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: 2229472
• 负责人: Francesco Restuccia
• 金额: $ 24.92万
• 依托单位: Northeastern 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=2229472&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.

通过改进频谱访问来提高抗干扰能力需要快速感知、认知和可操作的智能，以通过算法实时强制合规性。测量频谱使用情况、量化合法使用、检测违规行为和强制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增强的安全性。为此，该 SWIFT 项目将展示一个通过射频 (RF) 机器学习 (ML) 进行频谱态势感知的系统。主要目标是通过实时了解波形形状、频谱内容和调制方案，获得 6 GHz 以下传统频段中可操作的频谱情报。该研究将通过先进的硬件创新，创建频谱管理和动态频谱访问的终身增量学习方法。通过阵列处理、可靠的人工智能与终身学习算法的结合，该项目预计将比基于软件的系统提高至少 100 倍、低复杂度人工智能和数字信号处理。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（例如深度学习）将用于实时处理携带各种波形、调制和协议的宽带多向射频信号。该 SWIFT 团队由佛罗里达国际大学 (FIU)（南佛罗里达州最大的公立研究型 R1 大学，拥有 67% 以上的西班牙裔学生）领导，其中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。安柏里德航空航天大学的 PI 将带头努力指导科学、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（世界上最大的射频仿真器）和 PAWR 平台创建无线通信和 RF-ML 方面的研究生教材。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 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Toward Integrated Sensing and Communications in IEEE 802.11bf Wi-Fi Networks

迈向 IEEE 802.11bf Wi-Fi 网络中的集成传感和通信

• DOI: 10.1109/mcom.001.2200806
• 发表时间: 2023-07
• 期刊: IEEE Communications Magazine
• 影响因子: 11.2
• 作者: Meneghello, Francesca;Chen, Cheng;Cordeiro, Carlos;Restuccia, Francesco
• 通讯作者: Restuccia, Francesco

Wi-BFI: Extracting the IEEE 802.11 Beamforming Feedback Information from Commercial Wi-Fi Devices

Wi-BFI：从商业 Wi-Fi 设备中提取 IEEE 802.11 波束成形反馈信息

• DOI: 10.1145/3615453.3616514
• 发表时间: 2023-10
• 期刊: ACM
• 作者: Haque, Khandaker Foysal;Meneghello, Francesca;Restuccia, Francesco
• 通讯作者: Restuccia, Francesco

A 2 -UAV: Application-Aware Content and Network Optimization of Edge-Assisted UAV Systems

A 2-UAV：边缘辅助无人机系统的应用感知内容和网络优化

• DOI: 10.1109/infocom53939.2023.10229096
• 发表时间: 2023-05
• 期刊: IEEE
• 作者: Coletta, Andrea;Giorgi, Flavio;Maselli, Gaia;Prata, Matteo;Silvestri, Domenicomichele;Ashdown, Jonathan;Restuccia, Francesco
• 通讯作者: Restuccia, Francesco

Channel-Adaptive Dynamic Neural Networks for Low-Complexity Distributed Signal Awareness

用于低复杂度分布式信号感知的通道自适应动态神经网络

• DOI: 10.1109/milcom58377.2023.10356251
• 发表时间: 2023-10
• 期刊: IEEE
• 作者: Abdi, Mohammad;Ashdown, Jonathan;Turck, Kurt;Restuccia, Francesco
• 通讯作者: Restuccia, Francesco

SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split Computing

SplitBeam：通过拆分计算在 Wi-Fi 网络中实现有效且高效的波束成形

• DOI: 10.1109/icdcs57875.2023.00081
• 发表时间: 2023-07
• 期刊: IEEE
• 作者: Bahadori, Niloofar;Matsubara, Yoshitomo;Levorato, Marco;Restuccia, Francesco
• 通讯作者: Restuccia, Francesco