NeTS NEDG: Dynamic Spectrum Access for Availability and Reliability
NeTS NEDG: Dynamic Spectrum Access for Availability and Reliability
批准号:
0832090
负责人:
Haitao Zheng
金额:
$29.27万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2008
资助国家:
美国
项目状态:
已结题
起止时间:
2008-09-01 至 2013-03-31
中文摘要
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英文摘要
Historical static spectrum allocation has led to an artificial spectrum scarcity, leaving no usable spectrum for future wireless networks. Dynamic spectrum access is the ideal solution to break such scarcity and make spectrum available to new wireless networks. However, without providing proper reliability guarantees, dynamic spectrum access is unacceptable to many networks and services. Thus, instead of focusing solely on improving spectrum utilization, dynamic spectrum access should provide reliable spectrum usage that meets network's individual needs. This research develops SAFIRE, a robust architecture for dynamic spectrum access that provides reliable and efficient spectrum usage to large wireless networks. This research holds great practical values for wireless network designers and service providers who rely on available and reliable spectrum access to deploy and advance their networks.Recognizing the fundamental tradeoffs between spectrum utilization and reliability, this research focuses on efficient algorithms to meet individual networks? reliability requirements while improving spectrum utilization. The investigators will develop statistical mechanisms to proactively regulate spectrum demands based on their reliability requirements, and apply distributed coordination and cross-layer design to quickly adapt spectrum allocations and application patterns to network and spectrum dynamics. To support large-scale dynamic networks, this research focuses on computational-efficient mechanisms with minimum management overhead. These advancements will help attract large commercial interests into dynamic spectrum systems, facilitating their wide adoption. Finally, the investigators will use this research as a basis to develop interdisciplinary computer science training across both software and hardware layers.
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