On optimal training and beamforming in uncorrelated MIMO systems with feedback

Francisco Rubio; Dongning Guo; Michael L Honig; Xavier Mestre

doi:10.1109/CISS.2008.4558647

On optimal training and beamforming in uncorrelated MIMO systems with feedback

Francisco Rubio^*, Dongning Guo, Michael L Honig, Xavier Mestre

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

This paper studies the design and analysis of optimal training-based beamforming in uncorrelated multiple-input multiple-output (MIMO) channels with known Gaussian statistics. First, given the response of the MIMO channel to a finite sequence of training vectors, the beamforming vector which maximizes the average received signal-to-noise ratio (SNR) over all channel realizations is found. Secondly, the question of what consists of optimal training for a given amount of training is addressed. Upper and lower bounds for the maximum achievable SNR using beamforming are established. Furthermore, optimal training sequences are conjectured to satisfy the Welch bound. The conjecture is supported by the evidence that such sequences achieve close to the upper bound with moderate to large amount of trainings.

Original language	English (US)
Title of host publication	CISS 2008, The 42nd Annual Conference on Information Sciences and Systems
Pages	902-907
Number of pages	6
DOIs	https://doi.org/10.1109/CISS.2008.4558647
State	Published - Sep 22 2008
Event	CISS 2008, 42nd Annual Conference on Information Sciences and Systems - Princeton, NJ, United States Duration: Mar 19 2008 → Mar 21 2008

Other

Other	CISS 2008, 42nd Annual Conference on Information Sciences and Systems
Country	United States
City	Princeton, NJ
Period	3/19/08 → 3/21/08

ASJC Scopus subject areas

Computer Science Applications
Information Systems
Control and Systems Engineering

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title = "On optimal training and beamforming in uncorrelated MIMO systems with feedback",

abstract = "This paper studies the design and analysis of optimal training-based beamforming in uncorrelated multiple-input multiple-output (MIMO) channels with known Gaussian statistics. First, given the response of the MIMO channel to a finite sequence of training vectors, the beamforming vector which maximizes the average received signal-to-noise ratio (SNR) over all channel realizations is found. Secondly, the question of what consists of optimal training for a given amount of training is addressed. Upper and lower bounds for the maximum achievable SNR using beamforming are established. Furthermore, optimal training sequences are conjectured to satisfy the Welch bound. The conjecture is supported by the evidence that such sequences achieve close to the upper bound with moderate to large amount of trainings.",

author = "Francisco Rubio and Dongning Guo and Honig, {Michael L} and Xavier Mestre",

year = "2008",

month = sep,

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doi = "10.1109/CISS.2008.4558647",

language = "English (US)",

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booktitle = "CISS 2008, The 42nd Annual Conference on Information Sciences and Systems",

note = "CISS 2008, 42nd Annual Conference on Information Sciences and Systems ; Conference date: 19-03-2008 Through 21-03-2008",

}

Rubio, F, Guo, D , Honig, ML & Mestre, X 2008, On optimal training and beamforming in uncorrelated MIMO systems with feedback. in CISS 2008, The 42nd Annual Conference on Information Sciences and Systems., 4558647, pp. 902-907, CISS 2008, 42nd Annual Conference on Information Sciences and Systems, Princeton, NJ, United States, 3/19/08. https://doi.org/10.1109/CISS.2008.4558647

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AU - Guo, Dongning

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N2 - This paper studies the design and analysis of optimal training-based beamforming in uncorrelated multiple-input multiple-output (MIMO) channels with known Gaussian statistics. First, given the response of the MIMO channel to a finite sequence of training vectors, the beamforming vector which maximizes the average received signal-to-noise ratio (SNR) over all channel realizations is found. Secondly, the question of what consists of optimal training for a given amount of training is addressed. Upper and lower bounds for the maximum achievable SNR using beamforming are established. Furthermore, optimal training sequences are conjectured to satisfy the Welch bound. The conjecture is supported by the evidence that such sequences achieve close to the upper bound with moderate to large amount of trainings.

AB - This paper studies the design and analysis of optimal training-based beamforming in uncorrelated multiple-input multiple-output (MIMO) channels with known Gaussian statistics. First, given the response of the MIMO channel to a finite sequence of training vectors, the beamforming vector which maximizes the average received signal-to-noise ratio (SNR) over all channel realizations is found. Secondly, the question of what consists of optimal training for a given amount of training is addressed. Upper and lower bounds for the maximum achievable SNR using beamforming are established. Furthermore, optimal training sequences are conjectured to satisfy the Welch bound. The conjecture is supported by the evidence that such sequences achieve close to the upper bound with moderate to large amount of trainings.

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