Intelligent Base Station Placement in Urban Areas With Machine Learning

Loh, Wei Ren; Lim, Soo Yong; Rafie, Irfan Farhan Mohamad; Ho, Jia Sheng; Tze, Kah Sheng

doi:10.1109/lawp.2023.3281611

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…Path loss is a phenomenon in which the strength of a radio signal between a base station and mobile station decreases as it propagates through space. Predicting path loss is crucial in base-station positioning, because mobile stations require a minimum received signal power level to successfully decode the data received from the base station [ 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Accurate Path Loss Prediction Using a Neural Network Ensemble Method

Kwon,

Son

2024

Sensors

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Path loss is one of the most important factors affecting base-station positioning in cellular networks. Traditionally, to determine the optimal installation position of a base station, path-loss measurements are conducted through numerous field tests. Disadvantageously, these measurements are time-consuming. To address this problem, in this study, we propose a machine learning (ML)-based method for path loss prediction. Specifically, a neural network ensemble learning technique was applied to enhance the accuracy and performance of path loss prediction. To achieve this, an ensemble of neural networks was constructed by selecting the top-ranked networks based on the results of hyperparameter optimization. The performance of the proposed method was compared with that of various ML-based methods on a public dataset. The simulation results showed that the proposed method had clearly outperformed state-of-the-art methods and that it could accurately predict path loss.

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Section: Introductionmentioning

confidence: 99%