PurposeThis study explores challenges facing the applicability of deep learning (DL) in software-defined networks (SDN) based campus networks. The study intensively explains the automation problem that exists in traditional campus networks and how SDN and DL can provide mitigating solutions. It further highlights some challenges which need to be addressed in order to successfully implement SDN and DL in campus networks to make them better than traditional networks.Design/methodology/approachThe study uses a systematic literature review. Studies on DL relevant to campus networks have been presented for different use cases. Their limitations are given out for further research.FindingsFollowing the analysis of the selected studies, it showed that the availability of specific training datasets for campus networks, SDN and DL interfacing and integration in production networks are key issues that must be addressed to successfully deploy DL in SDN-enabled campus networks.Originality/valueThis study reports on challenges associated with implementation of SDN and DL models in campus networks. It contributes towards further thinking and architecting of proposed SDN-based DL solutions for campus networks. It highlights that single problem-based solutions are harder to implement and unlikely to be adopted in production networks.
BSTRACTThis paper focuses on evaluating the performance of a cellular network using power control based frequency reuse partitioning (FRP) in downlink (DL). In our work, in order to have the realistic environment, the spectral efficiency of the system is evaluated through traffic analysis, which most of the previous works did not consider.To further decrease the cell edge user's outage, the concept of power ratio is introduced and applied to the DL FRP based cellular network. In considering network topology, we first divide the cell coverage area into two regions, the inner and outer regions. We then allocate different sub-bands in the inner and outer regions of each cell. In the analysis, for each zone ratio, the performance of FRP system is evaluated for the given number of power ratios. We consider performance metrics such as call blocking probability, channel utilization, outage probability and effective throughput. The simulation results show that there is a significant improvement in the outage experienced by outer UEs with power control scheme compared to that with no power control scheme and an increase in overall system throughput.
Recently, there has been an increase of enrollment rate in government schools, as a result of fee free and expansion of compulsory basic education to form four in Tanzania. However, the completion rate of students is highly affected by extreme dropout rate. Researchers in previous studies have explored the causes of school dropout, and they came with general recommendation based on treatment measures. This study, however, deals with predictive measures in which classification algorithm is used in developing dropout predictive system. The targeted population of this study was obtained by employing purposive and non-probability sampling techniques. The study was guided by system theory and conducted in four councils of Tabora region in Tanzania because of high rate school dropout reported in the previous studies. After the analysis, it has been observed that social factors and academic factors have strong impact on the targeted variable dropout time. The study recommends the use of dropout predictive system in secondary schools so as to predict future outcomes of students earlier.
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