Deep learning model for traffic flow prediction in wireless network

Abstract

In wireless networks, the traffic metrics often play a significant role in forecasting the traffic condition in traffic management systems. The accuracy of prediction in data-driven model gets reduced when it is influenced by non-routing or non-recurring traffic events. The analytical data model used in the proposed method takes into account not only traffic volume and congestion, but also the characteristics of individual applications and user behaviour. This allows for more accurate traffic prediction and better traffic management in wireless networks. The simulation conducted in the paper evaluates the performance of the proposed method in terms of connection success probability and latency. The results show that the proposed method achieves a connection success probability of 93% and a latency of less than 2 ms, demonstrating its effectiveness in improving traffic prediction and management in wireless networks.