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：
1717045
负责人：
Atilla Eryilmaz
金额：
$ 25万
依托单位：
Ohio State University
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 2021-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1717045&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.

除了传统用于人类对人类或人与人之间的通信外，还设想了无线网络还构成了许多新兴应用，在医疗保健，运输和发电机分配中的骨干。这些应用程序的平稳操作在关键上取决于无线网络的令人满意的操作，尽管网络条件突然不断变化和严格的应用程序特定的服务要求，但负责代理之间信息的有效和及时传输的操作并没有考虑到应用程序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，以开发新的方法，以支持支持基本服务，并在包括低成本医疗保健，能源储蓄和安全的各个领域的应用程序中提供必要的服务。这项研究的进步将通过在将来获得高效和低成本的访问来使社会上的社会受益。该项目还将有助于推进对未来工程师的培训和教育，并在理论基础和设计有效的无线网络算法的设计方面具有良好的基础。该项目的广泛目的是为将来的无线网络开发统一的机器学习和资源分配框架，该框架可以适应物理层的快速变化的动态和统计信息以及应用程序层的越来越严格的服务要求。机器学习的基本问题是在系统依据的统计模型先验的统计模型时，在随机系统中做出决策。尽管在此问题上进行了很多活动，但未考虑无线网络独有的许多功能，包括多个用户之间的网络动态，交互和依赖关系以及瞬时延迟性能。该提案的重点是设计快速的在线学习算法，通过考虑无线网络的独特特征，从而导致网络性能的显着改善。