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Moammer Yami, Reza, Khazaei, Ali Akbar, Rahati Quchani, Saeed. (1402). ML and MCDM for Abnormal Cell Detection in 5G & B5G Networks. نشریه هنرهای کاربردی, 3(3), 23-34. doi: 10.22075/mseee.2025.34976.1172
Reza Moammer Yami; Ali Akbar Khazaei; Saeed Rahati Quchani. "ML and MCDM for Abnormal Cell Detection in 5G & B5G Networks". نشریه هنرهای کاربردی, 3, 3, 1402, 23-34. doi: 10.22075/mseee.2025.34976.1172
Moammer Yami, Reza, Khazaei, Ali Akbar, Rahati Quchani, Saeed. (1402). 'ML and MCDM for Abnormal Cell Detection in 5G & B5G Networks', نشریه هنرهای کاربردی, 3(3), pp. 23-34. doi: 10.22075/mseee.2025.34976.1172
Moammer Yami, Reza, Khazaei, Ali Akbar, Rahati Quchani, Saeed. ML and MCDM for Abnormal Cell Detection in 5G & B5G Networks. نشریه هنرهای کاربردی, 1402; 3(3): 23-34. doi: 10.22075/mseee.2025.34976.1172

ML and MCDM for Abnormal Cell Detection in 5G & B5G Networks

Modeling and Simulation in Electrical and Electronics Engineering
دوره 3، شماره 3 - شماره پیاپی 13، بهمن 2023، صفحه 23-34    اصل مقاله (598.04 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22075/mseee.2025.34976.1172
نویسندگان
Reza Moammer Yami* 1؛ Ali Akbar Khazaei1؛ Saeed Rahati Quchani2
1Department of Electrical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
2Department of Biomedical Engineering, Faculty of Engineering, Islamic Azad University, Central Tehran Branch, Tehran, Iran.
تاریخ دریافت: 17 مرداد 1403،  تاریخ پذیرش: 10 بهمن 1403 
چکیده
Self-organizing communication networks are a vital pillar in 5G and B5G technology, which operate automatically without human intervention in self-healing, self-configuring, and self-optimizing. Self-healing in these networks predicts and resolves network problems and improves performance with the following three methods in the research conducted: rule-based, algorithmic, and machine-learning approaches. This research used the TOPSIS technique as a multi-criteria decision-making method to rank and score cells after data preprocessing. Then, based on the rank of each cell, it is divided into two classes: normal and abnormal. Then, with three algorithms, decision tree, New Bayes and Random Fars, normal and abnormal cell prediction was performed independently. In the last step, using the combined method of maximum voting, the algorithm was completed and the results showed an improvement in the parameters Precisio=0.939, Recall=0.962, F-Measure=0.968, Accuracy=94.0717.
کلیدواژه‌ها
Self-Organizing Networks؛ Simple Bayes؛ Decision Tree Random Forest؛ 5G/B5G Networks؛ Topsis
مراجع
  • Moysen and L. Giupponi, “From 4G to 5G: Self organized network management meets machine learning,” Computer Communications, vol. 129, pp. 248–268, Sep. 2018, doi: 10.1016/j.comcom.2018.07.015.
  • Rodriguez, Fundamentals of 5G Mobile Networks. John Wiley & Sons, 2015.
  • Mulvey, C. H. Foh, M. A. Imran, and R. Tafazolli, “Cell Fault Management Using Machine Learning Techniques,” IEEE Access, vol. 7, pp. 124514–124539, 2019, doi: 10.1109/access.2019.2938410.
  • -L. Luo, Machine Learning for Future Wireless Communications. John Wiley & Sons, 2020.
  • Amirijoo et al., “Cell outage management in LTE networks,” in 2009 6th International Symposium on Wireless Communication Systems, Sep. 2009. Accessed: Aug. 02, 2024. [Online]. Available: http://dx.doi.org/10.1109/iswcs.2009.5285232
  • Liao, M. Wiczanowski, and S. Stanczak, “Toward cell outage detection with composite hypothesis testing,” in 2012 IEEE International Conference on Communications (ICC), Jun. 2012. Accessed: Aug. 02, 2024. [Online]. Available: http://dx.doi.org/10.1109/icc.2012.6364384
  • Karatepe and E. Zeydan, "Anomaly Detection In Cellular Network Data Using Big Data Analytics," European Wireless 2014; 20th European Wireless Conference, Barcelona, Spain, 2014, pp. 1-5.
