This paper presents statistical path loss models derived from experimental data collected in Port Harcourt in South-South region of Nigeria from 10 existing microcells operating at 876 MHz. The results of the measurements were used to develop path loss models for the urban (Category A) and the suburban (Category B) areas of Port Harcourt. The measurement results showed that the Pathloss increases by 35.5dB and 25.7dB per decade in the urban (Category A) and suburban (Category B) areas respectively. Variations in path loss between the measured and the predicted values from the Okumura-Hata model were calculated by finding the mean square errors (MSE) to be 10.7dB and 13.4dB for the urban and suburban terrains respectively. These variations (errors) were used to modify the Okumura-Hata models for the two terrain categories. Comparing the modified Hata model with the measured values for the two categories showed a better result. The developed statistical Pathloss models or the modified Hata models can be used in the urban and suburban areas of South-South Nigeria.
This paper evaluates the interference and noise suppression capability of uniform linear array adaptive beamforming antenna, at base stations of WCDMA mobile communication system. The signal-to-interference and noise ratio (SINR) was the performance index used for the evaluation and analysis. SINR was investigated for a conventional narrow band beam former by varying the number of antenna array elements, the inter-element spacing and number of interfering signals or users. The results were compared with that of omni-directional antenna. The graph obtained showed significant improvement in SINR as the number of antenna elements increases in the presence of large interferers for odd numbered array.Index Terms-Adaptive antenna arrays, uniform linear arrays, beamforming, interference suppression and noise reduction capability.
Contending with Non-Technical Losses (NTL) is a major problem for electricity utility companies. Hence providing a lasting solution to this menace motivates this and many more research work in the electricity sector in recent times. Non-technical losses are classed under losses incurred by the electricity utility companies in terms of energy used but not billed due to activities of users or malfunction of metering equipment. This paper therefore is aimed at proffering a solution to this problem by first detecting such loopholes via the analysis of consumers’ consumption pattern leveraging Machine learning (ML) techniques. Support vector machine classifier was chosen and used for classifying the customers’ energy consumption data, training the system and also for performing predictive analysis for the given dataset after a careful survey of a number of machine learning classifiers. A classification accuracy (and subsequently, class prediction) of 79.46% % was achieved using this technique. It has been shown, through this research work, that fraud detection in Electricity monitoring, and hence a solution to non-technical losses can be achieved using the right combinations of Machine Learning techniques in conjunction with AMI technology.
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