Under-determined training and estimation for distributed transmit beamforming systems

Zhang, JA; Yang, TT; Chen, Z

Under-determined training and estimation for distributed transmit beamforming systems

Zhang, JA

Yang, TT

Chen, Z

Permalink

Publication Type:: Journal Article
Citation:: IEEE Transactions on Wireless Communications, 2013, 12 (4), pp. 1936 - 1946
Issue Date:: 2013-05-20

Closed Access

	Filename	Description	Size
	07 ZhangYangTWC13_DistributedBeamforming.pdf	Published Version	578.74 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Zhang, JA https://orcid.org/0000-0002-6102-3762	en_US
dc.contributor.author	Yang, TT https://orcid.org/0000-0002-9870-6866	en_US
dc.contributor.author	Chen, Z	en_US
dc.date.issued	2013-05-20	en_US
dc.identifier.citation	IEEE Transactions on Wireless Communications, 2013, 12 (4), pp. 1936 - 1946	en_US
dc.identifier.issn	1536-1276	en_US
dc.identifier.uri	http://hdl.handle.net/10453/39571
dc.description.abstract	Distributed transmit beamforming (DTB) can significantly boost the signal-to-noise ratio (SNR) of a wireless communication system. To realize the benefits of DTB, generating and feeding back beamforming vector are very challenging tasks. Existing schemes have either enormous overhead or weak robustness in noisy channels. In this paper, we investigate the design of training sequences and beamforming vector estimators in DTB systems. We consider an under-determined case, where the length of training sequence N sent from each node is smaller than the number of source nodes M. We derive the optimal estimation of the beamforming vector that maximizes the beamforming gain and show that it can be well approximated as the linear minimum mean square error (LMMSE) estimator. Based on the LMMSE estimator, we investigate the optimal design of training sequences and propose efficient DTB schemes. We analytically show that these schemes can achieve approximately N times increased SNR in uncorrelated channels, and even higher gain in correlated ones. We also propose a concatenated training scheme which optimally combines the training signals over multiple frames to obtain the beamforming vector. Simulation results demonstrate that the proposed DTB schemes can yield significant gains even at very low SNRs, with total feedback bits much less than those required in the existing schemes. © 2002-2012 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Wireless Communications	en_US
dc.relation.isbasedon	10.1109/TWC.2013.022013.121258	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Under-determined training and estimation for distributed transmit beamforming systems	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	12	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1005 Communications Technologies	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
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	12	en_US

Abstract:

Distributed transmit beamforming (DTB) can significantly boost the signal-to-noise ratio (SNR) of a wireless communication system. To realize the benefits of DTB, generating and feeding back beamforming vector are very challenging tasks. Existing schemes have either enormous overhead or weak robustness in noisy channels. In this paper, we investigate the design of training sequences and beamforming vector estimators in DTB systems. We consider an under-determined case, where the length of training sequence N sent from each node is smaller than the number of source nodes M. We derive the optimal estimation of the beamforming vector that maximizes the beamforming gain and show that it can be well approximated as the linear minimum mean square error (LMMSE) estimator. Based on the LMMSE estimator, we investigate the optimal design of training sequences and propose efficient DTB schemes. We analytically show that these schemes can achieve approximately N times increased SNR in uncorrelated channels, and even higher gain in correlated ones. We also propose a concatenated training scheme which optimally combines the training signals over multiple frames to obtain the beamforming vector. Simulation results demonstrate that the proposed DTB schemes can yield significant gains even at very low SNRs, with total feedback bits much less than those required in the existing schemes. © 2002-2012 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/39571