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References

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1. Gacanin, H. and Ligata, A., Wi-fi self-organizing networks: Challenges and use cases. IEEE Commun. Mag., IEEE, 55, 7, 158–164, 2017.

2. Lohmüller, S., Cognitive Self-Organizing Network Management for Automated Configuration of Self-Optimization SON, in: PhD. Dissertation, University of Augsburg, 2019.

3. Thomas, R., DaSilva, L., MacKenzie, A., Cognitive Networks (book chapter), in: Cognitive Radio, Software Defined Radio, and Adaptive Wireless Systems, Springer, Germany, 2007.

4. Thang, V.V. and Pashchenko, F.F., Multistage System-Based Machine Learning Techniques for Intrusion Detection in WiFi Network. J. Comput. Networks and Commun., vol. 2019, Article ID 4708201, 13 pages, 2019. https://doi.org/10.1155/2019/4708201.

5. Mitchell, T., Machine Learning, McGraw Hill series in computer science, Mc- Graw-Hill, USA, 1997.

6. Liu, Y., Bi, S., Shi, Z., Hanzo, L., When Machine Learning Meets Big: A Wireless Communication Perspective. IEEE Veh. Technol. Mag., IEEE, 15, 63–72, 2020.

7. Kaufman, L. and Rousseeuw, P., Finding Groups in Data: An Introduction to Cluster Analysis, JohnWiley & Sons, US, 2018.

8. Andrews, J., Buzzi, S., Choi, W., Hanly, S., Lozano, A., Soong, A., Zhang, J., What will 5G be? IEEE J. Sel. Areas Commun., 32, 1065–1082, 2014. IEEE.

9. Agarwal, S., Kodialam, M., Lakshman, T., Traffic engineering in software defined networks. Proc. IEEE INFOCOM, pp. 2211–2219, 2013.

10. Mestres, A., Rodriguez-Natal, A., Carner, J., Barlet-Ros, P., Alarcón, E., Solé, M., Muntés-Mulero, V., Meyer, D., Barkai, S., Hibbett, M., Knowledge defined networking. ACM SIGCOMM Computer Communication Review, vol. 47, pp. 2–10, 2017.

11. Lim, S., Software Defined Network Detection System. Int. J. Recent Technol. Eng. (IJRTE), 8, 1391–1395, 2019.

12. Lin, P., Bi, J., Wolff, S., A west-east bridge based SDN inter-domain testbed. IEEE Commun. Mag., 53, 2, 190–197, 2015.

13. Xie, J., Yu, F., Huang, T., Xie, R., Liu, J., Wangz, C., Liu, Y., A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges. IEEE Commun. Surv. Tutorials, 21, 393–430, 2019.

14. Elkhodr, M., Hassan, Q., Shahrestani, S., Networks of the Future: Architectures, Technologies, and Implementations, CRC Press Taylor & Francis Group, USA, 2018.

15. Kosmidesa, P., Adamopouloua, E., Demestichasa, K., Anagnostoua, M., Rouskasb, A., On Intelligent Base Station Activation for Next Generation Wireless Networks. The 6th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, Elsevier, 2015.

16. Li, R., Zhao, Z., Chen, X., Zhang, H., Energy saving through a learning framework in greener cellular radio access networks. Proceedings of GLOBECOM, 1556–1561, 2012.

17. Yu, Y., Wang, T., Liew, S., Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks. IEEE International Conference on Communications (ICC), IEEE, 2018.

18. Onireti, O., A Cell Outage Management Framework for Dense Heterogeneous Networks. IEEE Trans. Veh. Technol., 65, 2097–2113, 2016.

19. Mohammadi, M. and Al-Fuqaha, A., Enabling Cognitive Smart Cities Using Big Data and Machine Learning: Approaches and Challenges. IEEE Commun. Mag., 56, 94–101, 2018.

20. He, Y., Software-Defined Networks with Mobile Edge Computing and Caching for Smart Cities: A Big Data Deep Reinforcement Learning Approach. IEEE Commun. Mag., 55, 31–37, 2017.

21. Jia, G., Yang, Z., Lam, H., Shi, J., Shikh-Bahaei, M., Channel assignment in uplink wireless communication using machine learning approach. arXiv preprint arXiv, 2001, 03952, 2020.

22. Zappone, A., Sanguinetti, L., Debbah, M., User association and load balancing for massive MIMO through deep learning. Proceedings of IEEE Asilomar Conference on Signals, Systems, and Computers, pp. 1262–1266, 2018.

