Data-driven resource allocation with traffic load prediction

Abstract

Wireless big data is attracting extensive attention from operators, vendors and academia, which provides new freedoms in improving the performance from various levels of wireless networks. One possible way to leverage big data analysis is predictive resource allocation, which has been reported to increase spectrum and energy resource utilization efficiency with the predicted user behavior including user mobility. However, few works address how the traffic load prediction can be exploited to optimize the data-driven radio access. We show how to translate the predicted traffic load into the essential information used for resource optimization by taking energy-saving transmission for non-real-time user as an example. By formulating and solving an energy minimizing resource allocation problem with future instantaneous bandwidth information, we not only provide a performance upper bound, but also reveal that only two key parameters are related to the future information. By exploiting the residual bandwidth probability derived from the traffic volume prediction, the two parameters can be estimated accurately when the transmission delay allowed by the user is large, and the closed-form solution of global optimal resource allocation can be obtained when the delay approaches infinity. We provide a heuristic resource allocation policy to guarantee a target transmission completion probability when the delay is no-so-large. Simulation results validate our analysis, show remarkable energy-saving gain of the proposed predictive policy over non-predictive policies, and illustrate that the time granularity in predicting traffic load should be identical to the delay allowed by the user.