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

Ohio State University, The, Columbus OH

Investigators

Abstract

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 requirements Existing 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.