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