Comparative Study of Machine Learning Techniques for Path Loss Prediction
| dc.contributor.advisor | Dereje Hailemariam (PhD) | |
| dc.contributor.author | Ademe Wondimneh | |
| dc.date.accessioned | 2024-03-12T15:15:29Z | |
| dc.date.available | 2024-03-12T15:15:29Z | |
| dc.date.issued | 2023-11 | |
| dc.description.abstract | Path loss is the term used to describe the difference in signal strength between transmitted and received. Predicting this loss is a crucial task in wireless and mobile communication to gather data for resource allocation and network planning. Deterministic and empirical models are the two fundamental propagation models that are used to calculate path loss. There is a trade-off between accuracy and computing complexity between these models. Machine learning models reflect a classic conflict between accuracy and complexity and have significant potential in path loss prediction because they can learn complicated non-linear correlations between input properties and target values. This study investigates the application of machine learning techniques for path loss prediction in Addis Ababa LTE networks. Artificial neural networks (ANNs), random forest regression (RFR), and multiple linear regressions (MLR) are employed as machine learning models and compared with the widely used COST 231 empirical model. Data for training and testing is obtained through measurements from Addis Ababa LTE networks. The performance of the proposed models is evaluated using statistical metrics such as root mean squared error (RMSE), mean absolute error (MAE), and R-squared (R2). The results demonstrate that the RFR model outperforms the other models in terms of prediction accuracy, achieving an MAE of 3.48, an RMSE of 5.35, and an R2 of 0.77. The ANN model also exhibits satisfactory performance with an MAE of 4.19, an RMSE of 5.78, and an R2 of 0.71. The Cost 231 model, on the other hand, exhibits lower prediction accuracy. In terms of computational complexity, ANNs are found to be the most computationally intensive, while MLR is the simplest model among the evaluated machine learning models. RFR falls between ANNs and MLR in terms of computational complexity. | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/2386 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | Propagation models | |
| dc.subject | Path loss prediction | |
| dc.subject | Machine learning | |
| dc.subject | ANN | |
| dc.subject | RFR | |
| dc.subject | MLR | |
| dc.title | Comparative Study of Machine Learning Techniques for Path Loss Prediction | |
| dc.type | Thesis |