EMI-RNN – Enhanced Multilayer Independently Recurrent Neural Networks for Handover Optimization in 5G Ultra Dense Networks

Dr K Madhavi
2019 International Journal of Research in Advent Technology  
With the rapid developments in the wireless communication technology, ultra dense networks (UDN) are identified as the cutting edge platform of research, in order to achieve the network capacity goals of 5G-fifth generation cellular networks. It is envisioned that the deployment of UDN plays a vital role in the development of 5G cellular networks to satisfy the data rate demands from the users. In an UDN, the inevitable deployment of low power base stations enables the users with many problems
more » ... n terms of Quality of experience (QoE), Quality of Service (QoS) along with the major problem of frequent handovers due to the mobility of users. Addressing this problem of handover optimization in the deployment of UDN's this paper proposes a novel framework EMI-RNN based on the recurrent neural networks to predict the handovers in advance and avoid the call drop rate. Simulation results presented in the study exhibits the performance and prediction rate of the proposed framework.
doi:10.32622/ijrat.72201988 fatcat:zvb6gzcg7vfy3plq3ydwhkw5mu