Bayesian Network Prediction of Mobile User Throughput in 5G Wireless Networks

Meng, Qingmin; Fang, Xiaoqiang; Yue, Wenjing; Yang, Meng; Wei, Jingcheng

doi:10.1109/icccas.2018.8768972

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…The research presented in paper [41], describes the use of Bayesian Network (BN) for throughput reliability prediction in 5G wireless networks. The BN algorithm is used in this study to predict future test results by estimating parameters such as base station load, user location, and moving speed, which affect the signal-to-noise ratio (SNR) received by users and the signal interference plus noise ratio (SINR).…”

Section: H Bayesian Network Classifiermentioning

confidence: 99%

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

Yuliana,

Iskandar,

Hendrawan

2024

IEEE Access

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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.

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Section: H Bayesian Network Classifiermentioning

confidence: 99%

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

Yuliana,

Iskandar,

Hendrawan

2024

IEEE Access

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“…In recent years, they have emerged as a prominent area of research in artificial intelligence and machine learning. Presently, probabilistic graphical models have found successful applications across various domains such as image analysis, communication systems, and computer science [6] [7]. In the domain of threat modeling, Bayesian networks are commonly employed for modeling and detecting distributed threats [8].…”

Section: Modelingmentioning

confidence: 99%

Research on new power business threat modeling method based on Bayesian networks

Li,

Zhang,

Shao

et al. 2024

Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024)

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The emerging power system, predominantly reliant on renewable energy sources, drives the future diversification of enduse energy structures. However, within this new power system, business terminals encounter various access risks. Terminals within unmanned aerial vehicle (UAV) power inspection systems, distributed photovoltaic business, and vehicular networking are susceptible to network attacks, exposing them to risks of information leakage. To address the security threats prevalent in the emerging power industry, this paper proposes a Bayesian network-based threat modeling approach. Building upon this, a probabilistic graphical model-based threat risk assessment scheme is presented. Furthermore, typical interaction scenarios of novel power system operations are selected to demonstrate the effectiveness of the model by employing Bayesian networks for modeling and simulation validation.

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