Signal Processing for Joint Communication and Radar Sensing Techniques in Autonomous Vehicular Networks

Luo, Yuyue

Signal Processing for Joint Communication and Radar Sensing Techniques in Autonomous Vehicular Networks

Luo, Yuyue

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Publication Type:: Thesis
Issue Date:: 2019

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Field	Value	Language
dc.contributor.author	Luo, Yuyue
dc.date.accessioned	2020-08-20T03:23:17Z
dc.date.available	2020-08-20T03:23:17Z
dc.date.issued	2019
dc.identifier.uri	http://hdl.handle.net/10453/142253
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_AU
dc.description.abstract	Joint communication and radar (radio) sensing (JCAS, also known as Radar-Communications) technology is promising for autonomous vehicular networks, for its appealing capability of realizing communication and radar sensing functions in an integrated system. Millimeter wave (mmWave) band has great potential forJCAS, and such mmWave systems often require the use of steerable array radiation beams. Therefore, beamforming (BF) is becoming a demanding feature in JCAS. Multibeam technology enables the use of two or more subbeams in JCAS systems,to meet different requirements of beamwidth and pointing directions. Generating and optimizing multibeam subject to the requirements is critical and challenging, particularly for systems using analog arrays. In this thesis, we investigate the BF techniques for JCAS, addressing the following two issues: 1. The multibeam generation and optimization for JCAS, considering both communication and sensing performance; 2. BF generation in the presence of hardware imperfections in mmWave JCASsystems, particularly those associated with quantized phase shifters, and the radiation characteristics of antenna arrays. Regarding the first issue, we mainly study two classes of multibeam generation methods: 1) the optimal combination of two pre-generated subbeams, and their BF vectors, using a combining phase coefficient; 2) global optimization methods which directly find solutions for a single BF vector. For the optimal combination problems, we firstly study the communication-focused optimization in two typical scenarios. We also develop constrained optimization problems, considering both the communication and sensing performances. Closed-form solutions for the optimal combination coefficient are provided in these works. We also formulate several global optimization problems and managed to provide near-optimal solutions to the original intractable complex NP-hard optimization problems, using semidefinite relaxation (SDR) techniques. Towards the second issue, we firstly study the quantization of the BF weight vector with the use of phase shifters. We focus on the two-phase-shifter array, where two phase shifters are used to represent each BF weight. We propose novel joint quantization methods by combining the codebooks of the two phase shifters. Analytically, the mean squared quantization error (MSQE) is derived for various quantization methods. We also propose BF methods by embedding the active pattern of antennas in the robust BF algorithms: 1) the diagonal loading and 2) the worst-case performance optimization algorithms. With the use of a more accurate array model, these methods can significantly reduce performance degradation caused by inconsistency between hypothesized ideal array models and practical ones.	en_AU
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/142253/2/02whole.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	Signal Processing for Joint Communication and Radar Sensing Techniques in Autonomous Vehicular Networks	en_AU
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

Joint communication and radar (radio) sensing (JCAS, also known as Radar-Communications) technology is promising for autonomous vehicular networks, for its appealing capability of realizing communication and radar sensing functions in an integrated system. Millimeter wave (mmWave) band has great potential forJCAS, and such mmWave systems often require the use of steerable array radiation beams. Therefore, beamforming (BF) is becoming a demanding feature in JCAS. Multibeam technology enables the use of two or more subbeams in JCAS systems,to meet different requirements of beamwidth and pointing directions. Generating and optimizing multibeam subject to the requirements is critical and challenging, particularly for systems using analog arrays. In this thesis, we investigate the BF techniques for JCAS, addressing the following two issues: 1. The multibeam generation and optimization for JCAS, considering both communication and sensing performance; 2. BF generation in the presence of hardware imperfections in mmWave JCASsystems, particularly those associated with quantized phase shifters, and the radiation characteristics of antenna arrays. Regarding the first issue, we mainly study two classes of multibeam generation methods: 1) the optimal combination of two pre-generated subbeams, and their BF vectors, using a combining phase coefficient; 2) global optimization methods which directly find solutions for a single BF vector. For the optimal combination problems, we firstly study the communication-focused optimization in two typical scenarios. We also develop constrained optimization problems, considering both the communication and sensing performances. Closed-form solutions for the optimal combination coefficient are provided in these works. We also formulate several global optimization problems and managed to provide near-optimal solutions to the original intractable complex NP-hard optimization problems, using semidefinite relaxation (SDR) techniques. Towards the second issue, we firstly study the quantization of the BF weight vector with the use of phase shifters. We focus on the two-phase-shifter array, where two phase shifters are used to represent each BF weight. We propose novel joint quantization methods by combining the codebooks of the two phase shifters. Analytically, the mean squared quantization error (MSQE) is derived for various quantization methods. We also propose BF methods by embedding the active pattern of antennas in the robust BF algorithms: 1) the diagonal loading and 2) the worst-case performance optimization algorithms. With the use of a more accurate array model, these methods can significantly reduce performance degradation caused by inconsistency between hypothesized ideal array models and practical ones.

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