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

• 批准号: 2120363
• 负责人: Ashutosh Sabharwal
• 金额: $ 36万
• 依托单位: William Marsh Rice 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=2120363&HistoricalAwards=false
• 关键词: Collaborative; Research; CCRI; New; RFDataFactory

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 url: http://rfdatafactory.net. All datasets, meta-data files, software application programming interfaces, tutorial materials, webinar recordings and other digital outcomes of this project 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、结构良好和开放访问的数据集，无线领域的应用机器学习（ML）研究面临着挑战。此外，缺乏对软件框架的直接访问，该框架可以根据详细的用户需求自动创建和分发数据集。RFDataFactory是一个合作项目，汇集了东北大学和莱斯大学的研究人员来弥合这一差距。RFDataFactory旨在提供适合5G及以后网络中与机器学习相关的研究的分类数据集，并推进对访问、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，方便地收集和预处理物理层到包级数据集。这将使几个NSF资助的实验平台能够生成数据集，如Colosseum模拟器和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，它将创建预处理工具，用于删除设备标识信息，并促进生成符合标准的元数据头。该项目还将产生一个可搜索的、集中的存储库，包括项目支持的和用户贡献的数据集，重点是可重用性。RFDataFactory将加速机器学习和无线领域交叉领域的跨学科研究，同时连接不同的社区，培养新一代无线数据集创建和共享的专业人员。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会议更新高级课程材料。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。该项目还将为国家科学基金会已经进行的其他大规模基础设施投资创造价值。项目地址：http://rfdatafactory.net。本项目所有数据集、元数据文件、软件应用程序编程接口、教程材料、网络研讨会录音等数字化成果将保存3年，项目完成后可通过项目网站访问。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识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

Robustness of Distributed Multi-User Beamforming: An Experimental Evaluation

分布式多用户波束成形的鲁棒性：实验评估

• DOI: 10.1109/sam53842.2022.9827783
• 发表时间: 2022
• 期刊: IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM
• 作者: Doost-Mohammady, Rahman;Zafari, Mehdi;Sabharwal, Ashutosh
• 通讯作者: Sabharwal, Ashutosh

A Deep Reinforcement Learning-Based Resource Scheduler for Massive MIMO Networks

• DOI: 10.1109/tmlcn.2023.3313988
• 发表时间: 2023-03
• 期刊: IEEE Transactions on Machine Learning in Communications and Networking
• 作者: Qing An;Santiago Segarra;C. Dick;A. Sabharwal;Rahman Doost-Mohammady