Collaborative Research: SWIFT: AI-based Sensing for Improved Resiliency via Spectral Adaptation with Lifelong Learning

通过改进频谱访问来抵御干扰需要快速感知、认知和可操作的智能，以算法实时执行合规性。测量频谱使用、量化合法使用、检测违规和强制合规的能力直接导致频谱利用率的提高、多个竞争用户的共存和安全性的增强。为此，SWIFT项目将展示一个通过射频（RF）机器学习（ML）实现频谱态势感知的系统。关键目标是通过实时了解波形形状、频谱内容和调制方案，在低于6 GHz的传统频段获得可操作的频谱情报。该研究将通过先进的硬件创新，为频谱管理和动态频谱访问创建终身增量学习方法。通过结合阵列处理、具有终身学习算法的可靠人工智能、低复杂性人工智能和数字信号处理，该项目预计将比基于软件的系统提高至少100倍。具体来说，人工智能技术将用于实现频谱智能，更具体地说，数据驱动技术，如深度学习，将用于实时处理宽带多向射频信号，这些信号包含多种波形、调制和协议。由佛罗里达国际大学（FIU）领导，这是南佛罗里达最大的公立研究型大学，拥有67%以上的西班牙裔学生，这个SWIFT团队将包括许多代表性不足的学生，他们将在夏季研究中学习频谱传感的关键概念。安柏瑞德航空大学（Embry-Riddle Aeronautical University）的私人顾问将带头在科学、技术、工程和数学领域指导女性。东北大学的PI将专注于基于Colosseum（世界上最大的RF模拟器）和PAWR平台创建无线通信和RF- ml的研究生教材。该团队将开发和维护公共开放数据集，用于训练人工智能无线电，以提高科学界的可用性和可重复性。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。

Resilience to interference via improved spectrum access requires fast sensing, cognition, and actionable intelligence to algorithmically enforce compliance in real-time. The ability to measure spectrum usage, quantify legitimate uses, detect violations and enforce compliance directly leads to improved spectrum utilization, coexistence of multiple competing users, and enhanced security. To this end, this SWIFT project will demonstrate a system for spectral situational awareness through radio frequency (RF) machine learning (ML). The key objective is to obtain actionable spectrum intelligence in the sub-6 GHz legacy bands through a real-time understanding of waveform shapes, spectral content, and modulation schemes. The research will create lifelong incremental learning approaches to spectrum management and dynamic spectrum access, enabled by advanced hardware innovations.The project is expected to improve at least 100x over software-based systems, through a combination of array processing, reliable AI with lifelong learning algorithms, low-complexity AI, and digital signal processing. Specifically, AI techniques will be used to achieve spectrum intelligence, and more specifically data driven techniques, such as deep learning, towards real-time processing of wideband multi-directional RF signals carrying a diverse set of waveforms, modulations, and protocols. Led by Florida International University (FIU) - South Florida's largest public research R1 university with 67+% Hispanic students, this SWIFT team will include many under-represented students, who in summer research, will learn key concepts in spectrum sensing. PIs at Embry-Riddle Aeronautical University will spearhead efforts in mentoring women in science, technology, engineering, and mathematics. The PI at Northeastern University will focus on creating graduate teaching materials in wireless communications and RF-ML based on Colosseum (the world's largest RF emulator) and the PAWR platforms. The team will develop and maintain public open datasets for training AI radios for usability and reproducibility of the scientific community.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.