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Collaborative Research: CCRI: New: RFDataFactory: Principled Dataset Generation, Sharing and Maintenance Tools for the Wireless Community

合作研究：CCRI：新：RFDataFactory：无线社区的原则性数据集生成、共享和维护工具

基本信息

批准号：
2120447
负责人：
Kaushik Chowdhury
金额：
$ 144万
依托单位：
Northeastern University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2120447&HistoricalAwards=false
关键词：
Collaborative Research CCRI New RFDataFactory

项目摘要

Applied machine learning (ML) research in wireless faces challenges due the inability of domain experts to easily access existing well-curated, well-structured, and open-access datasets. Furthermore, there is a lack of direct access to a software framework that automates dataset creation and distribution based on detailed user requirements. RFDataFactory is a collaborative project that brings together investigators from Northeastern University and Rice University to bridge this gap. RFDataFactory aims to make available categorized datasets suitable for research related to ML in 5G and beyond networks, and advance fundamental understanding and design tools for accessing, creating, sharing and storing wireless datasets.RFDataFactory will enable easy collection and preprocessing of physical layer to packet-level datasets through high-level directives and application programming interfaces. This will enable dataset generation for several NSF-funded experimentation platforms, such as the Colosseum emulator and NSF Platforms for Advanced Wireless Research. The project will significantly advance autonomous statistical analysis of RF spectrum activity, which will reduce data storage needs. Moreover, it will create pre-processing tools for removing device identifying information and facilitate generating standards compliant metadata headers. The project will also result in a search-able, centralized repository of both project-supported and user-contributed datasets with the focus on re-usability.RFDataFactory will accelerate interdisciplinary research at the intersection of machine learning and the wireless domain, as well as bridging different communities and train a new generation of professionals for wireless dataset creation and sharing. The project will seek to involve underrepresented students in research and learning activities, support annual dataset gathering challenges, update advanced course materials with hands-on tutorials and laboratory sessions. Through targeted high-school outreach, the project will increase awareness and excitement in the next generation of researchers. The project will also generate value for other large-scale infrastructure investments already made by the NSF.Project website, https://www.rfdatafactory.com/, will include all datasets, meta-data files, software application programming interfaces, tutorial materials, webinar recordings and other digital outcomes of this project, which will be maintained for 3 years, accessible via the project website after the completion of the project.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.

由于领域专家无法轻松访问现有的精心策划、结构良好且开放访问的数据集，无线领域的应用机器学习（ML）研究面临挑战。此外，缺乏直接访问软件框架的机会，该框架可根据详细的用户要求自动创建和分发数据集。RFDataFactory是一个合作项目，汇集了来自东北大学和莱斯大学的研究人员，以弥合这一差距。RFDataFactory旨在提供适用于5G及其他网络中ML相关研究的分类数据集，并推进访问，创建，共享和存储无线数据集的基本理解和设计工具。RFDataFactory将通过高级指令和应用程序编程接口轻松收集和预处理物理层到分组级数据集。这将为NSF资助的几个实验平台生成数据集，例如Colosseum仿真器和NSF高级无线研究平台。该项目将显著推进射频频谱活动的自主统计分析，从而减少数据存储需求。此外，它将创建用于删除设备标识信息的预处理工具，并促进生成符合标准的元数据报头。RFDataFactory将加速机器学习和无线领域交叉领域的跨学科研究，并在不同的社区之间架起桥梁，培养新一代无线数据集创建和共享的专业人才。该项目将寻求让代表性不足的学生参与研究和学习活动，支持年度数据集收集挑战，通过实践教程和实验室课程更新高级课程材料。通过有针对性的高中外展，该项目将提高下一代研究人员的认识和兴奋。该项目还将为NSF已经进行的其他大规模基础设施投资创造价值。项目网站https：//www.rfdatafactory.com/将包括该项目的所有数据集、元数据文件、软件应用程序编程接口、教程材料、网络研讨会录音和其他数字成果，这些成果将保留3年，项目完成后可通过项目网站访问。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

期刊论文数量（16）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Detection of Co- Existing RF Signals in CBRS Using ML: Dataset and API-Based Collection Testbed

DOI：
10.1109/mcom.002.2200682
发表时间：
2023-09
期刊：
IEEE Communications Magazine
影响因子：
11.2
作者：
Chinenye Tassie;Abdo Gaber;Vini Chaudhary;Nasim Soltani;M. Belgiovine;Michael Loehning;Vincent Kotzsch;Charles Schroeder;K. Chowdhury
通讯作者：
Chinenye Tassie;Abdo Gaber;Vini Chaudhary;Nasim Soltani;M. Belgiovine;Michael Loehning;Vincent Kotzsch;Charles Schroeder;K. Chowdhury

Hercules: An Emulation-Based Framework for Transport Layer Measurements over 5G Wireless Networks

Hercules：基于仿真的 5G 无线网络传输层测量框架

DOI：
10.1145/3615453.3616516
发表时间：
2023
期刊：
ACM
影响因子：
0
作者：
Pinto, Andrea;Ashdown, Andrew;Bin Hassan, Tanzil;Cheng, Hai;Esposito, Flavio;Bonati, Leonardo;D'Oro, Salvatore;Melodia, Tommaso;Restuccia, Francesco
通讯作者：
Restuccia, Francesco

Intelligent Closed-loop RAN Control with xApps in OpenRAN Gym

OpenRAN Gym 中使用 xApp 进行智能闭环 RAN 控制

DOI：
发表时间：
2022
期刊：
European Wireless 2022; 27th European Wireless Conference
影响因子：
0
作者：
Leonardo Bonati, Michele Polese
通讯作者：
Leonardo Bonati, Michele Polese

OpenRAN Gym: An Open Toolbox for Data Collection and Experimentation with AI in O-RAN

OpenRAN Gym：用于 O-RAN 中 AI 数据收集和实验的开放工具箱

DOI：
10.1109/wcnc51071.2022.9771908
发表时间：
2022
期刊：
2022 IEEE Wireless Communications and Networking Conference (WCNC
影响因子：
0
作者：
Bonati, Leonardo;Polese, Michele;D'Oro, Salvatore;Basagni, Stefano;Melodia, Tommaso
通讯作者：
Melodia, Tommaso

DeepCSI: Rethinking Wi-Fi Radio Fingerprinting Through MU-MIMO CSI Feedback Deep Learning https://arxiv.org/abs/2204.07614

DeepCSI：通过 MU-MIMO CSI 反馈深度学习重新思考 Wi-Fi 无线电指纹识别 https://arxiv.org/abs/2204.07614