A Nonparametric Bayesian Approach for Opportunistic Data Transfer in Cellular Networks
The number of mobile Internet users is growing rapidly, as well as the capability of mobile Internet devices. As a result, the enormous amount of traffic generated everyday on mobile Internet is pushing cellular services to their limits. The authors see great potential in the idea of scheduling the transmission of delay tolerant data towards times when the network condition is better. However, such scheduling requires good network condition prediction, which has not been effectively tackled in previous research. In this paper, they propose a Dynamic Hidden Markov Model (DHMM) to model the time dependent and location dependent network conditions observed by individual users. The model is dynamic since transition matrix and states are updated when new observations are available.