean estimation via importance sampling
  7. Simulation Results
  8. Conclusion

Figures (5)

  • Figure 1: CBF framework: (i) prediction step through DL-based channel quantile predictor (ii) calibration via CQR (iii) prior distribution construction with calibrated quantiles (iv) integration of prediction and measurement information through Bayesian filtering.
  • Figure 2: NMSE of the outdated channel, the KF-based predictor, the GRU-based predictor (with and without the DCBF scheme), and the MLP-based predictor (with and without the DCBF scheme) as a function of the transmit power under the UMi channel model with a user speed of $5$ km/h.
  • Figure 3: NMSE of the outdated channel, the KF-based predictor, the GRU-based predictor (with and without the DCBF scheme), and the MLP-based predictor (with and without the DCBF scheme) as a function of the transmit power under the UMi channel model with a user speed of $20$ km/h.
  • Figure 4: NMSE of the outdated channel, the KF-based predictor, the GRU-based predictor (with and without the DCBF scheme), and the MLP-based predictor (with and without the DCBF scheme) as a function of the transmit power under the UMa channel model with a user speed of $5$ km/h.
  • Figure 5: NMSE of the outdated channel, the KF-based predictor, the GRU-based predictor (with and without the DCBF scheme), and the MLP-based predictor (with and without the DCBF scheme) as a function of the transmit power under the UMa channel model with a user speed of $20$ km/h.