Efficient channel estimation in millimeter wave hybrid MIMO systems with low resolution ADCs

Aryan Kaushik, Evangelos Vlachos, John Thompson, Alessandro Perelli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Citations (Scopus)

Abstract

This paper proposes an efficient channel estimation algorithm for millimeter wave (mmWave) systems with a hybrid analog-digital multiple-input multiple-output (MIMO) architecture and few-bits quantization at the receiver. The sparsity of the mmWave MIMO channel is exploited for the problem formulation while limited resolution analog-to-digital converters (ADCs) are used in the receiver architecture. The estimation problem can be tackled using compressed sensing through the Stein's unbiased risk estimate (SURE) based parametric denoiser with the generalized approximate message passing (GAMP) framework. Expectation-maximization (EM) density estimation is used to avoid the need of specifying channel statistics resulting the EM-SURE-GAMP algorithm to estimate the channel. SURE, depending on the noisy observation, is minimized to adaptively optimize the denoiser within the parametric class at each iteration. The proposed solution is compared with the expectation-maximization generalized AMP (EM-GAMP) solution and the mean square error (MSE) performs better with respect to low and high signal-to-noise ratio (SNR) regimes, the number of ADC bits, and the training length. The use of the low resolution ADCs reduces power consumption and leads to an efficient mmWave MIMO system.
Original languageEnglish
Title of host publication2018 26th European Signal Processing Conference (EUSIPCO)
PublisherIEEE
Pages1825-1829
Number of pages5
ISBN (Print)978-1-5386-3736-4
DOIs
Publication statusPublished - 7 Sep 2018
Event2018 26th European Signal Processing Conference (EUSIPCO) - Rome, Italy
Duration: 3 Sep 20187 Sep 2018

Conference

Conference2018 26th European Signal Processing Conference (EUSIPCO)
Period3/09/187/09/18

Keywords

  • MIMO communication
  • Receivers
  • Channel estimation
  • Radio frequency
  • Transmitters
  • Sparse matrices
  • Signal resolution

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