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Variational Hierarchical Posterior Matching for mmWave Wireless Channels Online Learning

Abstract : We propose a beam alignment algorithm that enables initial access establishment between two transceivers equipped with hybrid digital-analog antenna arrays operating in millimeter wave wireless channels. The proposed method builds upon an active channel learning method based on hierarchical posterior matching that was originally proposed for single-sided beam alignment on single path dominant channels. We extend it to the double-sided alignment problem and propose an estimation framework based on variational Bayesian inference that accounts for the uncertainties on the unknown channel complex gain and noise variance. The proposed approach is numerically shown to be resilient to the single path assumption and reaches near optimal beamforming gains with a moderate training overhead, even at low signal-to-noise ratios.
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Submitted on : Tuesday, July 28, 2020 - 2:57:04 PM
Last modification on : Friday, April 22, 2022 - 10:48:04 AM
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  • HAL Id : hal-02908187, version 1


Nabil Akdim, Carles Navarro Manchón, Mustapha Benjillali, Pierre Duhamel. Variational Hierarchical Posterior Matching for mmWave Wireless Channels Online Learning. 21st IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2020), May 2020, Atlanta, GA, United States. ⟨hal-02908187⟩



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