  • Novaczki and P. Szilagyi, “Radio Channel Degradation Detection and Diagnosis Based on Statistical Analysis,” in 2011 IEEE 73rd Vehicular Technology Conference (VTC Spring), May 2011. Accessed: Aug. 02, 2024. [Online]. Available: http://dx.doi.org/10.1109/vetecs.2011.5956417
  • Kumpulainen, M. Särkioja, M. Kylväjä, and K. Hätönen, “Analysing 3G radio network performance with fuzzy methods,” Neurocomputing, vol. 107, pp. 49–58, May 2013, doi: 10.1016/j.neucom.2012.07.033.
  • Yan, L. Breslau, Z. Ge, D. Massey, D. Pei, and J. Yates, “G-RCA,” in Proceedings of the 6th International Conference, Nov. 2010. Accessed: Aug. 02, 2024. [Online]. Available: http://dx.doi.org/10.1145/1921168.1921175
  • N. Cronk, P. H. Callahan, and L. Bernstein, “Rule-based expert systems for network management and operations: an introduction,” IEEE Network, vol. 2, no. 5, pp. 7–21, Sep. 1988, doi: 10.1109/65.17975.
  • Jakobson and M. Weissman, “Alarm correlation,” IEEE Network, vol. 7, no. 6, pp. 52–59, Nov. 1993, doi: 10.1109/65.244794.
  • Frohlich, W. Nejdl, K. Jobmann, and H. Wietgrefe, “Model-based alarm correlation in cellular phone networks,” in Proceedings Fifth International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems. Accessed: Aug. 02, 2024. [Online]. Available: http://dx.doi.org/10.1109/mascot.1997.567613
  • F. Ciocarlie et al., “Anomaly Detection and Diagnosis for Automatic Radio Network Verification,” in Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Cham: Springer International Publishing, 2015, pp. 163–176. Accessed: Aug. 03, 2024. [Online]. Available: http://dx.doi.org/10.1007/978-3-319-16292-8_12
  • B. Porch, C. Heng Foh, H. Farooq, and A. Imran, “Machine Learning Approach for Automatic Fault Detection and Diagnosis in Cellular Networks,” in 2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), May 2020. Accessed: Aug. 03, 2024. [Online]. Available: http://dx.doi.org/10.1109/blackseacom48709.2020.9234962
  • Alho, A. Burian, J. Helenius, and J. Pajarinen, “Machine Learning Based Mobile Network Throughput Classification,” in 2021 IEEE Wireless Communications and Networking Conference (WCNC), Mar. 2021. Accessed: Aug. 03, 2024. [Online]. Available: http://dx.doi.org/10.1109/wcnc49053.2021.9417365
  • -C. Lin, “Large-Scale and High-Dimensional Cell Outage Detection in 5G Self-Organizing Networks,” in 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Nov. 2019. Accessed: Aug. 03, 2024. [Online]. Available: http://dx.doi.org/10.1109/apsipaasc47483.2019.9023348
  • Li, P. Yu, M. Yin, and L. Meng, “A distributed cell outage compensation mechanism based on RS power adjustment in LTE networks,” China Communications, vol. 11, no. 13, pp. 40–47, 2014, doi: 10.1109/cc.2014.7022524.
  • Wang, J. Zhang, and Q. Zhang, “Cooperative cell outage detection in Self-Organizing femtocell networks,” in 2013 Proceedings IEEE INFOCOM, Apr. 2013. Accessed: Aug. 03, 2024. [Online]. Available: http://dx.doi.org/10.1109/infcom.2013.6566865
  • Kayili and E. Sousa, “Cell outage compensation for irregular cellular networks,” in 2014 IEEE Wireless Communications and Networking Conference (WCNC), Apr. 2014. Accessed: Aug. 03, 2024. [Online]. Available: http://dx.doi.org/10.1109/wcnc.2014.6952531
  • de la Bandera, P. Munoz, I. Serrano, and R. Barco, “Adaptive Cell Outage Compensation in Self-Organizing Networks,” IEEE Transactions on Vehicular Technology, vol. 67, no. 6, pp. 5231–5244, Jun. 2018, doi: 10.1109/tvt.2018.2792406.
