Wireless Off-body Channel Analysis and Sparse Modeling

Cui, Pengfei

Wireless Off-body Channel Analysis and Sparse Modeling

Cui, Pengfei

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Publication Type:: Thesis
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Cui, Pengfei
dc.date.accessioned	2020-09-01T23:40:08Z
dc.date.available	2020-09-01T23:40:08Z
dc.date.issued	2020
dc.identifier.uri	http://hdl.handle.net/10453/142477
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_AU
dc.description.abstract	The successful application of very rapidly growing wearable devices relies on the research on the propagation characteristics of off-body channels which plays a key role in connecting the wireless body area network and cellular network, WiFi and other local area networks. This thesis concentrates on the bottleneck problems of the measurement, analysis and modeling of the off-body propagation characteristics. A large number of measurement investigations have been carried out to solve the thorny problem of complicated and changeable scenes of off-body channel and heavy fading caused by adjacent humans. These activities include different transmission schemes, different influence factors, and typical changeable configurations. Then, in this study, the systematic analysis of the measured big channel datasets are conducted based on traditional large/small scale propagation analysis methods and compressive sensing based sparse channel analysis methods. The first part of the thesis discusses the measurement and analysis of typical off-body channel types including single input single output (SISO), diversity reception and multiple input multiple output (MIMO). A two-factor integrated path loss model with variable body worn locations and variable access point (AP) height is proposed to improve the power management and link budgeting ability in off-body scenarios. A highly robust circularly polarized spatial diversity off-body scheme is made up and validated to tackle the heavy fading problem. In addition, the influences of humans including both hand-held effect and body obstruction effect on off-body transmission angular spectrum and capacity are estimated. In the second part of the thesis, the novel compressive sensing based sparse channel analysis methods are proposed to deal with the modeling problems of off-body temporal channels with complex multipath components. The channel impulse response (CIR) models of SISO and MIMO channels based on single measurement vector (SMV) and multi-measurement vector (MMV-CS) compressive sensing methods respectively are established. Finally, according to the off-body link types, the propagation characteristics, sparse analysis and modeling methods are integrated into several channel simulators with friendly GUI interface, whose source codes are shared on gitHub. Those models and simulators are expected to be used in theoretical analysis and engineering practice for the coverage planning, link simulation, algorithm design, and performance validation.	en_AU
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/142477/2/02whole.pdf
dc.rights	au.edu.uts.lib/ppc
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.rights	info:eu-repo/semantics/openAccess
dc.title	Wireless Off-body Channel Analysis and Sparse Modeling	en_AU
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

The successful application of very rapidly growing wearable devices relies on the research on the propagation characteristics of off-body channels which plays a key role in connecting the wireless body area network and cellular network, WiFi and other local area networks. This thesis concentrates on the bottleneck problems of the measurement, analysis and modeling of the off-body propagation characteristics. A large number of measurement investigations have been carried out to solve the thorny problem of complicated and changeable scenes of off-body channel and heavy fading caused by adjacent humans. These activities include different transmission schemes, different influence factors, and typical changeable configurations. Then, in this study, the systematic analysis of the measured big channel datasets are conducted based on traditional large/small scale propagation analysis methods and compressive sensing based sparse channel analysis methods. The first part of the thesis discusses the measurement and analysis of typical off-body channel types including single input single output (SISO), diversity reception and multiple input multiple output (MIMO). A two-factor integrated path loss model with variable body worn locations and variable access point (AP) height is proposed to improve the power management and link budgeting ability in off-body scenarios. A highly robust circularly polarized spatial diversity off-body scheme is made up and validated to tackle the heavy fading problem. In addition, the influences of humans including both hand-held effect and body obstruction effect on off-body transmission angular spectrum and capacity are estimated. In the second part of the thesis, the novel compressive sensing based sparse channel analysis methods are proposed to deal with the modeling problems of off-body temporal channels with complex multipath components. The channel impulse response (CIR) models of SISO and MIMO channels based on single measurement vector (SMV) and multi-measurement vector (MMV-CS) compressive sensing methods respectively are established. Finally, according to the off-body link types, the propagation characteristics, sparse analysis and modeling methods are integrated into several channel simulators with friendly GUI interface, whose source codes are shared on gitHub. Those models and simulators are expected to be used in theoretical analysis and engineering practice for the coverage planning, link simulation, algorithm design, and performance validation.

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http://hdl.handle.net/10453/142477