Abstract
Wireless user perception (WiUP) plays an important role in designing next-generation wireless communications systems. Users are very sensitive with the quality of WiUP. However, the bad quality of WiUP cannot be identified with traditional methods. In this paper, we propose an intelligent identification method using unsupervised machine learning. More precisely, we create an algorithm model based on historical data to realize feature extraction and clustering. The most similar cluster to those cells with bad WiUP is identified according to Euclidean distance. The experiment is conducted on the basis of a large amount of historical data. With several contrast experiments, Simulation results show that the method proposed achieves the accuracy of identification of bad WiUP over 93%. The study manifests that unsupervised machine learning is effective in identifying bad WiUP in wireless networks.
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References
Na, Z., Wang, Y., Li, X., Xia, J., Liu, X., Xiong, M.: Subcarrier allocation based Simultaneous Wireless Information and Power Transfer algorithm in 5G cooperative OFDM communication systems. Phys. Commun. 29, 164–170 (2018)
Liu, X., Zhang, X., Jia, M., Fan, L., Lu, W., Zhai, X.: 5G-based green broadband communication system design with simultaneous wireless information and power transfer. Phys. Commun. 28, 130–137 (2018)
Liu, M., Song, T., Gui, G.: Deep cognitive perspective: resource allocation for NOMA based heterogeneous IoT with imperfect SIC. IEEE Internet Things J. 1–11 (2018). https://doi.org/10.1109/jiot.2018.2876152
Liu, M., Yang, J., Song, T., Hu, J., Gui, G.: Deep learning-inspired message passing algorithm for efficient resource allocation in cognitive radio networks. IEEE Trans. Veh. Technol. 68(1), 641–653 (2018)
Wu, J., Dong, M., Ota, K., Li, J., Guan, Z.: FCSS: fog computing based content-aware filtering for security services in information centric social networks. IEEE Trans. Emerg. Top. Comput. (2017). https://doi.org/10.1109/TETC.2017.2747158
Zhang, N., et al.: Software defined networking enabled wireless network virtualization: challenges and solutions. IEEE Netw. 31(5), 42–49 (2017)
Cicalo, S., Sayadi, B., Faucheux, F., Tralli, V., Kerboeuf, S., Changuel, N.: Improving QoE and fairness in HTTP adaptive streaming over LTE network. IEEE Trans. Circuits Syst. Video Technol. 26(12), 2284–2298 (2015)
Brito, I.V.S., Figueiredo, G.B.: Improving QoS and QoE through seamless handoff in software-defined IEEE 802.11 mesh networks. IEEE Commun. Lett. 21(11), 2484–2487 (2017)
Kato, N., et al.: The deep learning vision for heterogeneous network traffic control: proposal, challenges, and future perspective. IEEE Wirel. Commun. 24(3), 146–153 (2017)
Kato, N.: Challenges of content-centric mobile networks. IEEE Network 31(1), 2 (2017)
Tan, S., Mayrovouniotis, M.L.: Reducing data dimensionality through optimizing neural network inputs. AIChE J. 41(6), 1471–1480 (1995)
Abdulhussain, M.I., Gan, J.Q.: An experimental investigation on PCA based on cosine similarity and correlation for text feature dimensionality reduction. In: Computer Science and Electronic Engineering Conference (CCEC), pp. 1–4 (2015)
Li, X., et al.: Joint multilabel classification with community-aware label graph learning. IEEE Trans. Image Process. 25(1), 484–493 (2016)
Fadlullah, Z.M., et al.: State-of-the-art deep learning: evolving machine intelligence toward tomorrow’s intelligent network traffic control systems. IEEE Commun. Surv. Tutorials 19(4), 2432–2455 (2017)
Wang, Y., Gui, G., Zhao, N., Yin, Y., Huang, H., Li, Y.: Deep learning for optical character recognition and its application to VAT invoice recognition (2018)
Huang, H., Gui, G., Sari, H., Adachi, F.: Deep learning for super-resolution DOA estimation in massive MIMO systems. In: 2018 IEEE 88th Vehicular Technology Conference (VTC Fall), Kansas, August 2018
Gao, X., Jin, S., Wen, C.K., Li, G.Y.: ComNet: combination of deep learning and expert knowledge in OFDM receivers. IEEE Commun. Lett. 22(12), 2627–2630 (2018)
Wang, T., Wen, C., Wang, H., Gao, F., Jiang, T., Jin, S.: Deep learning for wireless physical layer: opportunities and challenges. China Commun. 14(11), 92–111 (2017)
Lam, D., Wei, M., Wunsch, D.: Clustering data of mixed categorical and numerical type with unsupervised feature learning. IEEE Access 3, 1605–1613 (2015)
Chen, K., Yang, S.: Effect of multi-hidden-layer structure on performance of BP neural network: probe. In: 2012 8th International Conference on Natural Computation, no. 2010, pp. 1–5 (2012)
Zhang, Z., Zhao, M., Chow, T.W.S.: Binary-and multi-class group sparse canonical correlation analysis for feature extraction and classification. IEEE Trans. Knowl. Data Eng. 25(10), 2192–2205 (2013)
Agarwal, S., Ranjan, P., Ujlayan, A.: Comparative analysis of dimensionality reduction algorithms, case study: PCA. In: Proceedings of 2017 11th International Conference on Intelligent Systems and Control, ISCO 2017, pp. 255–259 (2017)
Liu, Y., Liu, S., Wang, Y., Lombardi, F., Han, J.: A stochastic computational multi-layer perceptron with backward propagation. IEEE Trans. Comput. 67(9), 1273–1286 (2018)
Yang, F., Yang, L., Wang, D., Qi, P., Wang, H.: Method of modulation recognition based on combination algorithm of K-means clustering and grading training SVM. China Commun. 15, 55–63 (2018)
Dokmanic, I., Parhizkar, R., Ranieri, J., Vetterli, M.: Euclidean distance matrices: essential theory, algorithms, and applications. IEEE Signal Process. Mag. 32(6), 12–30 (2015)
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Zhang, K., Fan, G., Zeng, J., Gui, G. (2019). Identification of Wireless User Perception Based on Unsupervised Machine Learning. In: Gui, G., Yun, L. (eds) Advanced Hybrid Information Processing. ADHIP 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 301. Springer, Cham. https://doi.org/10.1007/978-3-030-36402-1_54
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DOI: https://doi.org/10.1007/978-3-030-36402-1_54
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