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It is crucial for the service provider to comprehend and forecast mobiletraffic in large-scale cellular networks in order to govern and managemechanisms for base station placement, load balancing, and network planning.The purpose of this article is to extract and simulate traffic patterns frommore than 14,000 cells that have been installed in different metropolitanareas. To do this, we create, implement, and assess a method in which cells arefirst categorized by their point of interest and then clustered based on thetemporal distribution of cells in each region. The proposed model has beentested using real-world 5G mobile traffic datasets collected over 31 weeks invarious cities. We found that our proposed model performed well in predictingmobile traffic patterns up to 2 weeks in advance. Our model outperformed thebase model in most areas of interest and generally achieved up to 15\% lessprediction error compared to the na\"ive approach. This indicates that ourapproach is effective in predicting mobile traffic patterns in large-scalecellular networks.

5G Long-Term and Large-Scale Mobile Traffic Forecasting