A Novel Approach for Cluster Self-Optimization Using Big Data Analytics
محورهای موضوعی : Communication Systems & Devices
Abbas Mirzaei
1
,
Amir Rahimi
2
1 - Islamic Azad University, Ardabil Branch
2 - Islamic Azad University, Ardabil Branch
کلید واژه: Self-Optimization Networking, , Big Data, Quality of service (QoS), , Resource Allocation, , Load Balancing, ,
چکیده مقاله :
One of the current challenges in providing high bitrate services in next generation mobile networks is limitation of available resources. The goal of proposing a self-optimization model is to maximize the network efficiency and increase the quality of services provided to femto-cell users, considering the limited resources in radio access networks. The basis for our proposed scheme is to introduce a self-optimization model based on neighbouring relations. Using this model, we can create the possibility of controlling resources and neighbouring parameters without the need of human manipulation and only based on the network’s intelligence. To increase the model efficiency, we applied the big data technique for analyzing data and increasing the accuracy of the decision-making process in a way that on the uplink, the sent data by users is to be analyzed in self-optimization engine. The experimental results show that despite the tremendous volume of the analyzed data – which is hundreds of times bigger than usual methods – it is possible to improve the KPIs, such as throughput, up to 30 percent by optimal resource allocation and reducing the signaling load. Also, the presence of feature extraction and parameter selection modules will reduce the response time of the self-optimization model up to 25 percent when the number of parameters is too high Moreover, numerical results indicate the superiority of using support vector machine (SVM) learning algorithm. It improves the accuracy level of decision making based on the rule-based expert system. Finally, uplink quality improvement and 15-percent increment of the coverage area under satisfied SINR conditions can be considered as outcome of the proposed scheme.
One of the current challenges in providing high bitrate services in next generation mobile networks is limitation of available resources. The goal of proposing a self-optimization model is to maximize the network efficiency and increase the quality of services provided to femto-cell users, considering the limited resources in radio access networks. The basis for our proposed scheme is to introduce a self-optimization model based on neighbouring relations. Using this model, we can create the possibility of controlling resources and neighbouring parameters without the need of human manipulation and only based on the network’s intelligence. To increase the model efficiency, we applied the big data technique for analyzing data and increasing the accuracy of the decision-making process in a way that on the uplink, the sent data by users is to be analyzed in self-optimization engine. The experimental results show that despite the tremendous volume of the analyzed data – which is hundreds of times bigger than usual methods – it is possible to improve the KPIs, such as throughput, up to 30 percent by optimal resource allocation and reducing the signaling load. Also, the presence of feature extraction and parameter selection modules will reduce the response time of the self-optimization model up to 25 percent when the number of parameters is too high Moreover, numerical results indicate the superiority of using support vector machine (SVM) learning algorithm. It improves the accuracy level of decision making based on the rule-based expert system. Finally, uplink quality improvement and 15-percent increment of the coverage area under satisfied SINR conditions can be considered as outcome of the proposed scheme.
[1] A. Imran, A. Zoha and A. Abu-Dayya, “Challenges in 5G: how to empower SON with big data for enabling 5G,” IEEE Network, Vol. 28, No. 2, 2014, pp. 27–33.
[2] N. Baldo, L. Giupponi and J. Mangues-Bafalluy, "Big Data Empowered Self Organized Networks," European Wireless 2014; 20th European Wireless Conference, 2014, pp. 1-8.
[3] R. Murugeswari, S. Radhakrishnan, and D. Devaraj, “A multi-objective evolutionary algorithm based QoS routing in wireless mesh networks,” Applied Soft Computing, Vol. 40, No C, 2016, pp. 517–525.
[4] Jun, Ersin Uzun, and Jose J. Garcia-luna-aceves. "NETWORK CODING FOR CONTENT-CENTRIC NETWORK." U.S. Patent 20,160,065,685, issued March 3, 2016.
[5] H. Zhang, C. Jiang, R. Q. Hu and Y. Qian, "Self-organization in disaster-resilient heterogeneous small cell networks," IEEE Network, vol. 30, No. 2, 2016, pp. 116-121.
[6] S. Berger, M. Simsek, A. Fehske, P. Zanier, I. Viering, and G. Fettweis, “Joint Downlink and Uplink Tilt-Based Self-Organization of Coverage and Capacity Under Sparse System Knowledge,” IEEE Transactions on Vehicular Technology, Vol. 65, No. 4, 2016, pp. 2259–2273.
[7] M. Huang and XU. Zhang, “Enhanced automatic neighbor relation function for 5G cellular systems with massive MIMO,” 2017 IEEE International Conference on Communications (ICC), 2017, PP 1-6. 2017.
