CAREER: Radio Frequency Spectrum Sensing with a Fine-Tooth Nanomechanical Comb

无线设备和物联网（IoT）应用的巨大增长给射频（RF）网络基础设施带来了巨大的压力，使信道拥挤，使射频频谱过度拥挤。电池供电的便携式智能设备，如智能手机、可穿戴技术（如智能手表和眼镜）、个人雷达和其他智能设备都在争夺带宽，需要有效利用频谱。为了应对日益迫近的射频频谱短缺，多频段、多标准的无线系统需要“感知”？射频频谱响应于现场频谱的利用，以最有效的方式被要求。软件解决方案，如机器学习技术，在射频信号数字化后应用，通常需要很大的计算能力。机器学习技术也面临着在动态无线环境中识别可用信道的实时学习和决策的挑战。射频信号处理和频谱传感是一种潜在的解决方案，可以辅助软件技术并放宽接收器规格。这个CAREER项目提供了射频频谱传感的变革性解决方案：一个芯片级的多ghz纳米机电频率梳发生器，与其他射频前端电子设备集成。该项目旨在扩大K-12和本科学生，特别是少数民族和女性学生在STEM领域的参与。该扩展计划将通过与佐治亚理工学院的扩展项目合作，使用教育工具包和动手模块。在频谱传感器的核心，拟议的紧凑型射频梳状发生器，利用多个梳状齿产生的并行处理来执行快速射频频谱传感，使用比当前最先进的硬件解决方案更低的功耗和更少的电路组件。提出的纳米机电频率梳发生器针对超高频范围（3-30 GHz），并利用体声波谐振器中压电膜厚度的急剧减少产生宽带频率梳的新能力。利用其小尺寸和高质量因子(Q)，纳米级声学谐振器可以以非常低的输入功率驱动到非线性状态，这是梳子产生的先决条件。这个CAREER项目调查了多模态纳米机电频率梳形成和调谐背后的基础科学，这一点至今仍未得到充分的探索。非线性声子相互作用的研究可以解释这些声子系统中的基本物理现象，如孤子行为、本征局域模式和混沌行为。该奖项反映了美国国家科学基金会的法定使命，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。

The tremendous growth of wireless devices and Internet of Things (IoT) applications has placed a great strain on the radio frequency (RF) network infrastructures, congesting the channels and over-crowding the radio frequency spectrum. Battery-operated portable smart devices such as smartphones, wearable technologies (e.g., smart watches and glasses), personal radars, and other smart gadgets all compete for bandwidth and require efficient spectrum utilization. To combat the looming RF spectrum scarcity, multi-band, multi-standard wireless systems that ?sense? the RF spectrum in response to the in-situ spectrum utilization, in the most efficient manner are required. Software solutions such as machine learning techniques are applied after the RF signal is digitized and often require large computational power. The machine learning techniques also face challenges of real-time learning and decision making to identify available channels in dynamical wireless environments. Signal processing and spectrum sensing at RF is a potential solution that can assist the software techniques and relax the receiver specifications. This CAREER project offers a transformative solution of RF spectrum sensing: a chip-scale multi-GHz nano-electro-mechanical frequency comb generator, integrated with other RF front-end electronics. This project aims to broaden the participation of K-12 and undergraduate students, particularly minority and female students, in STEM fields. The outreach plan will use educational kits and hands-on modules through collaboration with outreach programs at Georgia Tech.At the heart of the spectrum sensor, the proposed compact RF comb generator, utilizes the parallel processing resulting from the multiple comb teeth to perform fast RF spectrum sensing, using lower power and fewer circuit components than the current state-of-the-art hardware solutions. The proposed nano-electro-mechanical frequency comb generator targets the super high frequency range (3-30 GHz) and harnesses new capabilities stemming from drastic reduction of piezoelectric film thickness in bulk acoustic wave resonators to generate wideband frequency combs. Leveraging their small size and high quality factor (Q), nanoscale acoustic resonators can be driven into the nonlinear regime, a prerequisite for comb generation, with very low input power. This CAREER project investigates the fundamental science behind the formation and tuning of multi-modal nano-electro-mechanical frequency combs, which remains under-explored to date. The study of nonlinear phonon interactions can elucidate fundamental physical phenomena, such as solitonic behavior, intrinsic localized modes, and chaotic behavior in such phononic systems.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.