  • Li, P. Yu, M. Yin, and L. Meng, “A distributed cell outage compensation mechanism based on RS power adjustment in LTE networks,” China Communications, vol. 11, no. 13, pp. 40–47, 2014, doi: 10.1109/cc.2014.7022524.
  • -K. Yeo and Y. Lu, “Fast array failure correction using improved particle swarm optimization,” in 2009 Asia Pacific Microwave Conference, Dec. 2009. Accessed: Aug. 03, 2024. [Online]. Available: http://dx.doi.org/10.1109/apmc.2009.5384420
  • P. Babalola and V. Balyan, “Cell Outage Detection in 5G Self-organizing Networks Based on FDA-HMM,” in Proceedings of Fourth International Conference on Computing, Communications, and Cyber-Security, Singapore: Springer Nature Singapore, 2023, pp. 119–130. Accessed: Aug. 03, 2024. [Online]. Available: http://dx.doi.org/10.1007/978-981-99-1479-1_9
  • Kecman, “Support Vector Machines – An Introduction,” in Support Vector Machines: Theory and Applications, Berlin, Heidelberg: Springer Berlin Heidelberg, 2005, pp. 1–47. Accessed: Aug. 03, 2024. [Online]. Available: http://dx.doi.org/10.1007/10984697_1
  • R. Eddy, “Hidden Markov models,” Current Opinion in Structural Biology, vol. 6, no. 3, pp. 361–365, Jun. 1996, doi: 10.1016/s0959-440x (96)80056-x.
  • Verma, H. Saraf, and S. H. Gupta, “Prediction of user throughput from Network Parameters of LTE Network using machine learning,” Mobile Networks and Applications, vol. 28, no. 1, pp. 244–253, Mar. 2022, doi: 10.1007/s11036-022-01934-6.
  • Zhang, “Introduction to machine learning: k-nearest neighbors,” Annals of Translational Medicine, vol. 4, no. 11, pp. 218–218, Jun. 2016, doi: 10.21037/atm.2016.03.37.
  • M. Al-Aidaroos, A. A. Bakar, and Z. Othman, “Naï ve bayes variants in classification learning,” in 2010 International Conference on Information Retrieval & Knowledge Management (CAMP), Mar. 2010. Accessed: Aug. 03, 2024. [Online]. Available: http://dx.doi.org/10.1109/infrkm.2010.5466902
  • Wang, K. Zhu, M. Sun, and Y. Deng, “An Ensemble Learning Approach for Fault Diagnosis in Self-Organizing Heterogeneous Networks,” IEEE Access, vol. 7, pp. 125662–125675, 2019, doi: 10.1109/access.2019.2925566.
  • Nguyen, V. “Anomaly detection in self-organizing network.” (2019).
  • Liu, Y. Wang, and J. Zhang, “New Machine Learning Algorithm: Random Forest,” in Information Computing and Applications, Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 246–252. Accessed: Aug. 03, 2024. [Online]. Available: http://dx.doi.org/10.1007/978-3-642-34062-8_32
  • -C. Tseng, Y.-C. Wang, K.-Y. Cheng, and Y.-Y. Hsieh, “iMouse: An Integrated Mobile Surveillance and Wireless Sensor System,” Computer, vol. 40, no. 6, pp. 60–66, Jun. 2007, doi: 10.1109/mc.2007.211.
  • Xia, Y. Owada, M. Inoue, and H. Harai, “Optical and Wireless Hybrid Access Networks: Design and Optimization,” Journal of Optical Communications and Networking, vol. 4, no. 10, p. 749, Sep. 2012, doi: 10.1364/jocn.4.000749.
  • S. Faiçal et al., “The use of unmanned aerial vehicles and wireless sensor networks for spraying pesticides,” Journal of Systems Architecture, vol. 60, no. 4, pp. 393–404, Apr. 2014, doi: 10.1016/j.sysarc.2014.01.004.
  • Shamshirband, A. Amini, N. B. Anuar, M. L. Mat Kiah, Y. W. Teh, and S. Furnell, “D-FICCA: A density-based fuzzy imperialist competitive clustering algorithm for intrusion detection in wireless sensor networks,” Measurement, vol. 55, pp. 212–226, Sep. 2014, doi: 10.1016/j.measurement.2014.04.034.
