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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 魏宏宇 | zh_TW |
| dc.contributor.advisor | Hung-Yu Wei | en |
| dc.contributor.author | Alexandre Benayoun | zh_TW |
| dc.contributor.author | Alexandre Benayoun | en |
| dc.date.accessioned | 2023-09-22T17:26:56Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-09-22 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-12 | - |
| dc.identifier.citation | [1] Anna Giannakou, Dipankar Dwivedi and Sean Peisert, “A machine learning approach for packet loss prediction in science flows,” Future Generation Computer Systems, vol. 102, pp. 190–197, 2020. [Online]. Available: https: //www.sciencedirect.com/science/article/pii/S0167739X19305850
[2] Dr. Kalpana Saha (Roy) and Tune Ghosh, “Study of packet loss prediction using machine learning,” International Journal of Mobile Communication Networking, vol. 11, pp. 1–11, 2020. [3] Amir F. Atiya, Sung Goo Yoo, Kil To Chong and Hyongsuk Kim, “Packet loss rate prediction using the sparse basis prediction model,” IEEE Transactions on Neural Networks, vol. 18, no. 3, pp. 950–954, 2007. [Online]. Available: https://ieeexplore.ieee.org/document/4182366 [4] HoomanHomayounfard,“Packet-losspredictionmodelbasedonhistoricalsymbolic time-series forecasting,” Ph.D. dissertation, University of Techology of Sidney, 2013. [Online]. Available: http://hdl.handle.net/10453/24097 [5] Chun You and Kavitha Chandra, “Time series models for internet data traffic,” in Proceedings 24th Conference on Local Computer Networks. LCN’99, 1999, pp. 164–171. [Online]. Available: https://ieeexplore.ieee.org/document/802013 [6] Rishabh Chauhan and Sunil Kumar, “Packet loss prediction using artificial intelligence unified with big data analytics, internet of things and cloud computing technologies,” in 2021 5th International Conference on Information Systems and Computer Networks (ISCON), 2021, pp. 01–06. [Online]. Available: https://ieeexplore.ieee.org/document/9702517 [7] Ali Safari Khatouni, Francesca Soro and Danilo Giordano, “A machine learning application for latency prediction in operational 4g networks,” in 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), 2019, pp. 71–74. [Online]. Available: https://ieeexplore.ieee.org/document/8717807 [8] Jefferson Silva, Marcio Kreutz, Monica Pereira and Marjory Da Costa-Abreu, “An investigation of latency prediction for noc-based communication architectures using machine learning techniques,” Journal of Supercomputing, pp. 1–19, 2019. [Online]. Available: http://shura.shu.ac.uk/25392/ [9] Robert Beverly, Karen Sollins and Arthur Berger, “Svm learning of ip adress strucure for latency prediction,” in Proceedings of the 2006 SIGCOMM Workshop on Mining Network Data, ser. MineNet ’06, 2006, pp. 299–304. [Online]. Available: https://doi.org/10.1145/1162678.1162682 [10] Bruno Astuto Arouche Nunes, Kerry Veenstra, William Ballenthin, Stephanie Lukin and Katia Obraczka, “A machine learning framework for tcp round-trip time estimation,” EURASIP Journal on Wireless Communications and Networking, no. 47, 2014. [Online]. Available: https://jwcn-eurasipjournals.springeropen.com/articles/10.1186/1687-1499-2014-47 [11] Desta Haileselassie Hagos, Paal E. Engelstad, Anis Yazidi and Carsten Griwodz, “A deep learning approach to dynamic passive rtt prediction model for tcp,” in 2019 IEEE 38th International Performance Computing and Communications Conference (IPCCC), 2019, pp. 1–10. [Online]. Available: https://ieeexplore.ieee.org/document/8958727 [12] Salman Memon and Muthucumaru Maheswaran, “Using machine learning for handover optimization in vehicular fog computing,” in Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, ser. SAC ’19, 2019, p. 182–190. [Online]. Available: https://doi.org/10.1145/3297280.3297300 [13] Hadeel Abdah, Joao Paulo Barraca and Rui L. Aguiar, “Handover prediction integrated with service migration in 5g systems,” in ICC 2020 - 2020 IEEE International Conference on Communications (ICC), 2020, pp. 1–7. [Online]. Available: https://ieeexplore.ieee.org/document/9149426 [14] Le Luong Vy, Li-Ping Tung and Bao-Shuh Paul Lin, “Big data and machine learning driven handover management and forecasting,” in 2017 IEEE Conference on Standards for Communications and Networking (CSCN), 2017, pp. 214–219. [Online]. Available: https://ieeexplore.ieee.org/document/8088624 [15] James A. Green, “Too many zeros and/or highly skewed? a tutorial on modelling health behaviour as count data with poisson and negative binomial regression,” Health psychology and behavioral medicine 2021, vol. 9, no. 1, pp. 436–455, 2021. [Online]. Available: https://www.tandfonline.com/doi/full/10.1080/21642850.2021.1920416 [16] Rob J. Hyndman and Anne B. Koehler, “Another look at measures of forecast accuracy,” International Journal of Forecasting, vol. 22, no. 4, pp. 679–688, 2006. [Online]. Available: https://doi.org/10.1016/j.ijforecast.2006.03.001 [17] Alexei Botchkarev, “A new typology design of performance metrics to measure errors in machine learning regression algorithms,” Interdisciplinary Journal of Information, Knowledge, and Management, vol. 14, pp. 45–79, 2019. [Online]. Available: https://doi.org/10.28945/4184 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90108 | - |
| dc.description.abstract | none | zh_TW |
| dc.description.abstract | This study focuses on the use of machine learning techniques to predict three important telecommunication parameters in the context of metro passengers itineraries. These parameters are the base station changes, the latency of the signal and the number of packet loss. They represent abnormal phenomenon, or events that can alter the phone’s performance. Being able to successfully predict it can lead to a better anticipation of these issues and enhance the user’s experience.
