Machine learning-based methods for path loss prediction in urban environment for LTE networks
This work presents prediction path loss models in an urban environment for cellular
networks with the help of machine learning methods. For this goal, Support Vector …
networks with the help of machine learning methods. For this goal, Support Vector …
Fusing diverse input modalities for path loss prediction: A deep learning approach
Tabular data and images have been used from machine learning models as two diverse
types of inputs, in order to perform path loss predictions in urban areas. Different types of …
types of inputs, in order to perform path loss predictions in urban areas. Different types of …
Explainable deep-learning-based path loss prediction from path profiles in urban environments
RT Juang - Applied Sciences, 2021 - mdpi.com
This paper applies a deep learning approach to model the mechanism of path loss based on
the path profile in urban propagation environments for 5G cellular communication systems …
the path profile in urban propagation environments for 5G cellular communication systems …
Path loss prediction in smart campus environment: Machine learning-based approaches
This paper presents a novel application of various machine learning (ML)-based
approaches towards prediction of path loss (PL) parameter for a smart campus environment …
approaches towards prediction of path loss (PL) parameter for a smart campus environment …
Random forests based path loss prediction in mobile communication systems
R He, Y Gong, W Bai, Y Li… - 2020 IEEE 6th International …, 2020 - ieeexplore.ieee.org
When deploying communication systems, an accurate wireless propagation model is
important to ensure the quality of service covering the region. Due to the complex radio …
important to ensure the quality of service covering the region. Due to the complex radio …
Pl-gan: Path loss prediction using generative adversarial networks
Accurate prediction of path loss is essential for the design and optimization of wireless
communication networks. Existing path loss prediction methods typically suffer from the …
communication networks. Existing path loss prediction methods typically suffer from the …
An ensemble machine learning approach for enhanced path loss predictions for 4G LTE wireless networks
Accurate path loss prediction models are indispensable in modern wireless communication
systems. In recent times, several path loss prediction models have been proposed to …
systems. In recent times, several path loss prediction models have been proposed to …
On the assessment of ensemble models for propagation loss forecasts in rural environments
This letter assesses different ensemble models, exploited to forecast propagation loss in
rural environments. Stacking, voting, bagging, and gradient boosted trees ensemble …
rural environments. Stacking, voting, bagging, and gradient boosted trees ensemble …
Path loss modeling based on neural networks and ensemble method for future wireless networks
In light of the technological advancements that require faster data speeds, there has been
an increasing demand for higher frequency bands. Consequently, numerous path loss …
an increasing demand for higher frequency bands. Consequently, numerous path loss …
Applying machine learning to LTE traffic prediction: Comparison of bagging, random forest, and SVM
N Stepanov, D Alekseeva, A Ometov… - … Congress on Ultra …, 2020 - ieeexplore.ieee.org
Today, a significant share of smartphone applications use Artificial Intelligence (AI) elements
that, in turn, are based on Machine Learning (ML) principles. Particularly, ML is also applied …
that, in turn, are based on Machine Learning (ML) principles. Particularly, ML is also applied …