  • Abid, A. Kachouri, and A. Mahfoudhi, “Outlier detection for wireless sensor networks using density‐based clustering approach,” IET Wireless Sensor Systems, vol. 7, no. 4, pp. 83–90, Aug. 2017, doi: 10.1049/iet-wss.2016.0044.
  • K. Wali, M. N. A. Prasad, N. C. Shreyas, N. C. Chaithanya, and P. K. Kuttaiah, “Gaussian mixture model-expectation maximization-based signal strength prediction for seamless connectivity in hybrid wireless networks,” in Proceedings of the 7th International Conference on Advances in Mobile Computing and Multimedia, Dec. 2009. Accessed: Aug. 03, 2024. [Online]. Available: http://dx.doi.org/10.1145/1821748.1821842
  • Feng, Y. Zhang, C. Li, Z. Dou, and J. Wang, “Anomaly detection of spectrum in wireless communication via deep auto-encoders,” The Journal of Supercomputing, vol. 73, no. 7, pp. 3161–3178, Mar. 2017, doi: 10.1007/s11227-017-2017-7.
  • Z. Erdogan and T. T. Bilgin, “A data mining approach for fall detection by using k-nearest neighbour algorithm on wireless sensor network data,” IET Communications, vol. 6, no. 18, pp. 3281–3287, Dec. 2012, doi: 10.1049/iet-com.2011.0228.
  • Wang, K. Wu, and L. M. Ni, “WiFall: Device-Free Fall Detection by Wireless Networks,” IEEE Transactions on Mobile Computing, vol. 16, no. 2, pp. 581–594, Feb. 2017, doi: 10.1109/tmc.2016.2557792.
  • Yu, H. Chen, W. Zhao, and L. Xie, “No-Reference QoE Prediction Model for Video Streaming Service in 3G Networks,” in 2012 8th International Conference on Wireless Communications, Networking and Mobile Computing, Sep. 2012. Accessed: Aug. 03, 2024. [Online]. Available: http://dx.doi.org/10.1109/wicom.2012.6478588
  • Salem, A. Guerassimov, A. Mehaoua, A. Marcus, and B. Furht, “Sensor fault and patient anomaly detection and classification in medical wireless sensor networks,” in 2013 IEEE International Conference on Communications (ICC), Jun. 2013. Accessed: Aug. 03, 2024. [Online]. Available: http://dx.doi.org/10.1109/icc.2013.6655254
  • A. Alsheikh, S. Lin, D. Niyato, and H.-P. Tan, “Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications,” IEEE Communications Surveys & Tutorials, vol. 16, no. 4, pp. 1996–2018, 2014, doi: 10.1109/comst.2014.2320099.
  • L. Thing, “IEEE 802.11 Network Anomaly Detection and Attack Classification: A Deep Learning Approach,” in 2017 IEEE Wireless Communications and Networking Conference (WCNC), Mar. 2017. Accessed: Aug. 03, 2024. [Online]. Available: http://dx.doi.org/10.1109/wcnc.2017.7925567
  • Yuan, Y. Lu, Z. Wang, and Y. Xue, “Droid-Sec,” in Proceedings of the 2014 ACM conference on SIGCOMM, Aug. 2014. Accessed: Aug. 03, 2024. [Online]. Available: http://dx.doi.org/10.1145/2619239.2631434
  • Kaplantzis, A. Shilton, N. Mani, and Y. A. Sekercioglu, “Detecting Selective Forwarding Attacks in Wireless Sensor Networks using Support Vector Machines,” in 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information, 2007. Accessed: Aug. 03, 2024. [Online]. Available: http://dx.doi.org/10.1109/issnip.2007.4496866
  • Blasco, D. Gunduz, and M. Dohler, “A learning theoretic approach to energy harvesting communication system optimization,” in 2012 IEEE Globecom Workshops, Dec. 2012. Accessed: Aug. 03, 2024. [Online]. Available: http://dx.doi.org/10.1109/glocomw.2012.6477834
  • Stevens-Navarro, Yuxia Lin, and V. W. S. Wong, “An MDP-Based Vertical Handoff Decision Algorithm for Heterogeneous Wireless Networks,” IEEE Transactions on Vehicular Technology, vol. 57, no. 2, pp. 1243–1254, Mar. 2008, doi: 10.1109/tvt.2007.907072.
