SpecEES: Spatio-Spectral Sensing with Wideband Feature Extraction Arrays

SPASS：利用宽带特征提取阵列进行空间-频谱感知对更高数据速率的需求不断增长，加上物联网（IoT）驱动的无线设备数量的快速增加，将不可避免地导致射频（RF）频谱的干扰。由于带宽是稀缺资源，因此频谱感测，即，为了机会性地重新使用这些资源而识别未使用的频率将是未来无线系统的关键组成部分。虽然现代无线系统已经依赖于天线阵列，通过重新使用空间中不同位置的频率来提高频谱效率，但很少有人关注空间中未使用的频谱的感知和重新使用。该项目建立在低功耗宽带收发器设计，宽带压缩感知和多天线无线通信的最新进展，以执行SPAtio-Spectral Sensing（SPASS）。SPASS能够识别频率和空间中未使用的资源，这可用于优化无线系统的频谱和能量效率。该项目追求跨越电路设计，理论和算法以及系统设计的垂直集成研究方法，以展示SPASS的有效性和局限性。这项工作产生的更广泛的影响包括一个广泛的推广计划，涉及来自拉丁美洲的本科生在这项研究中，越来越多的女高中生与康奈尔大学的居里学院的参与。通过非均匀小波采样（NUWS），最近推出的采样范式专门为RF特征提取多天线频谱传感，是这个项目的核心概念。这里探讨的关键思想是使用NUWS电路在相干天线阵列中提取频谱特征。这些功能，然后用于识别未使用的资源，在频率和空间的专门的空白检测算法的手段。该项目的重点是一个整体的和实用的方法来实现一个完整的和可用的SPASS系统;这是通过融合算法的设计与混合信号电路的设计多天线NUWS，提取宽带频谱特征直接从RF信号中的高灵敏度在一个节能的方式。为了识别未使用的资源，将开发利用频率和空间中频谱活动的稀疏和低秩结构的白空间检测算法。电路原型和算法将用于执行真实世界的RF测量，从而实现（i）对基于NUWS的SPASS的系统级权衡的基本分析，以及（ii）对现实物联网通信场景中未使用的资源的调查，揭示了在频率和空间中重组RF频谱的潜力。待开发的电路和算法可用于依赖于从多通道时域信号中提取频谱特征的广泛其他应用。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

SPASS: Spatio-Spectral Sensing with Wideband Feature Extraction ArraysThe ever-growing demand for higher data-rates combined with the rapid increase in the number of wireless devices driven by the Internet of Things (IoT), will inevitably cause congestions in the Radio-Frequency (RF) spectrum. Since bandwidth is a scarce resource, spectrum sensing, i.e., the identification of unused frequencies in order to opportunistically re-use these resources, will be a critical component of future wireless systems. While modern wireless systems already rely on antenna arrays to improve spectral efficiency by re-using frequencies at different locations in space, only little attention has been given to sensing and re-using unused spectrum in space. This project builds upon recent advances in low-power wideband transceiver design, wideband compressive sensing, and multi-antenna wireless communication in order to perform SPAtio-Spectral Sensing (SPASS). SPASS enables the identification of unused resources in both frequency and space, which can be used to optimize the spectral- and energy-efficiency of wireless systems. This project pursues a vertically-integrated research approach spanning circuit design, theory and algorithms, and system design in order to demonstrate the efficacy and limits of SPASS. Broader impacts resulting from this work include an extensive outreach plan involving undergraduate students from Latin America in this research and increasing participation of female high-school students with Cornell's CURIE Academy.Multi-antenna spectrum sensing via NonUniform Wavelet Sampling (NUWS), a recently introduced sampling paradigm specifically designed for RF feature extraction, is the core concept of this project. The key idea explored here is to use NUWS circuitry to extract spectral features at a coherent antenna array. These features are then used to identify unused resources in both frequency and space by means of specialized white-space detection algorithms. The project focuses on a holistic and practical approach to realizing a full and usable SPASS system; this is done by fusing the design of algorithms with the design of mixed-signal circuits for multi-antenna NUWS that extract wideband spectral features with high sensitivity directly from RF signals in an energy-efficient manner. To identify unused resources, white-space detection algorithms that exploit the sparse and low-rank structure of spectral activity in both frequency and space will be developed. The circuit prototypes and algorithms will be used to perform real-world RF measurements, which enable (i) a fundamental analysis of the system-level tradeoffs of NUWS-based SPASS and (ii) an investigation of the unused resources in realistic IoT communication scenarios that reveal the potential of reorganizing the RF spectrum in frequency and space. The circuits and algorithms to be developed find use in a broad range of other applications that rely on the extraction of spectral features from multi-channel time-domain signals.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.