Self-Healing in LTE networks with unsupervised learning techniques

Rahmani, Javad and Sadeqi, Abolfazl and Ayeh Mensah, Dennis Nii (2020): Self-Healing in LTE networks with unsupervised learning techniques. Published in: Computational Research Progress in Applied Science & Engineering , Vol. 06, No. 01 (24 March 2020): pp. 40-45.

Abstract

Recently the cellular networks are getting more complex in maintenance and network management, and rapidly growing in the number of users so that repairing and maintenance of the system are becoming more challenging and expensive. To solve the problems and maintain the system, operators depend on their experience but by increasing in type and density of the networks, this way will not operate as before. So Self-organizing network (SON) has been used in this study to solve these issues.

• Item Type: MPRA Paper
• Language: English
• Keywords: Self-healing, SVM, ADABOOST, Fuzzy, Adaptive Root Cause Analysis
• Subjects: L - Industrial Organization > L6 - Industry Studies: Manufacturing > L63 - Microelectronics ; Computers ; Communications Equipment; O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O31 - Innovation and Invention: Processes and Incentives; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q2 - Renewable Resources and Conservation > Q26 - Recreational Aspects of Natural Resources

References

[1] N. A. Golilarz, N. Robert, J. Addeh, A. Salehpour, Translation invariant wavelet based noise reduction using a new smooth nonlinear improved thresholding function, Computational Research Progress in Applied Science & Engineering 3 (2017) 104–108.

[2] N. A. Golilarz, H. Demirel, Thresholding neural network (TNN) based noise reduction with a new improved thresholding function, Computational Research Progress in Applied Science & Engineering 3 (2017) 81–84.

[3] Addeh, A. Khormali, N. A. Golilarz, Control chart pattern recognition using RBF neural network with new training algorithm and pracrical features, ISA Transactions 79 (2018) 202–216.

[4] N. A. Golilarz, A. Addeh, H. Gao, L. Ali, A. M. Roshandeh, H. M. Munir, R. Khan, A new automatic method for control chart patterns recognition based on ConvNet and harris hawks meta heuristic optimization algorithm, IEEE Access 7 (2019) 149398–149405.

[5] N. S. Gilani, M. T. Bina, F. Rahmani, and M. H. Imani, "Data-mining for Fault-Zone Detection of Distance Relay in FACTS-Based Transmission," in 2020 IEEE Texas Power and Energy Conference (TPEC), 2020, pp. 1-6.

[6] M. Khatibi, T. Amraee, H. Zargarzadeh, and M. Barzegaran, Comparative analysis of dynamic model reduction with application in power systems, in 2016 Clemson University Power Systems Conference (PSC) (2016) 1–6.

[7] M. Khatibi and S. Ahmed, Optimal resilient defense strategy against false data injection attacks on power system state estimation, in 2018 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) (2018) 1–5.

[8] M. Ketabdar, "Numerical and Empirical Studies on the Hydraulic Conditions of 90 degree converged Bend with Intake," International Journal of Science and Engineering Applications, vol. 5, pp. 441-444, 2016.

[9] F. Rahmani, "Electric Vehicle Charger based on DC/DC Converter Topology," International Journal of Engineering Science, vol. 18879, 2018.

[10] X. Liu, G. Chuai, W. Gao, Y. Ren, and K. Zhang, Diagnosis Based on Machine Learning for LTE Self-Healing, in International Conference in Communications, Signal Processing, and Systems (2017) 2096–2105.

[11] M. Khatibi, H. Zargarzadeh, and M. Barzegaran, Power system dynamic model reduction by means of an iterative SVD-Krylov model reduction method, in 2016 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) (2016) 1–6.

[12] M. Khatibi, S. Ahmed, Impact of Distributed Energy Resources on Frequency Regulation of the Bulk Power System, arXiv preprint arXiv:1906.09295 (2019).

[13] H. Mfula and J. K. Nurminen, Adaptive root cause analysis for self-healing in 5G networks, in 2017 International Conference on High Performance Computing & Simulation (HPCS) (2017) 136–143.

[14] L. Ali, S. Khan, N. A. Golilarz, Y. Imrana, I. Qasim, A. Noor, R. Nour, A Feature-Driven Decision Support System for Heart Failure Prediction Based on χ 2 Statistical Model and Gaussian Naive Bayes, Computational and Mathematical Methods in Medicine (2019) 1–8.

[15] F. Rahmani, F. Razaghian, and A. Kashaninia, "Novel Approach to Design of a Class-EJ Power Amplifier Using High Power Technology," World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, vol. 9, pp. 541-546, 2015.

[16] Gómez-Andrades, P. Muñoz, E. J. Khatib, I. de-la-Bandera, I. Serrano, and R. Barco, Methodology for the design and evaluation of self-healing LTE networks, IEEE Transactions on Vehicular Technology 65 (2015) 6468–6486.

[17] F. Rahmani, F. Razaghian, and A. Kashaninia, "High Power Two-Stage Class-AB/J Power Amplifier with High Gain and Efficiency," 2014.

[18] E. J. Khatib, R. Barco, A. Gómez-Andrades, and I. Serrano, Diagnosis based on genetic fuzzy algorithms for LTE self-healing, IEEE Transactions on vehicular technology 65 (2015) 1639–1651.

[19] R. Nofitasari and S. Haryadi, Self-healing mechanism with RSRP measurement in LTE network, in 2017 3rd International Conference on Wireless and Telematics (ICWT) (2017) 144–148.

[20] E. J. Khatib, R. Barco, P. Muñoz, I. De La Bandera, I. Serrano, Self-healing in mobile networks with big data, IEEE Communications Magazine 54 (2016) 114–120.

[21] I Bargegol, M. Nikookar, R. V. Nezafat, E. J. Lashkami, A. M. Roshandeh, Timing optimization of singalized intersections using shockwave theory by genetic algorithm, Computational Research Progress in Applied Science and Engineering 1 (2015) 160–167.