Optimized resource allocation in wireless systems

Rashid, U

Optimized resource allocation in wireless systems

Rashid, U

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Publication Type:: Thesis
Issue Date:: 2013

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Field	Value	Language
dc.contributor.author	Rashid, U
dc.date.accessioned	2013-09-04T06:26:19Z
dc.date.available	2013-09-04T06:26:19Z
dc.date.issued	2013
dc.identifier.uri	http://hdl.handle.net/10453/23491
dc.description	University of Technology, Sydney. Faculty of Engineering and Information Technology.	en_US
dc.description.abstract	Modern wireless systems rely to a great extent on the judicious distribution of available resources (e.g. power, bandwidth) to meet an ever increasing demand of better quality-of-service (QoS). Scarcity of these resources with time, coupled with the tremendous growth in numbers of users, network throughput, and applications, have resulted in making the problem of optimal resource allocation extremely important especially in wireless networks. Generally, optimization problems posed in the resource allocation framework are nonconvex and thus render it difficult to find an optimal solution. Previous studies on this subject have reported only numerically cumbersome and non-tractable solutions. This dissertation attempts to exploit the hidden convexity of the resource allocation problems under some given performance criteria such as minimum mean square error (MMSE) or signal-to-interference-plus-noise ratio (SINR) and then successfully finds tractable optimization formulations. The first research problem deals with the optimal power allocation and sensor assignment in linear and nonlinear networks for static and dynamic target tracking. The proposed method casts power allocation as a semi-definite program (SDP) while sensor selection is solved via d.c. (difference of convex functions/sets) programming. The second problem considers optimal beam-forming and source power allocation in relay-assisted multiuser communication. This problem is further extended to include multiple-antenna systems to exploit spatial diversity in modern cellular communication by jointly optimizing source precoding and relay processing matrices. Supremacy of the proposed d.c. programming based iterative algorithm over existing methods is demonstrated via extensive simulations.	en_US
dc.format	Thesis (PhD)	en_US
dc.language.iso	en	en_US
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/23491/6/02whole.pdf
dc.rights	au.edu.uts.lib/ppc
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.subject	Wireless sensor networks.	en
dc.subject	Convex optimization.	en_US
dc.subject	Multi-user communication.	en
dc.subject	Power allocation.	en
dc.title	Optimized resource allocation in wireless systems	en_US
dc.type	Thesis
utslib.copyright.status	open_access

Abstract:

Modern wireless systems rely to a great extent on the judicious distribution of available resources (e.g. power, bandwidth) to meet an ever increasing demand of better quality-of-service (QoS). Scarcity of these resources with time, coupled with the tremendous growth in numbers of users, network throughput, and applications, have resulted in making the problem of optimal resource allocation extremely important especially in wireless networks. Generally, optimization problems posed in the resource allocation framework are nonconvex and thus render it difficult to find an optimal solution. Previous studies on this subject have reported only numerically cumbersome and non-tractable solutions. This dissertation attempts to exploit the hidden convexity of the resource allocation problems under some given performance criteria such as minimum mean square error (MMSE) or signal-to-interference-plus-noise ratio (SINR) and then successfully finds tractable optimization formulations. The first research problem deals with the optimal power allocation and sensor assignment in linear and nonlinear networks for static and dynamic target tracking. The proposed method casts power allocation as a semi-definite program (SDP) while sensor selection is solved via d.c. (difference of convex functions/sets) programming. The second problem considers optimal beam-forming and source power allocation in relay-assisted multiuser communication. This problem is further extended to include multiple-antenna systems to exploit spatial diversity in modern cellular communication by jointly optimizing source precoding and relay processing matrices. Supremacy of the proposed d.c. programming based iterative algorithm over existing methods is demonstrated via extensive simulations.

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http://hdl.handle.net/10453/23491