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NeTS: Large: Collaborative Research: Foundations of Hierarchical Full-Duplex Wireless Networks

NeTS: Large: Collaborative Research: Foundations of Hierarchical Full-Duplex Wireless Networks
NeTS:大型:协作研究:分层全双工无线网络的基础
批准号:
1314538
负责人:
Ness Shroff
金额:
$39.01万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2013
资助国家:
美国
项目状态:
已结题
起止时间:
2013-07-01 至 2020-06-30

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中文摘要
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英文摘要
This project will develop foundational principles for hierarchical wireless network design by leveraging full-duplex transmissions in both access and wireless backhaul. Full-duplex is most promising at shorter ranges, and hence is fortuitously aligned with the predicted dominant access range in future networks. Furthermore, larger full-duplex ranges are feasible in infrastructure-to-infrastructure links, and hence are well suited for backhaul links. While full-duplex is well-aligned with the key elements of hierarchical networks, our current design principles are largely developed for half-duplex transmissions which is the basis for all current networks. With that in mind, this project will address both theory and protocols for hierarchical full-duplex networks by looking at: (1) data-driven signal models for self-interference caused by the node's own transmission to its own receiver, (2) theoretical foundations for scheduling and routing that leverage both self-interference and multi-hop interference cancellation; and (3) protocols and prototypes for network scale full-duplex resource management. Full-duplex breaks one of the basic design constraints in current wireless networks, all of which are either half-duplex in time or frequency; it will therefore rewrite wireless networking fundamentals. Further, with emphasis on realizable networks using extensive Rice University's programmable testbeds, the project will impact the next-generation of wireless networks via its corporate partners. Finally, the project team will establish a unique inter-university education and research program, which will include joint advising and collaborative advising and leverage the team's complementary expertise.
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Collaborative Research: NeTS: Medium: Black-box Optimization of White-box Networks: Online Learning for Autonomous Resource Management in NextG Wireless Networks
  • 批准号:
    2312836
  • 项目类别:
    Standard Grant
  • 资助金额:
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    2023
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AI Institute for Future Edge Networks and Distributed Intelligence (AI-EDGE)
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