NeTS: Small: Inducing and Exploiting Spectrum Predictability via Traffic Shaping and Mobility for Cognitive Communication in White Space

NeTS：小型：通过流量整形和移动性诱导和利用频谱可预测性，以实现空白空间中的认知通信

• 批准号: 1117462
• 负责人: Junshan Zhang
• 金额: $ 42.99万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117462&HistoricalAwards=false
• 关键词: NeTS; Small; Inducing; Exploiting; Spectrum

Spectrum opportunities in white space hinge heavily on the traffic patterns of the licensed users (PUs), and vary across space, time, and frequency. Making a paradigm shift, this project advocates to leverage traffic shaping and mobility patterns of PUs for inducing predictable structures of spectrum holes in the spatio-temporal domain, which in turn enables more efficient spectrum access by cognitive radio users. With such a common thread, this project will 1) study joint traffic shaping and network coding for PUs, as a spectrum shaper, to induce predictive structures in spectrum holes; 2) investigate SUs? cognitive transmissions via adaptive file fragmentation and predetermined file fragmentation that can match the characteristics of spectrum opportunities discovered on the fly; and 3) explore cognitive routing via exploiting PU-mobility predictability. Efficient spectrum usage will facilitate a wide variety of scientific and engineering applications and result in a significant impact on the society at large. This research will open a new direction for spectrum shaping that induces predictable structures of spectrum opportunities, which can then be exploited by SUs for effective cognitive communications. The findings will advance the state-of-the-art of cognitive radio networking and spur a new line of thinking. Another major task of this project is to integrate research with educational activities. In particular, the PIs will continue to involve under-represented and minority students in research.

白色空间中的频谱机会在很大程度上取决于许可用户（PU）的业务模式，并且在空间、时间和频率上变化。该项目提出了一种范式转变，即利用PU的流量整形和移动模式来诱导时空域中的频谱空洞的可预测结构，从而使认知无线电用户能够更有效地接入频谱。有了这样一个共同的线索，这个项目将1）研究联合流量整形和网络编码的PU，作为一个频谱整形器，诱导预测结构的频谱空洞; 2）调查SU？通过自适应文件分段和预定文件分段的认知传输，其可以匹配在运行中发现的频谱机会的特性;以及3）通过利用PU移动性可预测性来探索认知路由。高效的频谱使用将促进各种科学和工程应用，并对整个社会产生重大影响。这项研究将为频谱整形开辟一个新的方向，诱导频谱机会的可预测结构，然后可以利用SU进行有效的认知通信。这些发现将推动认知无线电网络的发展，并激发新的思维。 该项目的另一项主要任务是将研究与教育活动结合起来。特别是，PI将继续让代表性不足的学生和少数民族学生参与研究。