Browsing by Author "E. N. Onwuka"

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Evaluating the effectiveness of machine learning models for path loss prediction at 3.5GHz with focus on feature extraction
(Nigerian Journal of Technology, 2024) F. E. Shaibu; E. N. Onwuka; N. Salawu; Oyewobi S. Stephen
Accurate path loss prediction is vital for efficient resource allocation, interference reduction, and overall network reliability in 5G networks, particularly in the widely deployed mid-band frequency spectrum (such as 3.5 GHz). This study evaluates the effectiveness of machine learning models for path loss prediction at 3.5 GHz with a focus on feature prioritization. A feature selection method, recursive feature elimination, was used to identify significant features from datasets obtained through measurement campaigns, weather stations, 3-D ray tracing, geographical data, and simulations. Out of eighteen features, eleven, including new environmental features, were identified as significant features contributing to path loss. These selected variables were then utilized to optimize and train four common machine learning models (ANN, XGBoost, RF, and k-NN) to evaluate their performance in predicting path loss in a specific urban area called an irregular urban environment. The performance of these models was assessed by comparing their predictions with the measured path loss. The Random Forest model closely matched the measured path loss over the entire path length in both LoS and NLoS scenarios, achieving the lowest MAE of 0.15 dB and RMSE of 0.57 dB in the LoS scenario and 0.62 dB and 1.42 dB in the NLoS scenario, with R2 scores of 0.999995437 and 0.999996828, respectively. This indicates its superior performance in predicting path loss in the urban environment
Mobile Terminal’s Energy: A Survey of Battery Technologies and Power Control Techniques
(2013) Oyewobi S. Stephen; E. N. Onwuka; Onumanyi A. J
In the last few years of the past decade, advancements in electronics technology have increasingly made electronic devices offer high performance with corresponding decrease in size of the electronic devices. However with this increase in performance and decrease in size of electronics devices there is an increase in demand for mobile devices’ battery to keep the device up for longer period in operation and standby times. Research has however shown that advancements in battery technology have not being in tandem with advancements in other fields of electronics. Therefore wherever battery technology fails, it is complemented by electronics and communication technologies. In this work; a survey of mobile terminal energy management techniques and battery technologies was carried out and Dynamic power control algorithm (DPCA) was proposed. Results from simulation showed that DPCA increased battery performance when compared with another power control technique in literature.