Automated Self-Optimization in 5G Heterogeneous Wireless Communications Networks

Traditional single-tiered wireless communications networks cannot scale to satisfy exponentially rising demand. Operators are increasing capacity by densifying their existing Macro Cell deployments with co-channel Small Cells. However, cross-tier interference and load balancing issues present new optimization challenges in channel sharing heterogeneous networks (HetNets). 

One-size-fits-all heuristics for allocating resources are highly suboptimal, but designing ad-hoc controllers requires significant human expertise and manual fine tuning. In this paper a unified, flexible, and fully automated approach for end-toend optimization in multi-layer 5G HetNets is presented. A hill climbing algorithm is developed for reconfiguring cells in real time in order to track dynamic traffic patterns. Schedulers for allocating spectrum between user equipments are automatically synthesized using Grammar-based Genetic Programming. 

The proposed methods for configuring the HetNet and scheduling in the time-frequency domain can address ad-hoc objective functions. Thus, the operator can flexibly tune the trade-off between peak rates and fairness. Far cell edge downlink rates are increased by up to 250% compared to non-adaptive baselines. Alternatively, peak rates are increased by up to 340%. The experiments illustrate the utility and future potential of natural computing techniques in 5G software-defined wireless communications networks.