A Hybrid Recurrent Neural Network Architecture for the Prediction of Subscriber Traffic in a Mobile Telecommunication Network

Abstract

The accurate prediction of subscriber traffic is a critical challenge in telecommunication networks, as it is essential for making informed decisions to maintain consistent service quality. Various artificial intelligence (AI) methods have been explored for this purpose, with recurrent neural networks (RNNs) being particularly popular. However, RNNs often suffer from the gradient vanishing problem, which reduces their effectiveness in processing long sequences of data. Long short–term memory (LSTM) networks partially address this issue by controlling the propagation of learning errors, but they still face challenges related to convergence speed and residual learning errors. To address these limitations, this study proposes a new hybrid RNN architecture that combines features of Jordan and Frasconi networks for improved subscriber traffic prediction. The proposed architecture integrates two sublayers, a sigmoid and a Gaussian function, within a hidden layer that forms a loop. This configuration enables the network to leverage incremental learning through restricted Coulomb energy (RCE) at the local level, while also benefiting from global learning through backpropagation. The architecture was tested on a dataset consisting of cellular traffic data collected from a telecommunication operator in Cameroon. The results demonstrated that the hybrid RNN model outperforms traditional Jordan and Frasconi networks, achieving a root mean square error (RMSE) of 14.18 and a mean absolute error (MAE) of 8.61 using the Adam optimizer with a batch size of 64. In addition, the hybrid model showed superior performance in terms of RMSE, MAE, and convergence speed compared to the existing models. These findings suggest that the proposed model could support telecom operators in proactive congestion management and resource optimization.