23. Lin, P., Large-Scale and High-Dimensional Cell Outage Detection in 5G Self-Organizing Networks. Proceedings of APSIPA Annual Summit and Conference, pp. 8–12, 2019.

24. Pervez, F., Jaber, M., Qadir, J., Younis, S., Imran, M., Fuzzy Q-learning-based user-centric backhaul-aware user cell association scheme. 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 1840–1845, 2017.

25. Kumar, Y., Farooq, H., Imran, A., Fault Prediction and Reliability Analysis in a Real Cellular Network. 13th International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 1090–1095, 2017.

26. Boutaba, R., Salahuddin, M., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., Caicedo, O., A comprehensive survey on machine learning for networking: evolution, applications and research opportunities. J. Internet Serv. Appl., Springer, 9, 16, 2018. https://doi.org/10.1186/s13174-018-0087-2.

27 Song, R. and Willink, T., Machine Learning-Based Traffic Classification of Wireless Traffic. International Conference on Military Communications and Information Systems (ICMCIS), 2019.

28. Al-Issax, A., Bentaleb, A., Barakabitzex, A., Zinnery, T., Ghita, B., Bandwidth Prediction Schemes for Defining Bitrate Levels in SDN-enabled Adaptive Streaming. 15th International Conference on Network and Service Management (CNSM), 2019.

29. Fan, Z. and Liu, R., Investigation of machine learning based network traffic classification. Proceedings of ISWCS, pp. 1–6, 2017.

30. Song, C., Park, Y., Golani, K., Kim, Y., Bhatt, K., Goswami, K., Machine-learning based threat-aware system in software defined networks. Proceedings of IEEE ICCCN, pp. 1–9, 2017.

31. Glick, M. and Rastegarfar, H., Scheduling and control in hybrid data centers. Proceedings IEEE PHOSST’17, pp. 115–116, 2017.

32. Xiao, P., Qu, W., Qi, H., Xu, Y., Li, Z., An efficient elephant flow detection with cost-sen-sitive in SDN. 2015 1st International Conference on Industrial Networks and Intelligent Systems (INISCom), pp. 24–28, 2015.

33. Amaral, P., Dinis, J., Pinto, P., Bernardo, L., Tavares, J., Mamede, H., Machine learning in software defined networks: Data collection and traffic classification. Proceedings of IEEE ICNP’16, pp. 1–5, 2016.

34. Huang, T., Zhang, R., Zhou, C., Sun, L., QARC: video quality aware rate control for real-time video streaming based on deep reinforcement learning. ACM Multimedia Conference, ACM, 2018.

35. Azzouni, A. and Pujolle, G., Neutm: A neural network-based framework for traffic matrix prediction in SDN. IEEE/IFIP Network Operations and Management Symposium, 2018.

36. Jain, S., Khandelwal, M., Katkar, A., Nygate, J., Applying big data technologies to manage QoS in an SDN. Proceedings of IEEE CNSM’16, pp. 302–306, 2016.

37. Pasquini, R. and Stadler, R., Learning end-to-end application QoS from OpenFlow switch statistics. Proceedings of IEEE NETSOFT’17, pp. 1–9, 2017.

38. Letaifa, A., Adaptive QoE monitoring architecture in SDN networks: Video streaming services case. Proceedings of IEEE IWCMC’17, pp. 1383–1388, 2017.

39. Abar, T., Letaifa, A., Asmi, S., Machine learning based QoE prediction in SDN networks. Proceedings of IEEE IWCMC’17, pp. 1395–1400, 2017.

40. Comaneci, D. and Dobre, C., Securing Networks using SDN and Machine Learning. IEEE International Conference on Computational Science and Engineering, 2018.

41. Murudkar, C.V. and Gitlin, R.D., QoE-driven Anomaly Detection in Self Organizing Mobile Networks using Machine Learning. 2019 Wireless Telecommunications Symposium (WTS), pp. 1–5, April 2019.

42. Murudkar, C. and Gitlin, R., Machine Learning for QoE Prediction and Anomaly Detection in Self-Organizing Mobile Networking Systems. Int. J. Wireless Mobile Networks (IJWMN), 11, 2, April 2019.

43. Yao, H., Mai, T., Xu, X., Zhang, P., Li, M., Liu, Y., NetworkAI: An Intelligent Network Architecture for Self-Learning Control Strategies in Software Defined Networks. IEEE Internet Things J., 5, 4319–4327, 2018.

