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

合作研究：SWIFT：基于人工智能的传感通过频谱适应和终身学习提高弹性

• 批准号: 2229473
• 负责人: Sirani Mututhanthrige-Perera
• 金额: $ 25万
• 依托单位: Embry-Riddle Aeronautical University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229473&HistoricalAwards=false
• 关键词: Collaborative; Research; SWIFT; AI; based

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.

通过改进的光谱访问对干扰的弹性需要快速感应，认知和可行的智能，以实时实时执行算法。测量频谱使用情况，量化合法用途，检测违规行为和执行合规性的能力直接导致改进的频谱利用率，多个竞争用户的共存以及增强的安全性。为此，这个Swift项目将通过射频（RF）机器学习（ML）展示一个用于光谱情境意识的系统。关键目的是通过对波形形状，光谱含量和调制方案的实时理解，在Sub-6 GHz传统频段中获得可起作的光谱智能。这项研究将通过高级硬件创新来实现频谱管理和动态频谱访问的终生渐进学习方法。预计该项目通过阵列处理，可靠的AI与终身学习算法的组合，将至少100倍超过基于软件的系统，而不是基于软件的系统。具体而言，AI技术将用于实现频谱智能，更具体地说是数据驱动的技术，例如深度学习，用于实时处理宽带多向RF信号，载有各种波浪形，调制和协议。由佛罗里达国际大学（FIU）领导 - 南佛罗里达州最大的公共研究R1大学，拥有67％以上的西班牙裔学生，该迅速团队将包括许多代表性不足的学生，他们在夏季研究中将学习频谱感知的关键概念。 Embry-Riddle Aeronautical University的PIS将率先指导女性科学，技术，工程和数学。东北大学的PI将专注于基于罗马竞技场（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，以培训AI无线电以供科学界的可用性和可重复性。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来提供支持。