PROJECT TITLE :

Leveraging Intelligence from Network CDR Data for Interference Aware Energy Consumption Minimization - 2018

ABSTRACT:

Cell densification is being perceived because the panacea for the approaching capability crunch. But, high aggregated energy consumption and increased inter-cell interference (ICI) caused by densification, remain the 2 long-standing problems. We propose a completely unique network orchestration resolution for simultaneously minimizing energy consumption and ICI in ultra-dense 5G networks. The proposed resolution builds on a massive knowledge analysis of over ten million CDRs from a real network that shows there exists robust spatio-temporal predictability in real network traffic patterns. Leveraging this, we tend to develop a unique scheme to pro-actively schedule radio resources and tiny cell sleep cycles yielding substantial energy savings and reduced ICI, without compromising the users QoS. This theme is derived by formulating a joint Energy Consumption and ICI minimization drawback and solving it through a mix of linear binary integer programming, and progressive analysis based heuristic algorithm. Evaluations using: 1) a HetNet deployment designed for Milan town where massive information analytics are used on real CDRs data from the Telecom Italia network to model traffic patterns, 2) NS-three primarily based Monte-Carlo simulations with artificial Poisson traffic show that, compared to full frequency reuse and forever on approach, in best case, the proposed scheme can cut back energy consumption in HetNets to 1/8th while providing same or higher QoS.