  • Mastronarde and M. van der Schaar, “Fast Reinforcement Learning for Energy-Efficient Wireless Communication,” IEEE Transactions on Signal Processing, vol. 59, no. 12, pp. 6262–6266, Dec. 2011, doi: 10.1109/tsp.2011.2165211.
  • Pandana and K. J. R. Liu, “Near-optimal reinforcement learning framework for energy-aware sensor communications,” IEEE Journal on Selected Areas in Communications, vol. 23, no. 4, pp. 788–797, Apr. 2005, doi: 10.1109/jsac.2005.843547.
  • Lilith and K. Dogancay, “Distributed Dynamic Call Admission Control and Channel Allocation Using SARSA,” in 2005 Asia-Pacific Conference on Communications, 2005. Accessed: Aug. 03, 2024. [Online]. Available: http://dx.doi.org/10.1109/apcc.2005.1554084
  • Saleem, K.-L. A. Yau, H. Mohamad, N. Ramli, M. H. Rehmani, and Q. Ni, “Clustering and Reinforcement-Learning-Based Routing for Cognitive Radio Networks,” IEEE Wireless Communications, vol. 24, no. 4, pp. 146–151, Aug. 2017, doi: 10.1109/mwc.2017.1600117.
  • Xiao, Y. Li, C. Dai, H. Dai, and H. V. Poor, “Reinforcement Learning-Based NOMA Power Allocation in the Presence of Smart Jamming,” IEEE Transactions on Vehicular Technology, vol. 67, no. 4, pp. 3377–3389, Apr. 2018, doi: 10.1109/tvt.2017.2782726.
  • Sadeghi, F. Sheikholeslami, and G. B. Giannakis, “Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities,” IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 1, pp. 180–190, Feb. 2018, doi: 10.1109/jstsp.2017.2787979.
  • He, F. R. Yu, N. Zhao, V. C. M. Leung, and H. Yin, “Software-Defined Networks with Mobile Edge Computing and Caching for Smart Cities: A Big Data Deep Reinforcement Learning Approach,” IEEE Communications Magazine, vol. 55, no. 12, pp. 31–37, Dec. 2017, doi: 10.1109/mcom.2017.1700246.
  • Liu, B. Krishnamachari, S. Zhou, and Z. Niu, “DeepNap: Data-Driven Base Station Sleeping Operations Through Deep Reinforcement Learning,” IEEE Internet of Things Journal, vol. 5, no. 6, pp. 4273–4282, Dec. 2018, doi: 10.1109/jiot.2018.2846694.
  • Moysen and L. Giupponi, “A Reinforcement Learning Based Solution for Self-Healing in LTE Networks,” in 2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall), Sep. 2014. Accessed: Aug. 03, 2024. [Online]. Available: http://dx.doi.org/10.1109/vtcfall.2014.6965842
  • Vaezpour, L. Majzoobi, M. Akbari, S. Parsaeefard, and H. Yanikomeroglu, “A Deep Learning-Based Approach for Cell Outage Compensation in NOMA Networks,” IEEE Open Journal of Vehicular Technology, vol. 3, pp. 149–166, 2022, doi: 10.1109/ojvt.2022.3164685.
  • Zhang, K. Zhu, and D. Niyato, “A Generative Adversarial Learning-Based Approach for Cell Outage Detection in Self-Organizing Cellular Networks,” IEEE Wireless Communications Letters, vol. 9, no. 2, pp. 171–174, Feb. 2020, doi: 10.1109/lwc.2019.2947041.
  • Papathanasiou and N. Ploskas, “TOPSIS,” in Multiple Criteria Decision Aid, Cham: Springer International Publishing, 2018, pp. 1–30. Accessed: Aug. 03, 2024. [Online]. Available: http://dx.doi.org/10.1007/978-3-319-91648-4_1
  • Myles, R. N. Feudale, Y. Liu, N. A. Woody, and S. D. Brown, “An introduction to decision tree modeling,” Journal of Chemometrics, vol. 18, no. 6, pp. 275–285, Jun. 2004, doi: 10.1002/cem.873.
  • Klaine, P. V., Imran, M. A., Onireti, O., & Souza, R. D. (2017). A survey of machine learning techniques applied to self-organizing cellular networks. IEEE Communications Surveys & Tutorials, 19(4), 2392-2431.
 

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