权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

NeTS: Small: Collaborative Research: Fast Online Machine Learning Algorithms for Wireless Networks

NeTS：小型：协作研究：无线网络的快速在线机器学习算法

基本信息

批准号：
1718203
负责人：
Rayadurgam Srikant
金额：
$ 25万
依托单位：
University of Illinois at Urbana-Champaign
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-15 至 2021-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718203&HistoricalAwards=false
关键词：
NeTS Small Collaborative Research Fast

项目摘要

In addition to their traditional use for human-to-human or human-to-machine communication, wireless networks are also envisioned to form the backbone of many emerging applications in health care, transportation, and power distribution. The smooth operation of these applications critically depends on the satisfactory operation of the wireless networks that are responsible for the efficient and timely transfer of information between agents, despite abruptly changing network conditions and stringent application-specific service requirementsExisting state-of-the-art wireless network resource allocation does not account for the stringent and changing requirements of the application demands. This research proposes to leverage and extend the emerging area of machine learning to develop new approaches to low-delay wireless networks that are necessary for the support of essential services with applications in diverse domains including low-cost healthcare, energy savings, and security. Advances made in this research will benefit the society-at-large by enabling efficient and low-cost access to such services in the future. The project will also help advance the training and education of future engineers with a strong foundation on both the theoretical underpinnings and the practical considerations for the design of efficient wireless network algorithms. The broad objective of this project is to develop a unified machine learning and resource allocation framework for future wireless networks that can adapt to rapidly changing dynamics and statistics at the physical layer and the increasingly stringent service requirements at the application layer. The fundamental problem in machine learning is to make decisions in a stochastic system when the statistical model underlying the system is unknown a priori. While there has been much activity on this problem, many features unique to wireless networks are not considered, including rapidly changing network dynamics, interactions and dependencies among multiple users, and transient delay performance. The focus of the proposal is to design fast, online learning algorithms which lead to dramatic improvements in network performance, by taking into account the unique characteristics of wireless networks.

除了传统上用于人对人或人对机器通信之外，无线网络还被设想为形成医疗保健、运输和配电中许多新兴应用的骨干。这些应用的平稳运行关键取决于无线网络的令人满意的操作，无线网络负责代理之间的有效和及时的信息传输，尽管突然改变的网络条件和严格的应用特定的服务要求。现有的最先进的无线网络资源分配不考虑应用需求的严格和不断变化的要求。这项研究建议利用和扩展机器学习的新兴领域，开发低延迟无线网络的新方法，这些方法对于支持不同领域的基本服务（包括低成本医疗保健，节能和安全）是必要的。这项研究取得的进展将使整个社会受益，使人们能够在未来高效、低成本地获得这些服务。该项目还将有助于推进未来工程师的培训和教育，为设计高效无线网络算法的理论基础和实际考虑奠定坚实的基础。该项目的主要目标是为未来的无线网络开发一个统一的机器学习和资源分配框架，以适应物理层快速变化的动态和统计数据以及应用层日益严格的服务要求。机器学习的基本问题是在随机系统中做出决策，而系统的统计模型是先验未知的。虽然已经有很多活动在这个问题上，无线网络特有的许多功能没有考虑，包括快速变化的网络动态，多个用户之间的相互作用和依赖性，以及瞬态延迟性能。该提案的重点是设计快速，在线学习算法，导致网络性能的显着改善，考虑到无线网络的独特特性。