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

除了用于人与人或人与机器之间的传统通信外，无线网络还有望成为医疗保健、交通运输和电力分配等许多新兴应用的主干。这些应用的顺利运行关键依赖于无线网络的令人满意的运行，无线网络负责在代理之间高效和及时地传输信息，尽管网络条件突然变化和特定于应用的服务要求严格。现有的最先进的无线网络资源分配没有考虑到应用需求的严格和变化的要求。这项研究建议利用和扩展机器学习的新兴领域，以开发低延迟无线网络的新方法，这些方法对于支持基本服务和不同领域的应用是必要的，包括低成本医疗保健、节能和安全。这项研究取得的进展将使未来能够高效和低成本地获得此类服务，从而使整个社会受益。该项目还将有助于促进未来工程师的培训和教育，为设计高效的无线网络算法奠定坚实的理论基础和实践考虑。该项目的广泛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，该框架能够适应物理层快速变化的动态和统计以及应用层日益严格的服务要求。机器学习中的基本问题是在随机系统中做出决策时，系统背后的统计模型是先验未知的。虽然已经有很多关于这个问题的研究，但没有考虑到无线网络特有的许多特性，包括快速变化的网络动态、多个用户之间的交互和依赖以及瞬时延迟性能。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来显著提高网络性能。

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.