DocumentCode
20513
Title
Blind Pilot Decontamination
Author
Muller, Rafael Rios ; Cottatellucci, Laura ; Vehkapera, Mikko
Author_Institution
Friedrich-Alexander Univ. Erlangen-Nurnberg, Erlangen, Germany
Volume
8
Issue
5
fYear
2014
fDate
Oct. 2014
Firstpage
773
Lastpage
786
Abstract
A subspace projection to improve channel estimation in massive multi-antenna systems is proposed and analyzed. Together with power-controlled hand-off, it can mitigate the pilot contamination problem without the need for coordination among cells. The proposed method is blind in the sense that it does not require pilot data to find the appropriate subspace. It is based on the theory of large random matrices that predicts that the eigenvalue spectra of large sample covariance matrices can asymptotically decompose into disjoint bulks as the matrix size grows large. Random matrix and free probability theory are utilized to predict under which system parameters such a bulk decomposition takes place. Simulation results are provided to confirm that the proposed method outperforms conventional linear channel estimation if bulk separation occurs.
Keywords
blind source separation; channel estimation; covariance matrices; eigenvalues and eigenfunctions; multibeam antennas; probability; random processes; blind pilot decontamination; bulk separation; channel estimation; covariance matrices; eigenvalue spectra prediction; free probability theory; massive multiantenna system; matrix decomposition; pilot contamination problem; power control; random matrices; Channel estimation; Coherence; Eigenvalues and eigenfunctions; MIMO; Noise; Receiving antennas; Vectors; Multiple antennas; channel estimation; eigenvalue spectrum; free probability; massive MIMO; multiple-input multiple-output (MIMO) systems; principal component analysis; random matrices; spread-spectrum;
fLanguage
English
Journal_Title
Selected Topics in Signal Processing, IEEE Journal of
Publisher
ieee
ISSN
1932-4553
Type
jour
DOI
10.1109/JSTSP.2014.2310053
Filename
6756975
Link To Document