One approach to achieving greener cellular networks is to power them with renewable energy. When there is insufficient renewable energy, base stations (BSs) with lower predicted traffic loads are dynamically switched to sleep mode as a means of reducing total BS energy consumption. Prediction is done using only the traffic logs of active BSs since traffic logs are not available for sleeping BSs. However, prediction accuracy is degraded if information for the entire network is not used. We propose a scheme built on software-defined-networking and edge computing technologies for maintaining the accuracy of traffic prediction by incorporating the most important traffic logs into the prediction even when using only traffic logs for active BSs. It works by estimating the contribution of the traffic logs for each BS to traffic prediction accuracy. Evaluation of the proposed scheme through extensive numerical experiments using a dataset of actual BS traffic logs demonstrates that the proposed scheme is superior to two benchmark schemes in terms of prediction accuracy and robustness against the reduction of active BSs for energy saving and different BS sets.
ASJC Scopus subject areas
- Information Systems
- Hardware and Architecture
- Computer Networks and Communications