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Research Publications (Accounting and Informatics)

Permanent URI for this collectionhttp://ir-dev.dut.ac.za/handle/10321/212

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Author: search.filters.author.Olugbara, Oludayo O.Subject: search.filters.subject.Evolution

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    Performance of local averaging handover technique in long term evolution networks
    (SAIEE, 2015) Elujide, Israel Oludayo; Olugbara, Oludayo O.; Nepal, Thiruthlall; Owolawi, Pius Adewale
    In this paper, we investigate the performance of an alternative received signal filtering technique based on local averaging to improve the quality of handover decisions in Long Term Evolution (LTE) networks. The focus of LTE-Advance (LTE-A) networks is to provide enhanced capacity and reliability of radio access as well as broadband demand for mobile users. The necessity to maintain quality of service, especially for the delay sensitive data services and applications, has made mobility and handover decisions between the base stations in the LTE networks critical. Unfortunately, several handover decision algorithms in the LTE networks are based on the Reference Signal Received Power (RSRP) obtained as a linear averaging over the reference signals. The critical challenge with the linear averaging technique is that the limited reference signal available in the downlink packet introduces an estimation error. This estimation error is a result of the effects of linear averaging on propagation loss components in eliminating fast-fading from the received signals. Moreover, prompt and precise handover decisions cannot be based on inaccurate measurement. The standardized LTE layer 3 filtering technique is applied to the local averaged layer 1 signal to render it suitable for LTE handover decisions. The local averaging technique produces better handover than the linear averaging technique in terms of the reduced number of handover failures, improved high spectral efficiency and increased throughput, especially for cell-edge users with high speeds. The findings of this study suggest that the local averaging technique enhances mobility performance of LTE-Advance networks.
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    Multiobjective optimization of crop-mix planning using generalized differential evolution algorithm
    (2015) Adekanmbi, Oluwole; Olugbara, Oludayo O.
    This paper presents a model for constrained multiobjective optimization of mixed-cropping planning. The decision challenges that are normally faced by farmers include what to plant, when to plant, where to plant and how much to plant in order to yield maximum output. Consequently, the central objective of this work is to concurrently maximize net profit, maximize crop production and minimize planting area. For this purpose, the generalized differential evolution 3 algorithm was explored to implement the mixed-cropping planning model, which was tested with data from the South African grain information service and the South African abstract of agricultural statistics. Simulation experiments were conducted using the non-dominated sorting genetic algorithm II to validate the performance of the generalized differential evolution 3 algorithm. The empirical findings of this study indicated that generalized differential evolution 3 algorithm is a feasible optimization tool for solving optimal mixed-cropping planning problems.