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CCRI: Medium: Collaborative Research: 3DML: A Platform for Data, Design and Deployed Validation of Machine Learning for Wireless Networks and Mobile Applications

CCRI：媒介：协作研究：3DML：无线网络和移动应用机器学习的数据、设计和部署验证平台

基本信息

批准号：
2016727
负责人：
Yingyan Lin
金额：
$ 150万
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：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2016727&HistoricalAwards=false
关键词：
CCRI Medium Collaborative Research 3DML

项目摘要

The ever-increasing complexity of wireless networks and their emerging novel user applications (such as autonomous cars, virtual reality, and e-health) have spurred a significant demand to develop machine learning (ML) empowered, intelligent network management and optimization. However, there are still two main barriers to unleashing such innovations: (i) ML-based approaches require many large labeled datasets, which are difficult to acquire in the wireless context due to both privacy and cost challenges; and (ii) the challenge of deploying complex ML models into resource-constrained wireless devices. This project’s overarching goal is to design, develop, and disseminate a community platform called 3DML, for facilitating the development of ML-based innovations for next-generation wireless networks and mobile applications. 3DML will be the first platform, designed from the ground up, to meet the urgent need of exploring ML-based innovations for wireless applications, featuring three integrated key components. First, this project will develop 3DML-Data which has the ability to operate in networks with different scales and capture diverse network operating states and enable the collection of unprecedentedly diverse labeled datasets. Second, this project will design 3DML-Client, which consists of automated tools and compression libraries to (i) automatically generate efficient ML models and deployment strategies for achieving optimal trade-offs between task performance and resource consumption given diverse devices and applications, and (ii) provide a comprehensive pool of efficient ML modules and functions for fast development. Third, this project will develop 3DML-Infrastructure, which can make use of 3DML-Client with data collected from 3DML-Data, to generate efficient ML algorithms deployed into wireless infrastructure, and include a methodology for researchers to use ML algorithms to customize key modules for massive MIMO channel estimation, detection, decoding, beamforming, and spectrum sharing. This project addresses a pressing need of the wireless research community to develop a platform for ML-empowered intelligent network management. The success of this project will provide data and tools to enable automated and self-customized exploration and deployment of ML-based approaches for wireless applications. The educational program with workshops, online courses, and internships will involve not only undergraduate and graduate students from various institutes, but also practitioners from industry. Overall, 3DML will open up a host of new possibilities for developing innovations towards next generation intelligent wireless networks, including enhanced mobile broadband, massive Internet-of-things and ultra-low-latency applications in order to support numerous emerging applications. All of the developed datasets, tools, and libraries will be released at https://3dml.rice.eduThis 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.

无线网络及其新兴的新型用户应用（如自动汽车、虚拟现实和电子健康）的复杂性不断增加，促使人们迫切需要开发机器学习（ML）授权的智能网络管理和优化。然而，释放这些创新仍然存在两个主要障碍：（i）基于ML的方法需要许多大型标记数据集，由于隐私和成本挑战，这些数据集在无线环境中很难获得;以及（ii）将复杂ML模型部署到资源受限的无线设备中的挑战。该项目的总体目标是设计、开发和传播一个名为3DML的社区平台，以促进下一代无线网络和移动的应用的基于ML的创新开发。3DML将是第一个从头开始设计的平台，以满足探索基于ML的无线应用创新的迫切需求，具有三个集成的关键组件。首先，该项目将开发3DML-Data，该数据能够在不同规模的网络中运行，捕获不同的网络运行状态，并能够收集前所未有的多样化标记数据集。第二，该项目将设计3DML-Client，它由自动化工具和压缩库组成，以（i）自动生成高效的ML模型和部署策略，以在不同设备和应用程序的情况下实现任务性能和资源消耗之间的最佳权衡，以及（ii）为快速开发提供全面的高效ML模块和功能池。第三，该项目将开发3DML-Infrastructure，它可以利用3DML-Client和从3DML-Data收集的数据，生成部署到无线基础设施中的高效ML算法，并包括研究人员使用ML算法定制大规模MIMO信道估计，检测，解码，波束成形和频谱共享的关键模块的方法。该项目解决了无线研究界迫切需要开发一个ML授权的智能网络管理平台。该项目的成功将提供数据和工具，以实现自动化和自定义的探索和部署基于ML的无线应用方法。该教育计划包括研讨会，在线课程和实习，不仅涉及来自各机构的本科生和研究生，还涉及行业从业人员。总体而言，3DML将为下一代智能无线网络的创新开发开辟一系列新的可能性，包括增强型移动的宽带、大规模物联网和超低延迟应用，以支持众多新兴应用。所有开发的数据集、工具和库都将在www.example.com上发布https://3dml.rice.eduThis奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

期刊论文数量（14）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A3C-S: Automated Agent Accelerator Co-Search towards Efficient Deep Reinforcement Learning

A3C-S：自动化代理加速器协同搜索，实现高效深度强化学习

DOI：
10.1109/dac18074.2021.9586305
发表时间：
2021
期刊：
2021 58th ACM/IEEE Design Automation Conference (DAC
影响因子：
0
作者：
Fu, Yonggan;Zhang, Yongan;Li, Chaojian;Yu, Zhongzhi;Lin, Yingyan
通讯作者：
Lin, Yingyan

Enabling Resilience in Virtualized RANs with Atlas

DOI：
10.1145/3570361.3613276
发表时间：
2023-10
期刊：
Proceedings of the 29th Annual International Conference on Mobile Computing and Networking
影响因子：
0
作者：
Jiarong Xing;Junzhi Gong;Xenofon Foukas;Anuj Kalia;Daehyeok Kim;Manikanta Kotaru
通讯作者：
Jiarong Xing;Junzhi Gong;Xenofon Foukas;Anuj Kalia;Daehyeok Kim;Manikanta Kotaru

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