Handover management in ultra-dense 5G networks

In next generation 5G networks, there are a high number of mobile nodes and these high number of nodes produce ultra-high data traffic. Network densification is a promising architectural solution that can help meet such high data transfer requirements. In such scenarios, very high number of small cells (micro/pico/home) are located within the coverage area of a macrocell. This design results in a low-cost, low-power operation, with a significant increase in the total capacity, coverage, and energy efficiency of the network. On the other hand, this ultra-dense network architecture leads to different problems in different aspects. More specifically, this high number of small cell deployments in such ultra-dense networks (UDNs) may result in unnecessary, frequent, and ping-pong handovers, with additional problems related to increased delay and total failure of the handoff process. That can also lead to high packet loss, dropped and blocked calls. Additionally, due to the separation of control and data signaling in 5G technology, the handover operation must be executed in both tiers. For these reasons, handover management in this specific 5G deployment becomes a challenging issue. We believe that having an optimal and accurate handover management strategy, handover delay and overhead in these deployments can be dramatically decreased. Therefore, in addition to the architectural enhancements, the 5G requires improvements in terms of the network management. Accordingly, to solve the handover delay problem, in this thesis, we propose an SDN-based mobility and available resource estimation strategy. Here, we estimate the neighbor eNB transition probabilities of the mobile node and their available resource probabilities by using a Markov Chain formulation. This allows a mathematically elegant framework to select the optimal eNBs and then assign these to the mobile nodes virtually, with all connections completed through the use of OpenFlow Tables. Finally, we compare the conventional LTE and our proposed handover strategies by analyzing the observed delays according to the densification ratio parameter. Also, we analyze the handover failure ratios of both strategies according to the user number. Results reveal that the proposed strategy reduces the handover delay and failures by 52% and 21% compared to the conventional approach. Moreover, to solve the frequent, unnecessary and ping-pong handover problems, in this thesis, we propose an optimal eNodeB selection mechanism for 5G intra-macrocell handovers based on spatio-temporal estimations. In this approach, Kriging Interpolator with Semivariogram Analysis is supported by the Autoregressive model for selecting the optimal eNodeB before the connection setup. The stochastic and statistical behaviors of Kriging Interpolation provide better modeling performance. These operations are performed by the proposed EnodeB Estimation Entity. Also, these estimations 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 decreased by 35%, 37%, and 17% respectively compared to the conventional handover method.