On the Asymptotic MSE-Optimality of Parametric Bayesian Channel Estimation in mmWave Systems
Abstract
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.
Fields of Study & Subjects
Millimeter-Wave Propagation and Modeling
Advanced MIMO Systems Optimization
Advanced Wireless Communication Techniques
Publication details
- Journal / source
- IEEE Signal Processing Letters
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Volume
- 33
- Pages
- 653–657
- Publication date
- 1 January 2026