LSTM-based Alarm Prediction in the Mobile Communication Network
Xiaoxue Wang, Dong Liang · 2020
With the development of mobile communication network technology, the network scale is expanding, the network complexity is also improved. Once the network fails, it will cause a series of devices to generate numerous alarms. Therefore, it is particularly important to accurately predict the alarms, which can prevent the upcoming network alarms in advance or take other measures to reduce losses. Traditional network diagnosis mainly depends on network administrators, which has many subjective factors and low efficiency, and needs to train experienced technicians, which leads to long cycle and high cost. In addition, the manual method is difficult to use a large amount of historical data. In order to solve the problem of network alarm prediction more intelligently, data mining technology is an effective solution, which can mine valuable information from a large number of historical network alarm data. In this paper, combined with the actual network alarm log data of operators and considering the time correlation of alarms, a two-class network alarm prediction (LAB-NAP) algorithm based on LSTM and association algorithm is proposed, which adopts the method of combining LSTM-based two-class model with network alarm association mode. The LAB-NAP algorithm is compared with LSTM-based two-class network alarm prediction (LB-NAP) algorithm and LM-NAP, and the performance of three network alarm prediction modeling methods on the same data set is compared. It is verified that the LAB-NAP method proposed in this paper balances the prediction effect and training time in the large-scale dataset.