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

通过改进频谱接入来抵御干扰需要快速感知、认知和可操作的智能，以便通过算法实时强制执行合规性。测量频谱使用、量化合法使用、检测违规行为和强制执行合规性的能力直接导致频谱利用率的提高、多个竞争用户的共存以及安全性的增强。为此，该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。其主要目标是通过实时了解波形形状、频谱内容和调制方案，在6 GHz以下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接入创建终身增量学习方法。该项目预计将通过阵列处理、可靠的人工智能与终身学习算法、低复杂度人工智能和数字信号处理的组合，将基于软件的系统提高至少100倍。具体而言，AI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）将用于实时处理携带各种波形、调制和协议的宽带多向RF信号。该SWIFT团队由佛罗里达国际大学（FIU）领导，该大学是南佛罗里达最大的公立研究型R1大学，有67%以上的西班牙裔学生，该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感知的关键概念。安柏瑞德航空大学的PI将率先努力指导科学，技术，工程和数学领域的女性。东北大学的PI将专注于基于Colosseum（世界上最大的RF仿真器）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，用于培训人工智能无线电，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。

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.