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Computer Science > Networking and Internet Architecture

arXiv:2209.05989 (cs)
[Submitted on 22 Jul 2022]

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

Authors:Jiacheng Yin, Wenwen Li, Xidong Wang, Xiaozhou Ye, Ye Ouyang
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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.
Comments: 9 pages, 6 figures. Published at ITU Journal on Future and Evolving Technologies, viewable at this https URL
Subjects: Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG)
Cite as: arXiv:2209.05989 [cs.NI]
  (or arXiv:2209.05989v1 [cs.NI] for this version)
  https://doi.org/10.48550/arXiv.2209.05989
Journal reference: ITU Journal on Future and Evolving Technologies, Volume 3 (2022), Issue 3 - AI and machine learning solutions in 5G and future networks, Pages 21-29

Submission history

From: Jiacheng Yin [view email]
[v1] Fri, 22 Jul 2022 05:03:27 UTC (3,040 KB)
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