3G Cellular Network Fault Prediction Using LSTM-Conv1D Model

Abstract

Cellular network plays an important role in daily life by exploring digital world of communication. Cellular network technology continuously evolves in past decades from 1 to 5G and beyond. The evolution results in more network accessibility and data utilization. As the availability of network, mobility, and portability of cellular devices are increasing, the network traffic will also be increasing. Higher the transmission rates, higher will be the fault occurrence possibility. Monitoring network parameters and finding fault in cellular network are key factor in determining consistency of network. Cellular network which is highly dynamic than usual networks needs intelligent way of fault handling as the human over head will be unpredictable and very high. Modeling intelligent network fault identification system can simplify human efforts and improve efficiency with better accuracy. The research is on real-time data of 3G cellular network including various network parameters like uplink threshold and identifies the behavior of data usual or unusual to predict the fault occurrence. The study is on various LSTM techniques such as bidirectional LSTM, vanilla LSTM, and stacked LSTM combined with time distributed Conv1D.