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

TL;DR

This paper introduces a dense-MLP neural network approach for accurate cell-level multi-indicator traffic forecasting in 4G/5G networks, enabling proactive network management and energy saving.

Contribution

It proposes a novel dense-MLP model with additional fully-connected layers for improved forecasting accuracy in large-scale cellular networks.

Findings

Achieved the highest weighted MAPE score (0.2484) in the ITU-T challenge.

Successfully forecasted traffic indicators for 13,000 cells using six-month historical data.

Integrated the model into a real-world energy-saving system deployed in Jiangsu, China.

Abstract

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.