Mobile Broadband Adoption, Performance Measurements and Methodology: A Review

Umoh, Vincent B.; Ekpe, Unwana M.; Davidson, Innocent E.; Akpan, Joseph

doi:10.3390/electronics12071630

Cited by 5 publications

(2 citation statements)

References 69 publications

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“…When making this decision, they typically take a few things into account. The demands and views of their customers' wants and perceptions about price, product, promotion, service quality, customer service, and other aspects that may be influencing their choice of telecommunication service providers must thus be identified by telecommunication firms in developing a sustainable and high performing mobile broadband [3,4]. This would make it possible for the service providers to plan their operations profitably.…”

Section: Introductionmentioning

confidence: 99%

Comparing Two Deep-Learning Models in Discrete Choice Analysis of Customers’ Mobile Plan Preferences

Oliobi,

Chinhamu,

Zewotir

2024

Applied Sciences

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A discrete choice experiment (DCE) was used to investigate students’ preferences for mobile phone plans at a South African university. Upon obtaining the data, this study compares the predictive performance of two machine-learning models for discrete choice analysis and makes recommendations for model selection. Using concepts from blocked fractional factorial designs, a locally optimal DCE was created for the choice sets. This contrasts with alternative ways that, in practice, could be more difficult, especially when there is a large number of attributes. The call rate, data speed, customer service, premiums, and network coverage were the features considered. A total of 180 respondents were chosen from the student population using a two-stage sample approach, and data were gathered through face-to-face interviews. In this study, two deep-learning models are examined to analyze the data, which are the artificial neural network (ANN) and the extreme gradient boosting (XGBoost) models. Root mean square error (RMSE) and mean absolute error (MAE) are used to assess the model fitness, while accuracy, precision, recall and F1 score were used to compare the models’ performance. The results showed that XGBoost performs better compared to ANN in model fitness and prediction. Thus, the use of the XGBoost deep-learning model in choice preference modeling is therefore encouraged.

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Section: Introductionmentioning

confidence: 99%

Comparing Two Deep-Learning Models in Discrete Choice Analysis of Customers’ Mobile Plan Preferences

Oliobi,

Chinhamu,

Zewotir

2024

Applied Sciences

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“…Wireless communication systems have become essential in various areas, such as health, agriculture, finance, education, the Internet of Things, media, and entertainment [ 1 , 2 , 3 , 4 , 5 ]. Mobile communication generations must be updated approximately every ten years as the demand for faster and more reliable connections grows [ 6 ]. The latest generation of wireless communication systems, 5G technology, offers several advantages over previous generations, including higher data rates, reliable connections, and reduced latency in three different usage scenarios (eMBB, uRLLC, and mMTC) [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel High Isolation 4-Port Compact MIMO Antenna with DGS for 5G Applications

Güler

Keskin

2023

Micromachines

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This paper presents the design and realization of a simple and low-profile, four-port multiple-input-multiple-output (MIMO) antenna operating in a mm-wave band supporting 5G communication technologies. As part of the design methodology, the initial stage involved the development of a conventional monopole patch antenna optimized for operation at 26 GHz, which was matched to a 50 Ω stepped feed line. Afterward, a square-shaped defected ground structure (DGS) with semi-circle slots on the edges was placed on the ground to improve the isolation, and the circular and rectangular slots were incorporated as DGSs to optimize the antenna impedance bandwidth. Etching semi-circular-shaped slots on the ground plane achieved more than 34.2 dB isolation in the 26 GHz operating band. In addition, an arrangement of four symmetrical radiating elements was positioned orthogonally to minimize the antenna’s physical size and improve the isolation. The proposed MIMO antenna’s overall dimension was 25 × 25 mm2, which was printed on a Rogers 5880 substrate at a width of 0.787 mm and εr = 2.2. The proposed antenna covered the 5G mm-wave band with a 10 dB bandwidth ranging from 25.28–28.02 GHz, whereas the maximum gain attained for the proposed structure was 8.72 dBi. Additionally, the implementation of these slots effectively mitigated mutual coupling, resulting in reduced envelope correlation coefficient (ECC) values. Furthermore, other MIMO performance metrics, including channel capacity loss (CCL), mean effective gain (MEG), and diversity gain (DG), were analyzed for the proposed structure. The obtained results indicate its suitability for various usage areas, such as smart devices, mobile phones, and sensors in 5G applications.

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