The primary objective of this research is to compare different machine learning models for real-time predictions of the studied parameters. To do so, different algorithms (Neural Networks, Recurrent Neural Networks, LSTM and ARIMA), as well as several sets of features will be used. A comparison on the error metrics will also be conducted. The study took novel approaches in the nature of the studied parameters and the prediction delay, as it aims to forecast the value a few seconds into the future. Additionally, it proposes new solutions to make predictions with a dataset mainly composed of zeros. Overall, this study contributes to the understanding of machine learning applications in predicting telecommunication parameters in the case of metro passengers itineraries. The findings suggest that the selected machine learning algorithms, combined with appropriate error metrics and innovative approaches, offer reliable solutions for real-time predictions for the three studied parameters. Besides, the proposed solutions to avoid models that always predict zero with datasets mainly composed of null values proved to be successful. The predictions from the chosen models will help in the decision-making of the settings of the phone, to avoid abnormal phenomenon and maintain a good performance throughout the user’s route. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-09-22T17:26:56Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-09-22T17:26:56Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
Abstract ii Chapter 1. Introduction 1 1.1 Motivation 1 1.2 Related Works 2 1.3 Contribution 5 Chapter 2. Methodology 7 2.1 System Architecture 7 2.2 Processing Workflow 9 2.2.1 Data Collection 9 2.2.2 Preprocessing 10 2.2.3 Cross Validation 20 2.2.4 Testing 22 Chapter 3. Error Metrics 23 3.1 Structure of the metrics 23 3.2 Determining point distance 24 3.3 Normalization 25 3.4 Aggregation over a dataset 25 3.5 Choice of error metrics 25 Chapter 4. Neural Networks 29 4.1 Change of base station 29 4.1.1 Classification 30 4.1.2 Regression 30 4.1.3 Conclusion 33 4.2 Latency 34 4.2.1 Results 34 4.2.2 Conclusion 36 4.3 Packet Loss 37 4.3.1 Classification 37 4.3.2 Regression with loss 39 4.3.3 Regression with enlarged loss 40 4.3.4 Prediction with a new dataset 41 4.3.5 Conclusion 43 Chapter 5. Time series models 45 5.1 Base station 48 5.1.1 Results 49 5.1.2 Conclusion 52 5.2 Latency 53 5.2.1 Recurrent Neural Networks 53 5.2.2 ARIMA 57 5.2.3 Conclusion 60 5.3 Packet Loss 61 5.3.1 Results 61 5.3.2 Conclusion 63 Chapter 6. Test of the selected models 64 6.1 Base station 64 6.2 Latency 65 6.3 Packet Loss 66 Chapter 7. Conclusions and Future Directions 68 Bibliography 70 Appendices 73 | - |
| dc.language.iso | en | - |
| dc.subject | none | zh_TW |
| dc.subject | Neural Networks | en |
| dc.subject | Machine Learning | en |
| dc.subject | latency | en |
| dc.subject | handover | en |
| dc.subject | packet loss | en |
| dc.subject | error metric | en |
| dc.subject | time series | en |
| dc.title | 使用機器學習預測 3 個電信參數: 地鐵乘客行程 | zh_TW |
| dc.title | Using Machine Learning to Predict 3 Telecommunication Parameters: the Case of Metro Passengers Itineraries | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | Chih-Yu Wang;Rafael Kaliski;Dande Bhargavi;Danielle Andreu | zh_TW |
| dc.contributor.oralexamcommittee | Chih-Yu Wang;Rafael Kaliski;Dande Bhargavi;Danielle Andreu | en |
| dc.subject.keyword | none, | zh_TW |
| dc.subject.keyword | Machine Learning,latency,handover,packet loss,error metric,time series,Neural Networks, | en |
| dc.relation.page | 74 | - |
| dc.identifier.doi | 10.6342/NTU202304005 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2023-08-13 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電信工程學研究所 | - |
| 顯示於系所單位： | 電信工程學研究所 | |
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| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-111-2.pdf | 26.89 MB | Adobe PDF | 檢視/開啟 |
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