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Optimizations on Estimation and Positioning Techniques in Intelligent Wireless Systems
Optimizations on Estimation and Positioning Techniques in Intelligent Wireless Systems
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211153131
- ISBN
- 9798346830184
- DDC
- 515
- 저자명
- Oh, Myeung Suk.
- 서명/저자
- Optimizations on Estimation and Positioning Techniques in Intelligent Wireless Systems
- 발행사항
- [Sl] : Purdue University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 175 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
- 주기사항
- Advisor: Brinton, Christopher G.;Love, David J.
- 학위논문주기
- Thesis (Ph.D.)--Purdue University, 2024.
- 초록/해제
- 요약Wireless technologies across various applications aim to improve further by developing intelligent systems, where the performance is optimized through adaptive policy selections that efficiently adjust to the environment dynamics. As a result, accurate observation on the surrounding conditions, such as wireless channel quality and relative target location, becomes an important task. Although both channel estimation and wireless positioning problems have been well studied, with advanced wireless communications relying on complex technologies and being applied to diverse environments, optimization strategies tailored to their unique architectures and scenarios need to be further investigated. In this dissertation, four key research problems related to channel estimation and wireless positioning tasks for intelligent wireless systems are identified and studied. First, a channel denoising problem in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems is addressed, and a Q-learning-based successive denoising scheme, which utilizes a channel curvature magnitude threshold to recover unreliable channel estimates, is proposed. Second, a pilot assignment problem in scalable open radio access network (O-RAN) cell-free massive MIMO (CFmMIMO) systems is studied, where a low-complexity pilot assignment scheme based on a multi-agent deep reinforcement learning (MA-DRL) framework along with a codebook search strategy is proposed. Third, sensor selection/placement problems for wireless positioning are addressed, and dynamic and robust sensor selection schemes that minimize the Cramer-Rao lower bound (CRLB) are proposed. Lastly, a feature selection problem for deep learning-based wireless positioning is studied, and a unique feature size selection method, which weights over the expected information gain and classification capability, along with a multi-channel positioning neural network is proposed.
- 일반주제명
- Convex analysis
- 일반주제명
- Feature selection
- 일반주제명
- Deep learning
- 일반주제명
- Assignment problem
- 일반주제명
- Intelligent systems
- 일반주제명
- Neural networks
- 일반주제명
- Support vector machines
- 일반주제명
- Computer science
- 기타저자
- Purdue University.
- 기본자료저록
- Dissertations Abstracts International. 86-06B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211153131
■006m o d
■007cr#unu||||||||
■020 ▼a9798346830184
■035 ▼a(MiAaPQ)AAI31786081
■035 ▼a(MiAaPQ)Purdue25675002
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a515
■1001 ▼aOh, Myeung Suk.
■24510▼aOptimizations on Estimation and Positioning Techniques in Intelligent Wireless Systems
■260 ▼a[Sl]▼bPurdue University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a175 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-06, Section: B.
■500 ▼aAdvisor: Brinton, Christopher G.;Love, David J.
■5021 ▼aThesis (Ph.D.)--Purdue University, 2024.
■520 ▼aWireless technologies across various applications aim to improve further by developing intelligent systems, where the performance is optimized through adaptive policy selections that efficiently adjust to the environment dynamics. As a result, accurate observation on the surrounding conditions, such as wireless channel quality and relative target location, becomes an important task. Although both channel estimation and wireless positioning problems have been well studied, with advanced wireless communications relying on complex technologies and being applied to diverse environments, optimization strategies tailored to their unique architectures and scenarios need to be further investigated. In this dissertation, four key research problems related to channel estimation and wireless positioning tasks for intelligent wireless systems are identified and studied. First, a channel denoising problem in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems is addressed, and a Q-learning-based successive denoising scheme, which utilizes a channel curvature magnitude threshold to recover unreliable channel estimates, is proposed. Second, a pilot assignment problem in scalable open radio access network (O-RAN) cell-free massive MIMO (CFmMIMO) systems is studied, where a low-complexity pilot assignment scheme based on a multi-agent deep reinforcement learning (MA-DRL) framework along with a codebook search strategy is proposed. Third, sensor selection/placement problems for wireless positioning are addressed, and dynamic and robust sensor selection schemes that minimize the Cramer-Rao lower bound (CRLB) are proposed. Lastly, a feature selection problem for deep learning-based wireless positioning is studied, and a unique feature size selection method, which weights over the expected information gain and classification capability, along with a multi-channel positioning neural network is proposed.
■590 ▼aSchool code: 0183.
■650 4▼aConvex analysis
■650 4▼aFeature selection
■650 4▼aDeep learning
■650 4▼aAssignment problem
■650 4▼aIntelligent systems
■650 4▼aNeural networks
■650 4▼aSupport vector machines
■650 4▼aComputer science
■690 ▼a0984
■71020▼aPurdue University.
■7730 ▼tDissertations Abstracts International▼g86-06B.
■790 ▼a0183
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17165163▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
