Optimized training sequences for spatially correlated MIMO-OFDM

Tuan, HD; Kha, HH; Nguyen, HH; Luong, VJ

Optimized training sequences for spatially correlated MIMO-OFDM

Tuan, HD

Kha, HH Nguyen, HH Luong, VJ

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Wireless Communications, 2010, 9 (9), pp. 2768 - 2778
Issue Date:: 2010-09-01

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Field	Value	Language
dc.contributor.author	Tuan, HD https://orcid.org/0000-0003-0292-6061	en_US
dc.contributor.author	Kha, HH	en_US
dc.contributor.author	Nguyen, HH	en_US
dc.contributor.author	Luong, VJ	en_US
dc.date.issued	2010-09-01	en_US
dc.identifier.citation	IEEE Transactions on Wireless Communications, 2010, 9 (9), pp. 2768 - 2778	en_US
dc.identifier.issn	1536-1276	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15405
dc.description.abstract	In this paper, the training sequence design for multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems under the minimum mean square error (MMSE) criterion is addressed. The optimal training sequence for channel estimation in spatially correlated MIMO-OFDM systems was not known for an arbitrary signal-to-noise ratio (SNR). Only one class of training sequences was proposed in the literature in which the power allocation is given only for the extreme conditions of low and high SNRs. The current paper presents a necessary and sufficient condition for the optimal training sequence, and reformulates the training design problem as a convex optimization problem whose optimal solution is efficiently solved. In addition, tight upper bounds for MMSE and resulting low complexity iterative algorithms with the closed-form expression in iterations to find the optimum training sequence are derived. Simulation results confirm the superiority of the proposed design over the existing one in terms of both MSE estimation and BER performance. The proposed methods are also shown to be robust with respect to the spatial correlation mismatch at the transmitter. © 2010 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Wireless Communications	en_US
dc.relation.isbasedon	10.1109/TWC.2010.070710.081601	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Optimized training sequences for spatially correlated MIMO-OFDM	en_US
dc.type	Journal Article
utslib.citation.volume	9	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	1005 Communications Technologies	en_US
dc.location.activity	ISI:000283583900012	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access
pubs.issue	9	en_US
pubs.publication-status	Published	en_US
pubs.volume	9	en_US

Abstract:

In this paper, the training sequence design for multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems under the minimum mean square error (MMSE) criterion is addressed. The optimal training sequence for channel estimation in spatially correlated MIMO-OFDM systems was not known for an arbitrary signal-to-noise ratio (SNR). Only one class of training sequences was proposed in the literature in which the power allocation is given only for the extreme conditions of low and high SNRs. The current paper presents a necessary and sufficient condition for the optimal training sequence, and reformulates the training design problem as a convex optimization problem whose optimal solution is efficiently solved. In addition, tight upper bounds for MMSE and resulting low complexity iterative algorithms with the closed-form expression in iterations to find the optimum training sequence are derived. Simulation results confirm the superiority of the proposed design over the existing one in terms of both MSE estimation and BER performance. The proposed methods are also shown to be robust with respect to the spatial correlation mismatch at the transmitter. © 2010 IEEE.

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