On optimal input and capacity of non-coherent correlated MISO channels under per-antenna power constraints

Vu, M; Tran, NH; Tuan, HD; Nguyen, TV; Nguyen, DHN

On optimal input and capacity of non-coherent correlated MISO channels under per-antenna power constraints

Vu, M Tran, NH Tuan, HD

Nguyen, TV Nguyen, DHN

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Publication Type:: Conference Proceeding
Citation:: 2018 IEEE 7th International Conference on Communications and Electronics, ICCE 2018, 2018, pp. 115 - 120
Issue Date:: 2018-09-13

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Field	Value	Language
dc.contributor.author	Vu, M	en_US
dc.contributor.author	Tran, NH	en_US
dc.contributor.author	Tuan, HD https://orcid.org/0000-0003-0292-6061	en_US
dc.contributor.author	Nguyen, TV	en_US
dc.contributor.author	Nguyen, DHN	en_US
dc.date.issued	2018-09-13	en_US
dc.identifier.citation	2018 IEEE 7th International Conference on Communications and Electronics, ICCE 2018, 2018, pp. 115 - 120	en_US
dc.identifier.isbn	9781538636787	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134167
dc.description.abstract	© 2018 IEEE. This paper investigates the optimal input and capacity of non-coherent correlated multiple-input singleoutput (MISO) channels in fast Rayleigh fading under per-antenna power constraints. Toward this end, we first establish the convex and compact properties of the feasible sets, and demonstrate the existence of the optimal input distribution and the uniqueness of the optimal effective magnitude input distribution. By exploiting the solutions of a quadratic optimization problem, we show that the Kuhn-Tucker condition (KTC) on the optimal inputs can be simplified to a single dimension. As a result, we can apply the Identity Theorem to show the discrete and finite nature of the optimal effective magnitude distribution. By using this distribution, we then construct a finite and discrete optimal input vector distribution. The use of this input allows us to determine precisely the capacity gain of MISO over SISO via the phase solutions of a non-convex constrained quadratic optimization problem on a sphere. These phase solutions can be calculated effectively via a proposed penalized optimization algorithm.	en_US
dc.relation.ispartof	2018 IEEE 7th International Conference on Communications and Electronics, ICCE 2018	en_US
dc.relation.isbasedon	10.1109/CCE.2018.8465760	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	On optimal input and capacity of non-coherent correlated MISO channels under per-antenna power constraints	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:

© 2018 IEEE. This paper investigates the optimal input and capacity of non-coherent correlated multiple-input singleoutput (MISO) channels in fast Rayleigh fading under per-antenna power constraints. Toward this end, we first establish the convex and compact properties of the feasible sets, and demonstrate the existence of the optimal input distribution and the uniqueness of the optimal effective magnitude input distribution. By exploiting the solutions of a quadratic optimization problem, we show that the Kuhn-Tucker condition (KTC) on the optimal inputs can be simplified to a single dimension. As a result, we can apply the Identity Theorem to show the discrete and finite nature of the optimal effective magnitude distribution. By using this distribution, we then construct a finite and discrete optimal input vector distribution. The use of this input allows us to determine precisely the capacity gain of MISO over SISO via the phase solutions of a non-convex constrained quadratic optimization problem on a sphere. These phase solutions can be calculated effectively via a proposed penalized optimization algorithm.

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http://hdl.handle.net/10453/134167