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

在本项目中，将开发Wi-Fi网络中信道状态信息（CSI）的有效近似的理论和应用，它描述了无线链路的关键特征。该近似值将利用CSI的底层结构，只需要很少的参数，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，这将显著提高Wi-Fi网络的效率，并使越来越依赖于无线网络的高速和可靠性的教育、公共卫生和商业领域的更多应用和更多机会。将解决的基本问题包括：找到近似的理论解释，设计快速CSI压缩算法，设计更有效的CSI测量和预测方法，以及设计新的数据传输技术。在这个项目中获得的知识将通过揭示以前未被注意到的大类无线信道的重要特征，并为这些信道开发优化的方法来推进研究领域。在这个项目中获得的结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，来自代表性不足和少数群体的学生将积极接触和招募。这个项目的动机是一个有趣的实验发现，它表明Wi-Fi网络中的CSI向量在许多情况下可以很好地近似为很少的正弦波的线性组合，例如3，即使不可忽略路径的数量要大得多。参考以稀疏正弦（SPS）近似为SPS通道的信道，本项目的目标包括：1)通过研究信道的数学性质和设计快速的CSI压缩算法，了解MIMO信道SPS近似存在的理论基础；2)利用SPS近似的底层结构，设计高效的CSI测量和预测方法；3)利用信道的简化表示，设计新的SPS信道数据调制技术。所提出的算法和技术将在实验平台上实施，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带来的瓶颈，本研究的成果将为Wi-Fi网络提供及时的解决方案，以更好地支持MU-MIMO或大规模MIMO。SPS信道的新数据调制技术有望在降低复杂性的同时提高链路速度。

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.