NeTS: Small: Theory and Applications of Sparse Approximations of the Channel State Information in Wi-Fi Networks

NeTS：Small：Wi-Fi 网络中信道状态信息稀疏近似的理论与应用

• 批准号: 1618358
• 负责人: Zhenghao Zhang
• 金额: $ 36.08万
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1618358&HistoricalAwards=false
• 关键词: NeTS; Small; Theory; Applications; Sparse

In this project, theory and applications of efficient approximations of the Channel State Information (CSI) in Wi-Fi networks, which describes the key characteristics of the wireless links, will be developed. The approximation will exploit the underlying structure of the CSI and will require very few parameters, resulting in highly effective CSI compression and measurement methods, as well as new data transmission techniques, which will significantly improve the efficiency of Wi-Fi networks and enable more applications and more opportunities in education, public health, and business that increasingly depend on the high speed and reliability of wireless networks. Fundamental issues that will be addressed include: finding the theoretical explanation of the approximation, designing fast CSI compression algorithms, designing more efficient CSI measurement and prediction methods, and designing novel data transmission techniques. The knowledge gained in this project will advance the research field by revealing important features of a large class of wireless channels that were previously unnoticed, and developing optimized methods for such channels. Results obtained in this project will be used in classes related to networking. Both graduate and undergraduate students will participate in this project, and students from underrepresented and minority groups will be actively reached out to and recruited. This project is motivated by an interesting experimental discovery, which shows that the CSI vectors in Wi-Fi networks can be approximated very well in many case as the linear combination of very few, such as 3, sinusoids, even when the number non-negligible paths are much larger. Referring to channels with such SParse Sinusoid (SPS) approximation as SPS channels, the goals of this project include: 1) understanding the theoretical foundation regarding to the existence of the SPS approximation for MIMO channels by studying the mathematical properties of channel and designing fast CSI compression algorithms, 2) designing efficient CSI measurement and prediction methods by exploiting underlying structure of the SPS approximation, and 3) designing new data modulation techniques for SPS channels by exploiting the simplified representation of the channel. The proposed algorithms and techniques will be implemented in experimental platforms and tested in real-world wireless channels. By removing the bottleneck caused by the high overhead in CSI feedback and measurement, the outcomes of this research will be timely solutions for Wi-Fi networks for better supporting MU-MIMO or massive MIMO. The new data modulation techniques for SPS channels will likely improve the link speed while reducing the complexity.

在该项目中，将开发 Wi-Fi 网络中描述无线链路关键特性的信道状态信息 (CSI) 的有效近似的理论和应用。该近似将利用CSI的底层结构，并且需要很少的参数，从而产生高效的CSI压缩和测量方法以及新的数据传输技术，这将显着提高Wi-Fi网络的效率并启用更多应用教育、公共卫生和商业领域的更多机会越来越依赖于无线网络的高速和可靠性。将解决的基本问题包括：寻找近似的理论解释、设计快速CSI压缩算法、设计更有效的CSI测量和预测方法以及设计新颖的数据传输技术。该项目中获得的知识将通过揭示以前未被注意到的一大类无线信道的重要特征并开发此类信道的优化方法来推动研究领域的发展。该项目获得的成果将用于与网络相关的课程。研究生和本科生都将参与该项目，并且将积极接触和招募来自代表性不足和少数群体的学生。该项目的动机是一个有趣的实验发现，该发现表明 Wi-Fi 网络中的 CSI 向量在许多情况下可以很好地近似为极少数（例如 3 个）正弦曲线的线性组合，即使数量不可忽略路径要大得多。参考具有SParse正弦曲线（SPS）近似的信道作为SPS信道，该项目的目标包括：1）通过研究信道的数学特性和设计快速CSI，了解MIMO信道SPS近似存在的理论基础压缩算法，2）通过利用 SPS 近似的底层结构来设计有效的 CSI 测量和预测方法，以及 3）通过利用信道的简化表示来为 SPS 信道设计新的数据调制技术。所提出的算法和技术将在实验平台中实施，并在现实世界的无线信道中进行测试。通过消除 CSI 反馈和测量的高开销造成的瓶颈，这项研究的成果将成为 Wi-Fi 网络的及时解决方案，以更好地支持 MU-MIMO 或大规模 MIMO。 SPS 信道的新数据调制技术可能会提高链路速度，同时降低复杂性。