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Channel Estimation for Massive MIMO TDD Systems Assuming Pilot Contamination and Frequency Selective Fading

Felipe Augusto Pereira de Figueiredo UGent, Fabbryccio Cardoso, Ingrid Moerman UGent and Gustavo Fraidenraich (2017) IEEE Access.
abstract
Channel estimation is crucial for massive MIMO systems to scale up multi-user (MU) MIMO, providing significant improvement in spectral and energy efficiency. In this paper, we present a simple and practical channel estimator for multipath multi-cell massive MIMO TDD systems with pilot contamination, which poses significant challenges to channel estimation. The proposed estimator addresses performance under moderate to strong pilot contamination without previous knowledge of the inter-cell large-scale fading coefficients and noise power. Additionally, we derive and assess an approximate analytical mean square error (MSE) expression for the proposed channel estimator. We show through simulations that the proposed estimator performs asymptotically as well as the MMSE estimator with respect to the number of antennas and multipath coefficients.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
in press
subject
keyword
Massive MU-MIMO, channel estimation, pilot contamination, multipath, Zadoff-Chu sequences
journal title
IEEE Access
editor
Maurizio Magarini
ISSN
2169-3536
DOI
10.1109/ACCESS.2017.2749602
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
8530461
handle
http://hdl.handle.net/1854/LU-8530461
date created
2017-09-06 20:01:21
date last changed
2017-09-11 07:52:53
@article{8530461,
  abstract     = {Channel estimation is crucial for massive MIMO systems to scale up multi-user (MU) MIMO, providing significant improvement in spectral and energy efficiency. In this paper, we present a simple and practical channel estimator for multipath multi-cell massive MIMO TDD systems with pilot contamination, which poses significant challenges to channel estimation. The proposed estimator addresses performance under moderate to strong pilot contamination without previous knowledge of the inter-cell large-scale fading coefficients and noise power. Additionally, we derive and assess an approximate analytical mean square error (MSE) expression for the proposed channel estimator. We show through simulations that the proposed estimator performs asymptotically as well as the MMSE estimator with respect to the number of antennas and multipath coefficients.},
  author       = {Pereira de Figueiredo, Felipe Augusto and Cardoso, Fabbryccio and Moerman, Ingrid and Fraidenraich, Gustavo },
  editor       = {Magarini, Maurizio },
  issn         = {2169-3536},
  journal      = {IEEE Access},
  keyword      = {Massive MU-MIMO,channel estimation,pilot contamination,multipath,Zadoff-Chu sequences},
  language     = {eng},
  title        = {Channel Estimation for Massive MIMO TDD Systems Assuming Pilot Contamination and Frequency Selective Fading},
  url          = {http://dx.doi.org/10.1109/ACCESS.2017.2749602},
  year         = {2017},
}

Chicago
Pereira de Figueiredo, Felipe Augusto, Fabbryccio Cardoso, Ingrid Moerman, and Gustavo Fraidenraich. 2017. “Channel Estimation for Massive MIMO TDD Systems Assuming Pilot Contamination and Frequency Selective Fading.” Ed. Maurizio Magarini. IEEE Access.
APA
Pereira de Figueiredo, F. A., Cardoso, F., Moerman, I., & Fraidenraich, G. (2017). Channel Estimation for Massive MIMO TDD Systems Assuming Pilot Contamination and Frequency Selective Fading. (M. Magarini, Ed.)IEEE Access.
Vancouver
1.
Pereira de Figueiredo FA, Cardoso F, Moerman I, Fraidenraich G. Channel Estimation for Massive MIMO TDD Systems Assuming Pilot Contamination and Frequency Selective Fading. Magarini M, editor. IEEE Access. 2017;
MLA
Pereira de Figueiredo, Felipe Augusto, Fabbryccio Cardoso, Ingrid Moerman, et al. “Channel Estimation for Massive MIMO TDD Systems Assuming Pilot Contamination and Frequency Selective Fading.” Ed. Maurizio Magarini. IEEE Access (2017): n. pag. Print.