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CIF:Medium:Collaborative Research:Understanding and Managing Interference in Communications Networks

CIF:Medium:Collaborative Research:Understanding and Managing Interference in Communications Networks
CIF:中:协作研究:理解和管理通信网络中的干扰
批准号:
0904305
负责人:
Lizhong Zheng
金额:
$30.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2009
资助国家:
美国
项目状态:
已结题
起止时间:
2009-07-15 至 2013-06-30

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中文摘要
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英文摘要
The ubiquity of wireless devices and services and the ever increasing bandwidth demand make it imperative to improve spectrum utilization. Key to improving spectrum efficiency in a multi-user networks is understanding and managing interference. This collaborative project studies the phenomenon of interference in communication networks and develops a theoretical foundation on how to deal with interference under realistic operating conditions. The study addresses several long-standing open problems; solutions to those problems should collectively advance our understanding on interference and provide guidance on the design of future wireless networks. This project pursues a broad range of topics that are of great theoretical and practical significance. Conventional wisdom suggests that, since interference has structure that is not present in thermal noise, this structure should be exploited by transceivers. On the other hand, there are situations where interference can be essentially ignored without compromising system throughput, as evidenced by recent breakthroughs in the study of the sum-rate capacity of Gaussian interference channels. This project demonstrates that there are different regimes for interference management and provide means of analysis for various multi-user communication systems. The transformative nature of this project lies in its ambitious goal of establishing a solid theoretical foundation on how interference should be dealt with in complex networks, and in providing guidance on system designs that achieve optimal performance.
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Collaborative Research: MLWiNS: Deep Neural Networks Meet Physical Layer Communications -- Learning with Knowledge of Structure
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EAGER: Theoretic Structures of High Dimensional Data Decomposition
CIF: SMALL: The Linear Information Coupling Problem
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