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.

通过改进的频谱接入恢复干扰需要快速感知、认知和可操作的智能，以实时算法强制执行合规性。直接测量频谱使用、量化合法使用、检测违规和强制执行合规性的能力可提高频谱利用率、多个竞争用户共存并增强安全性。为此，SWIFT项目将演示一个通过射频(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，在低于6 GHz的传统频段中获得可行的频谱智能。这项研究将在先进的硬件创新的支持下，创建用于频谱管理和动态频谱访问的终身增量学习方法。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、低复杂性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。具体地说，人工智能技术将用于实现频谱智能，更具体地说，将用于实现数据驱动技术，如深度学习，以实现携带不同波形、调制和协议集的宽带多方向RF信号的实时处理。由佛罗里达国际大学(FIU)领导，该校是南佛罗里达州最大的公共研究型R1大学，拥有67%以上的西班牙裔学生，这个SWIFT团队将包括许多未被充分代表的学生，他们将在暑期研究中学习频谱感知的关键概念。安布里-里德尔航空大学的PiS将带头努力指导女性在科学、技术、工程和数学方面的工作。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方面的研究生教材。该团队将开发和维护公开的开放数据集，以培训人工智能无线电设备在科学界的可用性和重复性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

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
• 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

• DOI: 10.1109/icdcs57875.2023.00081
• 发表时间: 2023-07
• 期刊: 2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS)
• 作者: Niloofar Bahadori;Yoshitomo Matsubara;Marco Levorato;Francesco Restuccia

SiMWiSense: Simultaneous Multi-Subject Activity Classification Through Wi-Fi Signals

• DOI: 10.1109/wowmom57956.2023.00019
• 发表时间: 2023-03
• 期刊: 2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)
• 作者: Khandaker Foysal Haque;Milin Zhang;Francesco Restuccia

Zero-Shot Dynamic Neural Network Adaptation in Tactical Wireless Systems

• DOI: 10.1109/milcom58377.2023.10356318
• 发表时间: 2023-10
• 期刊: MILCOM 2023 - 2023 IEEE Military Communications Conference (MILCOM)
• 作者: Shahriar Rifat;Jonathan Ashdown;K. Turck;Francesco Restuccia

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
• 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