Machine Learning-Based Radio Coverage Prediction in Urban Environments
Article Properties
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DOI (url)
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Publication Date2020/12/01
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Indian UGC (journal)
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Refrences35
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Citations2
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Sanaz Mohammadjafari ORCID (unauthenticated)
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Sophie Roginsky
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Emir Kavurmacioglu ORCID (unauthenticated)
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Mucahit Cevik
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Jonathan Ethier
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Ayse Basar Bener
Cite
Mohammadjafari, Sanaz, et al. “Machine Learning-Based Radio Coverage Prediction in Urban Environments”. IEEE Transactions on Network and Service Management, vol. 17, no. 4, 2020, pp. 2117-30, https://doi.org/10.1109/tnsm.2020.3035442.
Mohammadjafari, S., Roginsky, S., Kavurmacioglu, E., Cevik, M., Ethier, J., & Bener, A. B. (2020). Machine Learning-Based Radio Coverage Prediction in Urban Environments. IEEE Transactions on Network and Service Management, 17(4), 2117-2130. https://doi.org/10.1109/tnsm.2020.3035442
Mohammadjafari, Sanaz, Sophie Roginsky, Emir Kavurmacioglu, Mucahit Cevik, Jonathan Ethier, and Ayse Basar Bener. “Machine Learning-Based Radio Coverage Prediction in Urban Environments”. IEEE Transactions on Network and Service Management 17, no. 4 (2020): 2117-30. https://doi.org/10.1109/tnsm.2020.3035442.
Mohammadjafari S, Roginsky S, Kavurmacioglu E, Cevik M, Ethier J, Bener AB. Machine Learning-Based Radio Coverage Prediction in Urban Environments. IEEE Transactions on Network and Service Management. 2020;17(4):2117-30.
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Performance evaluation of radio propagation models on gsm network in urban area of Lagos, Nigeria | 2014 | |||
| Data analysis, including statistics | 1968 | |||
| Convolutional neural network for prediction method of path loss characteristics considering diffraction and reflection in an open-square environment | 2019 | |||
| Radio propagation prediction model using convolutional neural networks by deep learning | 2019 | |||
| A proposal for path loss prediction in urban environments using support vector regression | 2014 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Electromagnetic radiation estimation at the ground plane near fifth‐generation base stations in China by using machine learning method | IET Microwaves, Antennas & Propagation |
| 2024 | |
| Comparative Analysis of Machine Learning Algorithms for 5G Coverage Prediction: Identification of Dominant Feature Parameters and Prediction Accuracy | IEEE Access |
| 2024 |
Citations Analysis
The category
Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks 2
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Electromagnetic radiation estimation at the ground plane near fifth‐generation base stations in China by using machine learning method and was published in 2024. The most recent citation comes from a 2024 study titled Electromagnetic radiation estimation at the ground plane near fifth‐generation base stations in China by using machine learning method. This article reached its peak citation in 2024, with 2 citations. It has been cited in 2 different journals, 100% of which are open access. Among related journals, the IET Microwaves, Antennas & Propagation cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.