Browsing by Author "Lysko Albert"

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    Open Access
    Admission Control in Sliced Networks, with Predictive Analytics
    (2023) Ngufor, Perose; Mwangama, Joyce; Lysko Albert
    Over the years, the telecommunications industry has constantly adapted to accommodate the rising demand for more specialised network connectivity. Network slicing was introduced as a solution for providing specialised networks to customers. However, network slicing has a set of objectives which require that legacy network functions be revisited and updated to support network slicing. One such function is admission control. This work proposes two admission control algorithms and investigates how the admission control function can be improved by incorporating traffic forecasting into the admission control process. In this work, we present the state-of-the-art in admission control in sliced networks and the state-of-the-art of the application of predictive analytics to admission control. We design and evaluate two intra-slice admission control algorithms namely, the Decision Matrix algorithm and the Utility Index algorithm. A real IP network dataset, containing network flows collected from the University of Cauca, Popayán network is used for the simulation and evaluation of these admission control algorithms. The proposed admission control algorithms presented various strengths, with the Utility Index algorithm being highly profitable to the operator and the Decision Matrix algorithm being suitable for traffic with a large proportion of high priority traffic. A traffic forecasting model was implemented based on the Holt-Winters Exponential Smoothing predictive model. This forecasting model was trained using the network data from the real IP network dataset and then incorporated into the admission control process. For prediction-based admission control, the traffic forecasting model was used to forecast resource requirements of future network traffic in each slice and pre-emptively make provisions for the upcoming traffic. The performance of the intra-slice admission control algorithms with and without the influence of traffic forecasting was analysed and it was found that the use of predictive analytics to predict future slice traffic allows for dynamic allocation of slice resources. Prediction-based admission control, when compared to the admission control without predictions, showed better performance in terms of probability of blocking, system utilisation, and profitability to the operator.
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    Open Access
    Mobility Management in 5G Heterogeneous Networks: A Handover Scheme for Reducing Handover Failures
    (2023) Monaheng, Reitumetse; Ramotsoela, Daniel; Lysko Albert
    Mobile/cellular communications have become very popular and advanced significantly in recent decades. Communications will inevitably evolve into the next generation of wireless communications, in which users will be connected via heterogeneous networks. Small cell (SC)-based ultra-dense heterogeneous networks (HetNets), which are underlaid on the coverage of a macro cell, are among the most promising alternatives for increasing capacity and coverage in 5G cellular networks. An ultra-dense network (UDN) refers to a setup in which the density of Radio Access Technologies (RATs) in a geographical area is increased. As a result, the areas covered by individual RATs begin to overlap. UDNs are regarded as a critical technology for 5G due to their capability to enhance connection quality and expand system capacity. There, small base stations (SBS) are located close to each other in a UDN. As a result, signals from two or more SBS can be received by a single user equipment (UE). This could lead to severe inter-cell interference. This usually happens when a handover has been delayed. If that happens, then, the handover command message (HCM) will not be received by the UE from its serving BS and, a handover failure (HOF) will be declared. Inter-cell interference is so severe in dense small cells that it occurs frequently, thus degrading signal quality and hence resulting in poor services to users. This dissertation focuses on reducing handover failures due to the unavailability of resources at the target cell when a user equipment moves out of small cells, which have the highest rate of handover failure. By utilising a semi-Markov mobility prediction algorithm for handover management, we have implemented a handover scheme that reduces the number of handover calls dropping. This paves the way for resources in the target base station to be reserved beforehand and thus reducing the number of handover failures significantly. The results of the proposed scheme were validated with a simulation in MATLAB with an environment consisting of small cells with different radio access technologies. From the simulation results, the prediction of the next location of the user equipment yielded lower handover call dropping as compared to new calls blocking as they were not predicted and hence resources not reserved for them.