44. Zhu, L., Tang, X., Shen, M., Du, X., Guizani, M., Privacy-preserving DDoS attack detection using cross-domain traffic in software defined networks. IEEE J. Sel. Areas Commun., 36, 628–643, 2018.

45. Côté, D., Using machine learning in communication networks. J. Opt. Commun. Networks, 10, D100–D109, 2018.

46. Sultana, N., Chilamkurti, N., Peng, W., Alhadad, R., Survey on SDN based network intrusion detection system using machine learning approaches. Peer-Peer Network Appl., 12, 2, 493–501, 2019.

47. Moura, H., Alves, A., Borges, J., Macedo, D., Vieira, M., Ethanol: A Software-Defined Wireless Networking architecture for IEEE 802.11 networks. Comput. Commun., Elsevier, 149, 176–188, 2020.

48. Lei, T., Wen, X., Lu, Z., Li, Y., A semi-matching based load balancing scheme for dense IEEE 802.11 WLANs. IEEE Access, 5, 15332–15339, 2017.

49. Peng, M., He, G., Wang, L., Kai, C., AP Selection Scheme Based on Achievable Throughputs in SDN-Enabled WLANs. IEEE Access, IEEE, 7, 4763–4772, 2019.

50. Fulara, H., Singh, G., Jaisinghani, D., Maity, M., Chakraborty, T., Naik, V., Use of machine learning to detect causes of unnecessary active scanning in wifi networks. Proceedings of WoWMoM, pp. 1–9, 2019.

51. Ernst, J., Kremer, S., Rodrigues, J., A utility based access point selection method for IEEE 802.11 wireless networks with enhanced quality of experience. Proceedings of IEEE ICC, pp. 2363–2368, 2014.

52. Chen, J., Liu, B., Zhou, H., Yu, Q., Gui, L., Shen, X., QoS-driven efficient client association in high-density software-defined WLAN. IEEE Trans. Veh. Technol., 66, 7372–7383, 2017.

53. Quer, G., Baldo, N., Zorzi, M., Cognitive call admission control for voip over ieee 802.11 using bayesian networks. In Proceedings of GLOBECOM, IEEE, pp. 1–6, 2011.

54. Coronado, E., Villalon, J., Garrido, A., Wi-balance: SDN-based load-balancing in enterprise WLANs. IEEE Conference on Network Softwarization (NetSoft), pp. 1–2, 2017.

55. Jagannath, J., Polosky, N., Jagannath, A., Restuccia, F., Melodia, T., Machine learning for wireless communications in the internet of things: A comprehensive survey. Ad Hoc Networks, 93, 2019. Elsevier. https://doi.org/10.1016/j.adhoc.2019.101913.

56. Sanguanpuak, T., Guruacharya, S., Rajatheva, N., Bennis, M., Latva-Aho, M., Multioperator spectrum sharing for small cell networks: A matching game perspective. IEEE Trans. Wireless Commun., 16, 3761–3774, 2017.

57. Grimaldi, S., Mahmood, A., Gidlund, M., An SVM-based method for classification of external interference in industrial wireless sensor and actuator networks. J. Sens. Actuator Networks, 6, 9, 2017. https://doi.org/10.3390/jsan6020009

58. Kulin, M., Kazaz, T., Moerman, I., Poorter, E., End-to-end learning from spectrum data: a deep learning approach for wireless signal identification in spectrum monitoring applications. IEEE Access, 6, 18484–18501, 2018.

59. Youssef, K., Bouchard, L., Haigh, K., Krovi, H., Silovsky, J., Valk, C., Machine learning approach to RF transmitter identification. IEEE J. Radio Freq. Identif., 2, 197–205, 2018.

60. Davaslioglu, K., Soltani, S., Erpek, T., Sagduyu, Y., DeepWiFi: Cognitive WiFi with Deep Learning. IEEE Trans. Mobile Comput., 20, 429–444 2019.

61. Jeunen, O., Bosch, P., Herwegen, M., Doorselaer, K., Godman, N., Latre, S., A machine learning approach for ieee 802.11 channel allocation. 14th International Conference on Network and Service Management (CNSM), pp. 28–36, 2018.

62. Baid, A. and D. Raychaudhuri, D., Understanding channel selection dynamics in dense Wi-Fi networks. IEEE Commun. Mag., 53, 110–117, 2015.

