NeTS: Small: Beyond Listen-Before-Talk: Advanced Cognitive Radio Access Control in Distributed Multiuser Networks
NeTS: Small: Beyond Listen-Before-Talk: Advanced Cognitive Radio Access Control in Distributed Multiuser Networks
批准号:
0917251
负责人:
Xin Liu
金额:
$49.82万
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:
2009-09-01 至 2013-08-31
中文摘要
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英文摘要
The award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). Cognitive radio (CR) has the potential to improve spectrum utilization and expand wireless communication services by opportunistically utilizing underutilized spectrum bands. This project designs advanced cognitive radio access and power control algorithms that can achieve better spectrum efficiency while limiting interference to primary communications. Moving beyond the more traditional access strategies that rely only on secondary user (SU) spectral sensing to avoid collision with primary users (PUs), this research exploits various levels of primary network?s data link control (DLC) signaling and feedback information. Such DLC information is available in many practical wireless systems, such as transmission profile, receiver ACK/NACK, channel quality indicator, and power control information. Utilizing such information elevates the level of SU cognition. It provides more efficient spectrum sharing, better PU protection (especially in the presence of multiple distributed SUs), and multiple levels of SU and PU interaction. The major outcomes include: 1) Distributed multi-SU cognitive access and power control based on PU receiver feedback information; 2) Optimal algorithms for distributed multi-SU access control in multi-channel cognitive environments; 3) Cognitive radio access robust to PU behavioral changes and incomplete PU feedback information; and 4) Hierarchical cognitive radio networks of users with varying degrees of cognition. This significantly broadens the future applications of wireless services in areas with limited open spectrum. The plan recruits students, especially from under-represented groups, and integrates the results into the classes for computer science and electrical engineering majors.
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