IHCS: Collaborative Research: Compressive Spectrum Sensing in Cognitive Radio Networks

IHCS：协作研究：认知无线电网络中的压缩频谱感知

• 批准号: 1028790
• 负责人: Yin Zhang
• 金额: $ 25万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1028790&HistoricalAwards=false
• 关键词: IHCS; Collaborative; Research; Compressive; Spectrum

The objective of this research is to improve design of collaboratively discovering unused spectrum in a revolutionary wireless communication paradigm, the cognitive-radio network, in which cognitive-radio users can detect and share the unused spectrum. The proposed approach is to apply collaborative compressive sensing to increase spectrum sensing bandwidth, speed, and accuracy. Specifically, the cognitive radios, rather than sweeping a set of channels sequentially, will sense linear combinations of the powers of multiple channels and report them to the fusion center, where the occupied channels are recovered using compressive sensing algorithms. Missing and erroneous reports can be exactly recovered by matrix completion since the matrix of all reports has a low-rank. Prior knowledge of channel gains is not required. The system computes more but senses much less and faster, which will be validated by both numerical and USRP2-based simulations.The proposed research is potentially transformative as the novel framework and algorithms will broadly apply to signal sensing involving multiple sensors, modalities, and data sources. This research will have a broader impact on several audiences. The study of the jointly-sparse signal reconstruction will contribute to researchers working in compressive sensing and wireless networks. The hardware implementation will bring fresh ideas to the industrial community. The proposed research will be integrated into the existing combined education/research effort at the University of Houston and Rice University, improve education of under-represented minorities at the two institutions, and expose students to state-of-the-art research in wireless networks and compressive sensing through the NSF sponsored VIGRE program.

本研究的目的是改进革命性无线通信范式（认知无线电网络）中协作发现未使用频谱的设计，其中认知无线电用户可以检测和共享未使用频谱。所提出的方法是应用协作压缩感知来提高频谱感知带宽、速度和准确性。具体来说，认知无线电不是顺序扫描一组信道，而是感知多个信道功率的线性组合并将其报告给融合中心，在融合中心使用压缩感知算法恢复占用的信道。由于所有报告的矩阵具有低秩，因此可以通过矩阵补全来准确地恢复丢失和错误的报告。不需要预先了解信道增益。该系统计算更多，但感知更少且速度更快，这将通过数值和基于 USRP2 的模拟进行验证。拟议的研究具有潜在的变革性，因为新颖的框架和算法将广泛应用于涉及多个传感器、模式和数据的信号感知来源。这项研究将对一些受众产生更广泛的影响。 联合稀疏信号重建的研究将为压缩感知和无线网络领域的研究人员做出贡献。硬件实现将为工业界带来新的想法。拟议的研究将整合到休斯顿大学和莱斯大学现有的联合教育/研究工作中，改善两所机构中代表性不足的少数族裔的教育，并使学生接触到无线网络领域最先进的研究以及通过 NSF 赞助的 VIGRE 计划进行压缩传感。