63. Herzen, J., Lundgren, H., Hegde, N., Learning Wi-Fi Performance. 12th Annual International Conference on Sensing, Communication, and Networking (SECON), IEEE, 2015.

64. Sui, K., Zhou, M., Liu, D., Ma, M., Pei, D., Zhao, Y., Li, Z., Moscibroda, T., Characterizing and Improving WiFi Latency in Large-Scale Operational Networks. The 14th ACM International Conference on Mobile Systems, Applications, and Services, ACM, 2016.

65. Coronado, E., Thomas, A., Riggio, R., Adaptive ML-Based Frame Length Optimization in Enterprise SD-WLANs. J. Network Syst. Manage., Springer, 28, 850–881, 2020.

66. Ibarrola, E., Davis, M., Voisin, C., Close, C., Cristobo, L., QoE Enhancement in Next Generation Wireless Ecosystems: A Machine Learning Approach. IEEE Commun. Stand. Mag., 3, 63–70, 2019.

67. Košťál, K., Bencel, R., Ries, M., Trúchly, P., Kotuliak, I., High Performance SDN WLAN Architecture. Sensors, 29, 8, 1880, 8, 2019.

68. Wang, Z., Xu, Y., Li, L., Tian, H., Cui, S., Handover control in wireless systems via asynchronous multi-user deep reinforcement learning. IEEE Internet Things J., IEEE, 5, 4296–4307, 2018.

69. Sequeira, L., Cruz, J., Ruiz-Mas, J., Saldana, J., Fernandez-Navajas, J., Almodovar, J., Building an SDN enterprise WLAN based on virtual APs. IEEE Commun. Lett., 21, 374–377, 2017.

70. Kumar, V., Kandpal, D.C., Jain, M., Gangopadhyay, R., Debnath, S., K-mean clustering based cooperative spectrum sensing in generalized fading channels. Twenty Second National Conference on Communication (NCC), IEEE, pp. 1–5, 2016.

71. Lu, Y., Zhu, P., Wang, D., Fattouche, M., Machine learning techniques with probability vector for cooperative spectrum sensing in cognitive radio networks. IEEE Wireless Communications and Networking Conference, IEEE, pp. 1–6, 2016.

72. Sobabe, G., Song, Y., Bai, X., Guo, B., A cooperative spectrum sensing algorithm based on unsupervised learning. 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), IEEE, pp. 1–6, 2017.

73. Wang, Y., Zhang, Y., Wan, P., Zhang, S., Yang, J., A spectrum sensing method based on empirical mode decomposition and k-means clustering algorithm. Wireless Commun. Mobile Comput., 2018, Article ID 6104502, 10, 2018.

74. Zhang, S., Wang, Y., Li, J., Wan, P., Zhang, Y., Li, N., A cooperative spectrum sensing method based on information geometry and fuzzy c-means clustering algorithm. EURASIP J. Wireless Commun. Networking, 17, 2019, 2019.

75. Hassan, Y., El-Tarhuni, M., Assaleh, K., Learning-based spectrum sensing for cognitive radio systems. J. Comput. Networks Commun., 13, 2012, 2012.

76. Ahmad, H., Ensemble classifier based spectrum sensing in cognitive radio networks. Wireless Commun. Mobile Comput., 2019, 16, 2019.

77. Huang, X., Hu, F., Wu, J., Chen, H., Wang, G., Jiang, T., Intelligent cooperative spectrum sensing via hierarchical dirichlet process in cognitive radio networks. IEEE J. Sel. Areas Commun., 33, 771–787, 2015.

78. Liu, B., Li, Z., Si, J., Zhou, F., Blind continuous hidden markov model-based spectrum sensing and recognition for primary user with multiple power levels. IET Commun., 9, 1396–1403, 2015.

79. Paul, A. and Maity, S., Kernel fuzzy c-means clustering on energy detection based cooperative spectrum sensing. Digital Commun. Networks, 2, 196–205, 2016.

80. Awe, O. and Lambotharan, S., Cooperative spectrum sensing in cognitive radio networks using multi-class support vector machine algorithms. 9th International Conference on Signal Processing and Communication Systems (ICSPCS), IEEE, pp. 1–7, 2015.

81. Xu, Y., Cheng, P., Chen, Z., Li, Y., Vucetic, B., Mobile collaborative spectrum sensing for heterogeneous networks: A bayesian machine learning approach. IEEE Trans. Signal Process., 66, 5634–5647, 2018.

1 Email: murads@ppu.edu

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