SpecEES: DISCOVER: Device Identification for Spectrum-optimization using COnVolutional nEural netwoRks

SpecEES：DISCOVER：使用卷积神经网络进行频谱优化的设备识别

• 批准号: 1923789
• 负责人: Kaushik Chowdhury
• 金额: $ 75万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1923789&HistoricalAwards=false
• 关键词: SpecEES; DISCOVER; Device; Identification; Spectrum

The research objective of this project, called DISCOVER, is to harness the power of deep machine learning (ML) algorithms to communicate wirelessly with high spectral efficiency and low power consumption. The techniques will result in advanced networking protocols that consume minimal resources by securely identifying the devices that are active in the surrounding environment without (or with minimal) control signaling. Apart from learning channel usage and device activity, DISCOVER will allow for rapidly deploying these algorithms in hardware, so that real-time inferences can be made. Thus, DISCOVER is directly aligned with the US President's executive order from February 2019 'Maintaining American Leadership in Artificial Intelligence' that seeks to prioritize research and development of America's artificial intelligence (AI) capabilities. DISCOVER aims to bring together industry, academia and government stakeholders through collaborative workshops towards identifying high priority challenges, limitations of available data sources, and identify a list of candidate machine learning solutions that will shape the next generation of wireless technologies. The open source release of signal datasets and simulation code will foster new interactions of wireless researchers with core machine learning domain experts.DISCOVER has three goals for optimizing spectrum utilization with overlapping interests of either energy saving or resilience to identity spoofing through the use of deep learning architectures: 1. It aims to explore deep convolutional neural network (CNN) architectures that will allow highly accurate device classification and demonstrate how to eliminate identifier-related protocol fields. This approach of reducing packet headers will achieve quantifiable spectrum utilization improvements, especially for large-scale deployment of the Internet of Things (IoT). 2. It aims to demonstrate the first learning-in-the-loop radio frequency (RF) system where spectrum-driven decisions are enabled through real-time deep learning algorithms implemented directly on the device hardware. This will result in significant energy savings for embedded IoT devices. 3. The emulation engine developed in the project will empower users to create custom-signals to train ML algorithms. Furthermore, it will create community RF signal datasets that will ensure means of standardized validation for the larger research 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.

该项目的研究目标称为Discover，是利用深度机器学习（ML）算法的力量，以高光谱效率和低功耗进行无线通信。这些技术将导致高级网络协议，通过安全地识别在没有（或使用最少）控制信号的周围环境中活跃的设备，从而消耗最小的资源。除了学习渠道使用率和设备活动外，Discover还可以快速在硬件中部署这些算法，以便进行实时推断。因此，Discover与2019年2月的美国总统行政命令直接保持一致，该命令旨在优先考虑美国人工智能（AI）能力的研究和发展。 Discover旨在通过协作研讨会将行业，学术界和政府利益相关者汇集在一起​​，以确定高度优先挑战，可用数据源的局限性，并确定将塑造下一代无线技术的候选机器学习解决方案列表。信号数据集和仿真代码的开源发布将促进无线研究人员与核心机器学习域专家的新相互作用。确保有三个目标，可以通过三个目标来优化频谱利用，并重叠节能或对身份的弹性对身份欺骗的兴趣，以欺骗深度学习体系结构：1。允许深度卷积网络（CORTISTIRE IMPERTITY ERNECTIRATION ERNECTIRATION ERNECTIRATIES）。标识符相关协议字段。这种减少数据包标头的方法将实现可量化的光谱利用率改进，尤其是用于大规模部署物联网（IoT）。 2。它的目的是展示第一个在环射频（RF）系统中，该系统通过直接在设备硬件上实现的实时深度学习算法来实现频谱驱动的决策。这将为嵌入式IoT设备节省大量能源。 3。项目中开发的仿真引擎将使用户能够创建自定义信号来培训ML算法。此外，它将创建社区RF信号数据集，以确保对较大的研究社区的标准化验证手段。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估的评估来获得支持的。

项目成果

Multimodality in mmWave MIMO Beam Selection Using Deep Learning: Datasets and Challenges

使用深度学习的毫米波 MIMO 波束选择中的多模态：数据集和挑战

• DOI: 10.1109/mcom.002.2200028
• 发表时间: 2022
• 期刊: IEEE Communications Magazine
• 影响因子: 11.2
• 作者: Gu, Jerry;Salehi, Batool;Roy, Debashri;Chowdhury, Kaushik R.
• 通讯作者: Chowdhury, Kaushik R.

PRONTO: Preamble Overhead Reduction With Neural Networks for Coarse Synchronization

• DOI: 10.1109/twc.2023.3256961
• 发表时间: 2021-12
• 期刊: IEEE Transactions on Wireless Communications
• 影响因子: 10.4
• 作者: N. Soltani;Debashri Roy;K. Chowdhury

NN-key: A Neural Network-Based Secret Key for Demapping OFDM Symbols

NN-key：基于神经网络的 OFDM 符号解映射密钥

• DOI: 10.1109/ccnc49033.2022.9700617
• 发表时间: 2022
• 期刊: IEEE 19th Annual Consumer Communications & Networking Conference (CCNC
• 作者: Soltani, Nasim;Li, Yanyu;Erdogmus, Deniz;Wang, Yanzhi;Chowdhury, Kaushik
• 通讯作者: Chowdhury, Kaushik

Going beyond RF: A survey on how AI-enabled multimodal beamforming will shape the NextG standard

• DOI: 10.1016/j.comnet.2023.109729
• 发表时间: 2023-03
• 期刊: Comput. Networks
• 作者: Debashri Roy;Batool Salehi;Stella Banou;Subhramoy Mohanti;Guillem Reus Muns;M. Belgiovine;P. Ganesh-P.-Gane

AirNN: Over-the-Air Computation for Neural Networks via Reconfigurable Intelligent Surfaces

• DOI: 10.1109/tnet.2022.3225883
• 发表时间: 2023-12
• 期刊: IEEE/ACM Transactions on Networking
• 作者: Sara Garcia Sanchez;Guillem Reus-Muns;Carlos Bocanegra;Yanyu Li;Ufuk Muncuk;Yousof Naderi;Yanzhi Wang;Stratis Ioannidis;K. Chowdhury