[8] K.-L. Yap and Y.-W. Chong, “Software-Defined Networking Techniques to Improve Mobile Network Connectivity: Technical Review,” IETE Technical Review, 2017, pp.1-13.
[9] Gebremichail and C. Beard, “Fade Duration Based Neighbor Cell List Optimization for Handover in Femtocell Networks,” International Journal of Interdisciplinary Telecommunications and Networking, Vol. 9, No. 2, 2017, pp. 1–15.
[10] T. Han and N. Ansari, “Network Utility Aware Traffic Load Balancing in Backhaul-Constrained Cache-Enabled Small Cell Networks with Hybrid Power Supplies,” IEEE Transactions on Mobile Computing, Vol. 16, No. 10, 2017, pp. 2819–2832.
[11] Guita, José, Luis M. Correia, and Marco Serrazina. "Balancing the Load in LTE Urban Networks via Inter-Frequency Handovers." M.S. thesis, Instituto Superior Técnico ,University of Lisbon, Lisbon, Portugal, 2016.
[12] K. Zheng, Z. Yang, K. Zhang, P. Chatzimisios, K. Yang, and W. Xiang, “Big data-driven optimization for mobile networks toward 5G,” IEEE Network, Vol. 30, No. 1, 2016, pp. 44–51.
[13] S, L, B. Iwan, R. Nicolas, Q. Ripault, J.R. Andrade, S. Han, H. Kim, "Self-optimization of plasmonic nanoantennas in strong femtosecond fields." Optica, Vol.4, No.9, 2017, pp. 1038-1043.
[14] Z. Dawy, W. Saad, A. Ghosh, J. G. Andrews, and E. Yaacoub, “Toward Massive Machine Type Cellular Communications,” IEEE Wireless Communications, Vol. 24, No. 1, 2017, pp. 120–128,.
[15] Gillot, David, and John Yue Jun Jiang. "Method and system for providing roaming intelligence (RI) to a host network operator for its roaming traffic." U.S. Patent 9,338,663, 2016.
[16] S. Fong, C. Fang, N. Tian, R. Wong, B. W. Yap, "Self-Adaptive Parameters Optimization for Incremental Classification in Big Data Using Neural Network." In Big Data Applications and Use Cases Springer International Publishing, 2016.
[17] Agrawal, Himanshu, and Krishna Asawa. "New Architecture for Dynamic Spectrum Allocation in Cognitive Heterogeneous Network using Self Organizing Map." arXiv preprint arXiv, 2016.
[18] G. Foster, S. Vahid, and R. Tafazolli, “SON Evolution for 5G Mobile Networks,” Fundamentals of 5G Mobile Networks, USA, Wiley, 2015.
[19] H. Goudarzi and M. Pedram, “Hierarchical SLA-Driven Resource Management for Peak Power-Aware and Energy-Efficient Operation of a Cloud Datacenter,” IEEE Transactions on Cloud Computing, Vol. 4, No. 2, 2016, pp. 222–236.
[20] C. Segura, C. Coello, G. Valladares, C. Leon, " Using multi-objective evolutionary algorithms for single-objective constrained and unconstrained optimization." Annals of Operations Research, Vol.240, No.1, 2016, pp. 217-250.
[21] Z. M. Fadlullah, D. M. Quan, N. Kato, and I. Stojmenovic, “GTES: An Optimized Game-Theoretic Demand-Side Management Scheme for Smart Grid,” IEEE Systems Journal, Vol. 8, No. 2, 2014, pp. 588–597,.
[22] Y. Liu, C. Yuen, S. Huang, N. Ul Hassan, X. Wang and S. Xie, "Peak-to-Average Ratio Constrained Demand-Side Management With Consumer's Preference in Residential Smart Grid," IEEE Journal of Selected Topics in Signal Processing, Vol. 8, No. 6, 2014, pp. 1084-1097.
[23] A. De Waegenaere, J. L. Wielhouwer," A breakpoint search approach for convex resource allocation problems with bounded variables."Optimization Letters ,Vol.6, No.4, 2012, pp. 629-640.
[24] A. Galindo-Serrano, “Self-organized Femto-cells: A Time Difference Learning Approach,” Ph.D. thesis, Universitat Politecnica de Catalunya (UPC), Barcelona, Spain, 2013.
[25] Y. Jiang, Q. Liu, F. Zheng, X. Gao and X. You, Energy-efficient joint resource allocation and power control for communications, IEEE Transactions on Vehicular Technology 65(8) (2016), 6119–6127.
