CAREER: Signal Processing for Practical Data Communication over the Doubly-Selective Wireless Channel
CAREER: Signal Processing for Practical Data Communication over the Doubly-Selective Wireless Channel
批准号:
0237037
负责人:
Philip Schniter
金额:
$39.98万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2003
资助国家:
美国
项目状态:
已结题
起止时间:
2003-07-01 至 2009-06-30
中文摘要
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英文摘要
ABSTRACT0237037Philip SchmiterOhio State UniversityThe rapidly evolving global information infrastructure includes broadband wireless communication as a key component. Communication technology of the future will need to operate at the data rates, at high carrier frequencies, in high mobility situations, and under high levels of co-channel interference, all while maintaining reliable data transmission using small and inexpensive components. The fundamental challenge for future wireless technology stems from the harsh characteristics of the wireless propagation channel.In specific, reflections from objects in the physical environment induce rapidly changing echo patterns which make it difficult for the receiver to recover the transmitted information without error. Especially challenging are doubly-selective channels, i.e., channels which echo or blur the transmitted information significantly in both the time and frequency domains. Since, these characteristics arise when high-rate information streams are propagated at high frequencies in highly mobile environments, the success of future wireless applications hinges on our ability to communicate information reliably over the doubly-selective channel.This research investigates practical signaling, detection, and estimation strategies for spectrally-efficient broadband data communication over doubly-selective wireless channels. Orthogonal frequency division multiplexing, known for its advantages in time-non-selective fading, is used as a starting point in the study of practical data detection and channel estimation methods. Extending these ideas, the investigators focus on non-orthogonal multicarrier signaling schemes which are specifically designed to optimize detection performance under receiver complexity constraints.
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Collaborative Research: CIF: Medium: Learning and Inference in High-Dimensional Models: Rigorous Analysis and Applications
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批准号:1955587
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资助金额:$44.99万
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财政年份:2020
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负责人:Philip Schniter
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依托单位:
Approximate Message Passing Algorithms and Networks
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批准号:1716388
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依托单位:
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批准号:1527162
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资助金额:$24.71万
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财政年份:2015
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依托单位:
Message-Passing Strategies for High-Dimensional Inference
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批准号:1218754
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资助金额:$16.21万
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财政年份:2012
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负责人:Philip Schniter
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依托单位:
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批准号:1018368
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项目类别:Standard Grant
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资助金额:$42.3万
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财政年份:2010
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负责人:Philip Schniter
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