Abstract
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.
Highlights
In 5G, the proactive network optimisation boosts the network in disposing the exponential traffic growth (600x to 2500x capacity increase [1]), stringent service requirements (10,000 or more low-rate devices per cell site [2]), and reducing capital and operational expenditure (≈ 60× expenditure increase [1])
In order to overcome these issues, several proactive load-balancing methods based on machine learning have been proposed to deal with the unpredictable traffic by learning, forecasting, adjusting cell margins [11]–[14]
Traditional proactive load balancing methods are developed based on machine learning algorithms to forecast the cell load condition in which the profit will be maximised
Summary
In 5G, the proactive network optimisation boosts the network in disposing the exponential traffic growth (600x to 2500x capacity increase [1]), stringent service requirements (10,000 or more low-rate devices per cell site [2]), and reducing capital and operational expenditure (≈ 60× expenditure increase [1]). Fuzzy logic controllers were used in [8], [9] for auto-tuning handover margins These controllerbase methods have a cold-start problem because it has insufficient information at the start-up and requires time to converge. In order to overcome these issues, several proactive load-balancing methods based on machine learning have been proposed to deal with the unpredictable traffic by learning, forecasting, adjusting cell margins [11]–[14]. Cellular data, such as cell load, call blocking ratio, UE and BS distribution, are usually used to learn prior knowledge for the proactive optimisation.
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