Comparative Analysis of Machine Learning Algorithms for 5G Coverage Prediction: Identification of Dominant Feature Parameters and Prediction Accuracy

Abstract

5G technology is a key factor in
delivering faster and more reliable wireless
connectivity. One crucial aspect in 5G network
planning is coverage prediction, which enables
network providers to optimize infrastructure
deployment and deliver high-quality services to
customers. This study conducts a comprehensive
analysis of machine learning algorithms for 5G
coverage prediction, focusing on dominant
feature parameters and accuracy. Notably, the
Random Forest algorithm demonstrates superior
performance with an RMSE of 1.14 dB, MAE of
0.12, and R2 of 0.97. The CNN model, the
standout among deep learning algorithms,
achieves an RMSE of 0.289, MAE of 0.289, and
R2 of 0.78, showcasing high accuracy in 5G
coverage prediction. Random Forest models
exhibit near-perfect metrics with 98.4% accuracy,
precision, recall, and F1-score. Although CNN
outperforms other deep learning models, it
slightly trails Random Forest in performance. The
research highlights that the final Random Forest
and CNN models outperform other models and
surpass those developed in previous studies.
Notably, 2D Distance Tx Rx emerges as the most
dominant feature parameter across all
algorithms, significantly influencing 5G coverage
prediction. The inclusion of horizontal and
vertical distances further improves prediction
results, surpassing previous studies. The study
underscores the relevance of machine learning
and deep learning algorithms in predicting 5G
coverage and recommends their use in network
development and optimization. In conclusion,
while the Random Forest algorithm stands out as
the optimal choice for 5G coverage prediction,
deep learning algorithms, particularly CNN, offer
viable alternatives, especially for spatial data
derived from satellite images. These accurate
predictions facilitate efficient resource allocation
by network providers, ensuring high-quality
services in the rapidly evolving landscape of 5G
technology. A profound understanding of
coverage prediction remains pivotal for
successful network planning and reliable service
provision in the 5G era.

The rapid development of 5G technology is
transforming industries and enhancing connectivity
through higher data speeds, ultra-low latency, and
increased network capacity.
Efficient deployment of 5G networks requires
accurate coverage prediction, ensuring seamless
service across diverse geographical areas.
Traditional coverage prediction methods often rely
on time-consuming and resource-intensive
simulations, making them impractical for large-scale
deployment.
Machine learning (ML) offers promising alternatives
for real-time and scalable 5G coverage prediction by
analyzing large datasets and identifying patterns that
influence network performance.
ML algorithms can improve the accuracy of coverage
predictions by considering complex environmental