Effectively inserted training for channel state estimation of spatially correlated MIMO-OFDM

Sheng, Z; Tuan, HD; Kha, HH; Fang, Y

Effectively inserted training for channel state estimation of spatially correlated MIMO-OFDM

Sheng, Z

Tuan, HD

Kha, HH Fang, Y

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Publication Type:: Conference Proceeding
Citation:: 2016 IEEE 6th International Conference on Communications and Electronics, IEEE ICCE 2016, 2016, pp. 89 - 93
Issue Date:: 2016-09-07

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Field	Value	Language
dc.contributor.author	Sheng, Z https://orcid.org/0000-0003-0288-7031	en_US
dc.contributor.author	Tuan, HD https://orcid.org/0000-0003-0292-6061	en_US
dc.contributor.author	Kha, HH	en_US
dc.contributor.author	Fang, Y	en_US
dc.date.issued	2016-09-07	en_US
dc.identifier.citation	2016 IEEE 6th International Conference on Communications and Electronics, IEEE ICCE 2016, 2016, pp. 89 - 93	en_US
dc.identifier.isbn	9781509019311	en_US
dc.identifier.uri	http://hdl.handle.net/10453/101746
dc.description.abstract	© 2016 IEEE. This work considers channel state estimation of multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) with spatial correlation by inserting a very short-length training sequence into each OFDM symbol. The rate occupancy of the training sequence within one OFDM symbol is just 1/128. The objective for designing training sequences is to minimize the entropy of the error between channel state and its estimator. Unlike the case with full length training sequences, where the training sequence design is formulated as a convex optimization problem with the available computational solution, the problem of short-length training sequence design is highly nonconvex and thus is very computationally challenging. Our main result is to develop a low-complexity iterative procedure for its solution. Simulations show the effectiveness of our methods.	en_US
dc.relation.ispartof	2016 IEEE 6th International Conference on Communications and Electronics, IEEE ICCE 2016	en_US
dc.relation.isbasedon	10.1109/CCE.2016.7562618	en_US
dc.title	Effectively inserted training for channel state estimation of spatially correlated MIMO-OFDM	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	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.publication-status	Published	en_US

Abstract:

© 2016 IEEE. This work considers channel state estimation of multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) with spatial correlation by inserting a very short-length training sequence into each OFDM symbol. The rate occupancy of the training sequence within one OFDM symbol is just 1/128. The objective for designing training sequences is to minimize the entropy of the error between channel state and its estimator. Unlike the case with full length training sequences, where the training sequence design is formulated as a convex optimization problem with the available computational solution, the problem of short-length training sequence design is highly nonconvex and thus is very computationally challenging. Our main result is to develop a low-complexity iterative procedure for its solution. Simulations show the effectiveness of our methods.

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http://hdl.handle.net/10453/101746