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A Heuristic K-Means-based Unsupervised Machine Learning Model for Unmanned Aerial Vehicle Mounted Reconfigurable Intelligent Surface for Enhanced 5G and Beyond Networks Performance

by Raymond Sabogu-Sumah, Opare Kwasi Adu-Boahen, James D. Gadze, Kwame Opuni-Boachie Obour Agyekum, Edmund Y. Fianko, Dennis N.A. Gookyi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 73
Year of Publication: 2025
Authors: Raymond Sabogu-Sumah, Opare Kwasi Adu-Boahen, James D. Gadze, Kwame Opuni-Boachie Obour Agyekum, Edmund Y. Fianko, Dennis N.A. Gookyi
10.5120/ijca2025924584

Raymond Sabogu-Sumah, Opare Kwasi Adu-Boahen, James D. Gadze, Kwame Opuni-Boachie Obour Agyekum, Edmund Y. Fianko, Dennis N.A. Gookyi . A Heuristic K-Means-based Unsupervised Machine Learning Model for Unmanned Aerial Vehicle Mounted Reconfigurable Intelligent Surface for Enhanced 5G and Beyond Networks Performance. International Journal of Computer Applications. 186, 73 ( Mar 2025), 1-8. DOI=10.5120/ijca2025924584

@article{ 10.5120/ijca2025924584,
author = { Raymond Sabogu-Sumah, Opare Kwasi Adu-Boahen, James D. Gadze, Kwame Opuni-Boachie Obour Agyekum, Edmund Y. Fianko, Dennis N.A. Gookyi },
title = { A Heuristic K-Means-based Unsupervised Machine Learning Model for Unmanned Aerial Vehicle Mounted Reconfigurable Intelligent Surface for Enhanced 5G and Beyond Networks Performance },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2025 },
volume = { 186 },
number = { 73 },
month = { Mar },
year = { 2025 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number73/a-heuristic-k-means-based-unsupervised-machine-learning-model-for-unmanned-aerial-vehicle-mounted-reconfigurable-intelligent-surface-for-enhanced-5g-and-beyond-networks-performance/ },
doi = { 10.5120/ijca2025924584 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-03-25T22:41:34+05:30
%A Raymond Sabogu-Sumah
%A Opare Kwasi Adu-Boahen
%A James D. Gadze
%A Kwame Opuni-Boachie Obour Agyekum
%A Edmund Y. Fianko
%A Dennis N.A. Gookyi
%T A Heuristic K-Means-based Unsupervised Machine Learning Model for Unmanned Aerial Vehicle Mounted Reconfigurable Intelligent Surface for Enhanced 5G and Beyond Networks Performance
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 73
%P 1-8
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper investigates the use of Reconfigurable Intelligent Surfaces (RIS) installed on Unmanned Aerial Vehicles (UAVs) for a Non-Orthogonal Multiple Access (NOMA) mobile cellular system coverage extension and sum rate maximization. The UAV only acts as a medium to mount the RIS and therefore does not expend any other energy aside from that used to keep it flying. The study formulates a non-convex optimization problem to maximize the sum rate of the NOMA Cellular system by optimizing the transmit power and the location of the UAV. Due to the complexity of the formulated optimization problem, the study devised a heuristic clustering model using the Angle of Arrival (AoA) of User Equipment (UEs) signals at the Base Station (BS). The simulation results show that the use of the UAV-RIS system improves the coverage probability of the 5G and B5G network. Further, the proposed heuristic clustering technique improves the system sum rate between 8.3 to 87% depending on the transmit power of the BS.

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Index Terms

Computer Science
Information Sciences
UAV
RIS
Heuristic K-means
clustering

Keywords

Machine Learning NOMA 5G B5G Power Domain NOMA Sum Rate AoA Coverage Probability

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