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.

由于域专家无法轻松访问现有的策划良好，结构良好和开放式访问数据集，因此无线面临的应用机器学习（ML）研究挑战。此外，缺乏直接访问软件框架，该软件框架可以根据详细的用户要求自动创建数据集创建和分发。 RFDATAFACTORY是一个合作项目，它汇集了东北大学和赖斯大学的调查人员，以弥合这一差距。 RFDATAFACTORY旨在使适用于5G和超越网络中ML的研究的分类数据集，并提高基本理解和设计用于访问，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界面轻松收集和预处理物理层到数据包级别的数据集。这将为多个NSF资助的实验平台，例如Colosseum模拟器和NSF平台，用于高级无线研究。该项目将大大提高RF光谱活动的自主统计分析，这将减少数据存储需求。此外，它将创建预处理工具，用于删除设备识别信息并促进生成标准符合标准的元数据标头。该项目还将导致一个可搜索的，集中式的存储库，同时由项目支持和用户组成的数据集，重点是可重复使用。 RFDATAFACTORY将在机器学习与无线领域的交集中加速跨学科研究，并桥接不同的社区，并培训新一代的专业人员进行无线数据集创建和共享。该项目将寻求使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，通过动手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宣传，该项目将提高下一代研究人员的意识和兴奋。该项目还将为NSF已经进行的其他大规模基础设施投资产生价值。项目URL：http：//rfdatafactory.net。项目完成后，所有数据集，元数据文件，软件应用程序编程界面，教程材料，网络研讨会记录和其他数字成果将维持3年，可在项目完成后通过项目网站访问。此奖项反映了NSF的法规任务并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来评估值得支持的。

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