On the Asymptotic MSE-Optimality of Parametric Bayesian Channel Estimation in mmWave Systems

Fuente: arXiv
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Main Authors: Weißer, Franz, Utschick, Wolfgang
Format: Preprint
Published: 2025
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author Weißer, Franz
Utschick, Wolfgang
author_facet Weißer, Franz
Utschick, Wolfgang
contents The mean square error (MSE)-optimal estimator is known to be the conditional mean estimator (CME). This paper introduces a parametric channel estimation technique based on Bayesian estimation. This technique uses the estimated channel parameters to parameterize the well-known LMMSE channel estimator. We first derive an asymptotic CME formulation that holds for a wide range of priors on the channel parameters. Based on this, we show that parametric Bayesian channel estimation is MSE-optimal for high signal-to-noise ratio (SNR) and/or long coherence intervals, i.e., many noisy observations provided within one coherence interval. Numerical simulations validate the derived formulations.
format Preprint
id arxiv_https___arxiv_org_abs_2504_16710
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle On the Asymptotic MSE-Optimality of Parametric Bayesian Channel Estimation in mmWave Systems
Weißer, Franz
Utschick, Wolfgang
Signal Processing
The mean square error (MSE)-optimal estimator is known to be the conditional mean estimator (CME). This paper introduces a parametric channel estimation technique based on Bayesian estimation. This technique uses the estimated channel parameters to parameterize the well-known LMMSE channel estimator. We first derive an asymptotic CME formulation that holds for a wide range of priors on the channel parameters. Based on this, we show that parametric Bayesian channel estimation is MSE-optimal for high signal-to-noise ratio (SNR) and/or long coherence intervals, i.e., many noisy observations provided within one coherence interval. Numerical simulations validate the derived formulations.
title On the Asymptotic MSE-Optimality of Parametric Bayesian Channel Estimation in mmWave Systems
topic Signal Processing
url https://arxiv.org/abs/2504.16710