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Path Loss Measurement Validation Using Naïve Bayes Classification for High-Speed Rail

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This work proposes path loss measurement validation for high-speed rail wireless communication using Naïve Bayes Classification Method. Path loss propagation model is a major component in wireless network planning in order to meet expected service level requirements and optimized cell towers distribution. However, path loss in high-speed rail propagates rapidly because of unexpected environmental shifts, high mobility, and unique channel modelling. All these unique characteristics lead to inaccuracies and uncertainties in the path loss measurement. Naïve Bayes Classification with Gaussian Classifier offers a better solution in validating path loss measurement than the conventional method for typical wireless communication, which provides statistical descriptions of site-specific environments. The measurement dataset has been taken as continuous values and it is assumed to be distributed according to normal distribution. The dataset has been collected from a running high-speed train. Data discretization has been used as a pre-processing step of transforming continuous data attribute values into a finite set of intervals consisting of path loss in maximum and minimum speed for accuracy or inaccuracy class. The satisfying result of the best performance has been obtained in Accuracy Value (0.9114), Precision Value (0.9147), Recall Value (0.8772), F1-score (0.8956), and Area Under Curve score (0.83). The results have shown that the Naive Bayes method can potentially replace the conventional method in validating path loss measurement, especially for high-speed rail.
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Gaussian Classifier; High-Speed Rail; Measurement Validation; Path Loss; Naïve Bayes

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M. Zhang, Q. Zhang, Y. Lv, W. Sun, H.Wang, An AI Based High Speed Railway Automatic Train Operation System Analysis and Design. 2018 International Conference on Intelligent Rail Transportation (ICIRT), December 12-14, 2018, Singapore.

Y. Zhang, J. Wen, G. Yang, Z. He, and J. Wang, Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion, Applied Sciences, vol. 9 n. 9. 2019, pp. 1-18.

J. Wen, Y. Zhang, G. Yang, Z. He, W. Zhang, Path Loss Prediction Based on Machine Learning Methods for Aircraft Cabin Environments, IEEE Access, vol. 7, 2019 pp. 159251-159261, 2019.

Joao Rafael De Figueiredo Cabral, A Machine Learning Approach for Path Loss Estimation in Emerging Wireless Networks. Ph.D. dissertation, Faculdae De Engenharia Da Universidade Do Porto, February 22, 2019.

J. Xie, Z. Song, Y. Li, Y. Zhang, H.Yu, J. Zhan, Z. Ma, Y. Qiao, J. Zhang, J. Guo,A Survey On Machine Learning-Based Mobile Big Data Analysis: Challenges and Applications. Wireless Communications and Mobile Computing, vol. 2018 n 8738613, 2018, pp 1-19.

N. Mehdiyev, D. Enke, P. Fettke, P. Loos, Evaluating Forecasting Methods by Considering Different Accuracy Measures, Procedia Computer Science, vol. 95, 2016, pp. 264-271.

Ayotunde Oluwaseun Laiyemo, High Speed Moving Networks in Future Wireless Systems. Ph.D. dissertation ,Universitatis Ouluensis, Oulu, 2018.

J. Hu, W. Zhong, Q. Liu, Study On the Performance of High Rail Damage of Four Different Materials. Advances in Materials Science and Engineering, vol. 2018 n 5016414, 2018, pp 1-7.

H. Ma, Y. Xu, Research on Inter-Carrier Interference Elimination Algorithms in High Speed Railway Scenarios, International Conference on Measuring Technology and Mechatronics Automation, ICMTMA, February 28-29, 2020, Phuket, Thailand.

M. S. Sneppe, D. Namiot, On 5G Projects for Urban Railways, 22nd Confererence of Open Innovations Association (FRUCT), April 9-13, 2018, Petrozavodsk, Russia.

T. T. Huong, N. M. Dat, To T. Thao, N. D. Viet, V. V. Yem, Compensating Doppler Frequency Shift of High Speed Rail Communication, International Journal of Applied Engineering Research, vol. 13 n 17, 2018 , pp. 13344-13348

T. Zhou, C. Tao, K. Liu , Analysis of Non Stationary Characteristics for High-Speed Railway Scenarios. Wireless Communications and Mobile Computing, vol. 2018 n. 1729121, 2018, pp. 1-7.

Y. Zhang, T. Zheng, P. Dong, H. Luo, Z. Pang, Comphrensive Analysis on Heterogeneous Wireless Network in High Speed Scenarios, Wireless Communications and Mobile Computing, vol. 2018 n. 4259510, 2018, pp. 1-12.

F.J. Yang, An Implementation of Naive Bayes Classifier, International Conference on Computational Science and Computational Intelligence (CSCI), December 13-15, 2018, Las Vegas, USA.

S. Shareetunnisa, S. Chinna Gopi, A. Viswanadapalli, P. K. Nelapati, A Naive Bayes Algorithm of Data Mining Method For Clustering of Data. International Journal of Advanced Science and Technology, vol. 29 n 6, 2020, pp. 8021-8028.

Simona Salicone, Marco Prioli, Measuring Uncertainity Within The Theory of Evidence, Springer Series in Maesurement Science and Technology 1st edition, 2018

Duntsch, G. Gediga, Confusion Matrices and Rough Set data Analysis. arXiv:1902.01487,2019

R. Yacouby, D. Axman, Probabilistic Extention of Precision,Recall and F1-Score for More Thorough Evaluation of Classification Models. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, November 16-19, 2020, Edinburg, UK.

Y. Lei, Y. Ying, Stochastic Proximal AUC Maximisation. arXiv: 1906.06053v1, 2019

Md. A. Haque, Md. A. Rahman,Md. S. Siddik, Non Functional Requirement Classifications with Feature Extraction and Machine Learning-An Empirical Study. International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), May 3-5, 2019, Dhaka, Bangladesh.

El Badlaoui, O., Maazouzi, A., Hammouch, A., Cepstral Features Extraction for Heart Sounds Classification, (2018) International Review of Electrical Engineering (IREE), 13 (5), pp. 421-427.

Yankovich, E., Yankovich, K., Baranovskiy, N., Forest Mapping Using Classification of Sentinel-2A Imagery for Forest Fire Danger Prediction: a Case Study, (2021) International Journal on Engineering Applications (IREA), 9 (3), pp. 148-161.

Hendel, M., Benyettou, A., Hendel, F., Fusion of Direct Probabilistic Multi-Class Support Vector Machines to Enhance Mental Tasks Recognition Performance in BCI Systems, (2018) International Journal on Communications Antenna and Propagation (IRECAP), 8 (5), pp. 430-438.

Marrugo Cardenas, N., Amaya Hurtado, D., Ramos Sandoval, O., Comparison of Multi-Class Methods of Features Extraction and Classification to Recognize EEGs Related with the Imagination of Two Vowels, (2018) International Journal on Communications Antenna and Propagation (IRECAP), 8 (5), pp. 398-405.

Shatnawi, M., Bani Yassein, M., Aljawarneh, S., Alodibat, S., Meqdadi, O., Hmeidi, I., Al Zoubi, O., An Improvement of Neural Network Algorithm for Anomaly Intrusion Detection System, (2020) International Journal on Communications Antenna and Propagation (IRECAP), 10 (2), pp. 84-93.

Bani Yassein, M., Alomari, O., Detecting the Online Shopping Factors Using the Arab Tweets on Media Technology, (2020) International Journal on Communications Antenna and Propagation (IRECAP), 10 (3), pp. 206-211.


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