Context-aware proactive 5G load balancing and optimization for urban areas

In the fifth-generation (5G) mobile networks, the traffic is estimated to have a fast-changing and imbalance spatial-temporal distribution. It is challenging for a system-level optimisation to deal with while empirically maintaining quality of service. The 5G load balancing aims to address this problem by transferring the extra traffic from a high-load cell to its neighbouring idle cells. In recent literature, controller and machine learning algorithms are applied to assist the self-optimising and proactive schemes in drawing load balancing decisions. However, these algorithms lack the ability of forecasting upcoming high traffic demands, especially during popular events. This shortage leads to cold-start problems because of reacting to the changes in the heterogeneous dense deployment. Notably, the hotspots corresponding with skew load distribution will result in low convergence speed. To address these problems, this paper contributes to three aspects. Firstly, urban event detection is proposed to forecast the changes in cellular hotspots based on Twitter data for enabling context-awareness. Secondly, a proactive 5G load balancing strategy is simulated considering the prediction of the skewed-distributed hotspots in urban areas. Finally, we optimise this context-aware proactive load balancing strategy by forecasting the best activation time. This paper represents one of the first works to couple the real-world urban event detection with proactive load balancing.