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With the development of 4G/5G, the rapid growth of traffic has caused a largenumber of cell indicators to exceed the warning threshold, and network qualityhas deteriorated. It is necessary for operators to solve the congestion inadvance and effectively to guarantee the quality of user experience. Cell-levelmulti-indicator forecasting is the foundation task for proactive complexnetwork optimization. In this paper, we propose the 4G/5G Cell-levelmulti-indicator forecasting method based on the dense-Multi-Layer Perceptron(MLP) neural network, which adds additional fully-connected layers betweennon-adjacent layers in an MLP network. The model forecasted the followingweek's traffic indicators of 13000 cells according to the six-month historicalindicators of 65000 cells in the 4G&5G network, which got the highest weightedMAPE score (0.2484) in the China Mobile problem statement in the ITU-T AI/ML in5G Challenge 2021. Furthermore, the proposed model has been integrated into theAsiaInfo 4G/5G energy-saving system and deployed in Jiangsu Province of China.

4G 5G Cell-level Multi-indicator Forecasting based on Dense-MLP