NSF CNS 1117597: Understanding
Traffic Dynamics in Cellular Data Networks and Applications to Resource
Management
Broadband cellular
networks are emerging to be the most common means for mobile data access
worldwide. Predictions from industry analysts indicate that the volume of data
through cellular data networks will increase exponentially in near future.
Understanding of the mobile data traffic via measurement and analysis is
critical for the development of resource management techniques for these
networks. While spectrum resources are of great concern, this project
specifically focuses on the energy required to operate the cellular network
infrastructure, specifically base stations. The project undertakes a
significant modeling exercise with two goals. One goal is intellectual, driven
towards understanding the spatio-temporal dynamics of mobile traffic and
discovering possible structure or relationships. The project uses
state-of-the-art machine learning tools to develop models using large-scale
data collected directly from the operator's networks. Such modeling will bring
new insights that in turn will help to deploy and manage future generation
cellular data networks. The second goal is utilitarian. Here, techniques are
developed to predict base station loads for use in resource management,
specifically energy. Algorithms are designed to exploit energy-optimization
opportunities to turn off specific network resources based on the forecasted load
The project has significant broader impact. It develops technologies to appreciably reduce energy consumption in cellular networks. Overall, this exercise will both reduce cost, and contribute to the environment. The project also contributes to several green initiatives in both institutions and to the education and training of graduate students.
Some
part of this research is supported by a NSF Award Number: CNS 1117597: NeTS Small: Collaborative
Research: Understanding Traffic Dynamics in Cellular Data Networks and
Applications to Resource Management (Disclaimer: "Any opinions,
findings, and conclusions or recommendations expressed in this material are
those of the author(s) and do not necessarily reflect the views of the National
Science Foundation.")
Maintained by
Milind M. Buddhikot