Optimal enodeb estimation for 5G intra-macrocell handover management

In next generation 5G intra-macrocell deployment due to th high number of small cells existing in the network, one of th main concerns is the increased handover rate, followed b frequent, unnecessary and ping-pong handover challenges That can also lead to high packet loss, dropped and blocke calls. Moreover, in 5G intra-macrocell deployments, due t the control and data channel separation handover operatio must be executed in two tiers (both data and control channels) For these reasons, handover management in this spec 5G deployment becomes a challenging issue. We believ that,having an optimal and accurate eNodeB estimation handover overhead in these deployments can be dramaticall decreased. In this paper, we propose an optimal eNodeB selectio mechanism for 5G intra-macrocell handovers base on spatio-Temporal estimations. In this approach, Krigin Interpolator with Semivariogram Analysis is supported b the Autoregressive model for selecting the optimal eNode before the connection setup. The stochastic and statistica behaviors of Kriging Interpolation provide better modelin performance. These operations are performed by the propose EnodeB Estimation Entity. Also, these estimation are applied to both the data and control channels independently As a result of the proposed management scheme unnecessary, frequent and ping-pong handover rates are decrease by %35, %37 and %17 respectively compared to th traditional handover method.