Abstract
Self-healing is considered as an indispensable function to achieve intelligent network management in future wireless communication systems. However, in ultra-dense networks (UDNs), it’s a great challenge to realize efficient self-healing due to the massive and diverse network nodes, as well as complex transmission environment. The failed network access point (AP) may result in sudden traffic outage and severe user service degrading. In this paper, we propose an effective self-healing mechanism for UDNs with complete procedure of intelligent failure detection, diagnosis and recovery. Cognitive technology has been introduced to realize the effective detection of the AP working status. Then the processed information are analyzed based on multi-armed bandit model for possible AP failure judgement. After it is confirmed that an AP is failed, the impacted users, which are served originally by the failed AP, would be accessed to the proper neighbor APs. Furthermore, the corresponding resource allocation based on Non-Orthogonal Multiple Access (NOMA) is proposed. Simulation results show that the proposed mechanism could detect the AP failure effectively and realize quick self-healing for the network.
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References
Rakshit, S.M., Banerjee, S., Hempel, M., Sharif, H.: Towards an integrated approach for distributed 5G cell association in UDN under interference and mobility. In: 2018 International Conference on Computing, Networking and Communications (ICNC), March 2018, pp. 810–814 (2018)
Jiang, W., Strufe, M., Schotten, H.D.: Intelligent network management for 5G systems: the SELFNET approach. In: 2017 European Conference on Networks and Communications (EuCNC), June 2017, pp. 1–5 (2017)
Nagarajan, D.R., Thiagarajah, S.P., Alias, M.Y.: Robust son system with enhanced handover performance system. In: 2017 IEEE 13th Malaysia International Conference on Communications (MICC), Nov 2017, pp. 276–281 (2017)
Moysen, J., Giupponi, L.: A reinforcement learning based solution for self-healing in LTE networks. In: IEEE 80th Vehicular Technology Conference (VTC2014-Fall), Sept 2014, pp. 1–6 (2014)
Chernogorov, F., Repo, I., Räisänen, V., Nihtilä, T., Kurjenniemi, J.: Cognitive self-healing system for future mobile networks. In: 2015 International Wireless Communications and Mobile Computing Conference (IWCMC), Aug 2015, pp. 628–633 (2015)
Lin, F. Y.-S., Tsai, M., Wen, Y., Hsiao, C.: Adaptive power ranges and associations for self-healing in multiple types of Wi-Fi networks. In: 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), June 2017, pp. 1084–1089 (2017)
Liu, Y., Li, X., Ji, H., Wang, K., Zhang, H.: Joint APS selection and resource allocation for self-healing in ultra dense network, July 2016, pp. 1–5 (2016)
Alias, M., Saxena, N., Roy, A.: Efficient cell outage detection in 5G HetNets using hidden Markov model. IEEE Commun. Lett. 20(3), 562–565 (2016)
Zhang, T., Feng, L., Yu, P., Guo, S., Li, W., Qiu, X.: A handover statistics based approach for cell outage detection in self-organized heterogeneous networks. In: 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), May 2017, pp. 628–631 (2017)
Onireti, O., et al.: A cell outage management framework for dense heterogeneous networks. IEEE Trans. Veh. Technol. 65(4), 2097–2113 (2016)
Lei, L., Yuan, D., Ho, C.K., Sun, S.: Joint optimization of power and channel allocation with non-orthogonal multiple access for 5G cellular systems. In: 2015 IEEE Global Communications Conference (GLOBECOM), Dec 2015, pp. 1–6 (2015)
Zhou, Z.: Machine Learning. Tsinghua University Press (2016)
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This paper is sponsored by National Natural Science Foundation of China (Grant 61771070 and 61671088).
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Gao, Z., Li, X., Ji, H., Zhang, H. (2019). A Distributed Self-healing Mechanism Based on Cognitive Radio and AP Cooperation in UDN. In: Liu, X., Cheng, D., Jinfeng, L. (eds) Communications and Networking. ChinaCom 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 262. Springer, Cham. https://doi.org/10.1007/978-3-030-06161-6_36
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DOI: https://doi.org/10.1007/978-3-030-06161-6_36
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