研究生: |
Olivia Ferlita Olivia Ferlita |
---|---|
論文名稱: |
運用多輸出類神經網路預測無線網路覆蓋範圍 Predicting the wireless network coverage using multi-output neural network |
指導教授: |
呂政修
Jenq-Shiou Leu |
口試委員: |
陳郁堂
Yie-Tarng Chen 鄭瑞光 Ray-Guang Cheng 方文賢 Wen-Hsien Fang |
學位類別: |
碩士 Master |
系所名稱: |
電資學院 - 電子工程系 Department of Electronic and Computer Engineering |
論文出版年: | 2021 |
畢業學年度: | 109 |
語文別: | 英文 |
論文頁數: | 75 |
中文關鍵詞: | Self-Organizing Network (SON) 、Multi-output neural network 、Signal coverage prediction 、Signal coverage map 、Coverage status prediction 、Coverage status map |
外文關鍵詞: | Self-Organizing Network (SON), Multi-output neural network, Signal coverage prediction, Signal coverage map, Coverage status prediction, Coverage status map |
相關次數: | 點閱:191 下載:0 |
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[1] M. M. Hasan, S. Kwon, and S. Oh, “Frequenthandover mitigation in ultra-dense heterogeneous networks,” IEEE Transactions on Vehicular Technology, vol. 68, no. 1, pp. 1035–1040, 2019.
[2] 3rd Generation Partnership Project, Universal Mobile Telecommunications System (UMTS), Self Organizing Networks (SON): Concepts and requirements, 01 2015.
[3] 3rd Generation Partnership Project, Universal Mobile Telecommunications System (UMTS), Radio measurement collection for Minimization of Drive Tests (MDT): Overall description, 04 2016.
[4] T. Rappaport, Wireless Communications: Principles and Practice. USA: Prentice Hall PTR, 2nd ed., 2001.
[5] R. Kumar, S. Srivastava, J. R. Gupta, and A. Mohindru, “Comparative study of neural networks for dynamic nonlinear systems identification,” Soft Comput., vol. 23, p. 101–114, Jan. 2019.
[6] B. Sayrac, J. Riihijärvi, P. Mähönen, S. Ben Jemaa, E. Moulines, and S. Grimoud, “Improving coverage estimation for cellular networks with spatial bayesian prediction based on measurements,” in Proceedings of the 2012 ACM SIGCOMM Workshop on Cellular Networks: Operations, Challenges, and Future Design, CellNet ’12, (New York, NY, USA), p. 43–48, Association for Computing Machinery, 2012.
[7] L. Yi, X. Deng, M. Wang, D. Ding, and Y. Wang, “Localized confident information coverage hole detection in internet of things for radioactive pollution monitoring,” IEEE Access, vol. 5, pp. 18665–18674, 2017.
[8] A. GalindoSerrano, B. Sayrac, S. Ben Jemaa, J. Riihijärvi, and P. Mähönen, “Automated coverage hole detection for cellular networks using radio environment maps,” in 2013 11th International Symposium and Workshops on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt), pp. 35–40, 2013.
[9] D. Kong, J. Zhu, C. Duan, L. Lu, and D. Chen, “Bayesian linear regression for surface roughness prediction,” Mechanical Systems and Signal Processing, vol. 142, p. 106770, 2020.
[10] S. Liu, M. Lu, H. Li, and Y. Zuo, “Prediction of gene expression patterns with generalized linear regression model,” Frontiers in Genetics, vol. 10, p. 120, 2019.
[11] H. AlShehri, A. AlQarni, L. AlSaati, A. Batoaq, H. Badukhen, S. Alrashed, J. Alhiyafi, and S. O. Olatunji, “Student performance prediction using support vector machine and k-nearest neighbor,” in 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1–4, 2017.
[12] L. Kuang, H. Yan, Y. Zhu, S. Tu, and X. Fan, “Predicting duration of traffic accidents based on cost-sensitive bayesian network and weighted k-nearest neighbor,” Journal of Intelligent Transportation Systems, vol. 23, no. 2, pp. 161–174, 2019.
[13] B. M. Henrique, V. A. Sobreiro, and H. Kimura, “Stock price prediction using support vector regression on daily and up to the minute prices,” The Journal of Finance and Data Science, vol. 4, no. 3, pp. 183–201, 2018.
[14] Q. Quan, Z. Hao, H. Xifeng, and L. Jingchun, “Research on water temperature prediction based on improved support vector regression,” Neural Computing and Applications, pp. 1–10, 03 2020.
[15] P. A. Harrison, R. Dunford, D. N. Barton, E. Kelemen, B. MartínLópez, L. Norton, M. Termansen, H. Saarikoski, K. Hendriks, E. GómezBaggethun, B. Czúcz, M. GarcíaLlorente, D. Howard, S. Jacobs, M. Karlsen, L. Kopperoinen, A. Madsen, G. Rusch, M. van Eupen, P. Verweij, R. Smith, D. Tuomasjukka, and G. Zulian, “Selecting methods for ecosystem service assessment: A decision tree approach,” Ecosystem Services, vol. 29, pp. 481–498, 2018. SI: Synthesizing OpenNESS.
[16] M. I. AlHajri, N. T. Ali, and R. M. Shubair, “Indoor localization for iot using adaptive feature selection: A cascaded machine learning approach,” IEEE Antennas and Wireless Propagation Letters, vol. 18, no. 11, pp. 2306–2310, 2019.
[17] S. Rufaida, J.S. Leu, K.W. Su, A. Haniz, and J.I. Takada, “Construction of an indoor radio environment map using gradient boosting decision tree,” Wireless Networks, vol. 26, 11 2020.
[18] X. Zhen, M. Yu, X. He, and S. Li, “Multi-target regression via robust low-rank
learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 2, pp. 497–504, 2018.
[19] O. Reyes and S. Ventura, “Performing multi-target regression via a parameter sharing-based deep network,” International Journal of Neural Systems, 03 2019.
[20] Y. Kong, D. Li, Y. Fan, and J. Lv, “Interaction pursuit in high-dimensional
multi-response regression via distance correlation,” The Annals of Statistics, vol. 45, no. 2, pp. 897 – 922, 2017.
[21] Z. Zheng, M. T. Bahadori, Y. Liu, and J. Lv, “Scalable interpretable multi-response regression via seed,” Journal of Machine Learning Research, vol. 20, no. 107, pp. 1–34, 2019.
[22] H. Borchani, G. Varando, C. Bielza, and P. Larranaga, “A survey on multi-output
regression,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 5, 07 2015.
[23] Y. Goldberg, “Neural network methods for natural language processing,” Synthesis Lectures on Human Language Technologies, vol. 10, no. 1, pp. 1–309, 2017.
[24] T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyama, “Optuna: A nextgeneration
hyperparameter optimization framework,” KDD ’19, (New York, NY, USA), p. 2623–2631, Association for Computing Machinery, 2019.