請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98990完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 魏宏宇 | zh_TW |
| dc.contributor.advisor | Hung-Yu Wei | en |
| dc.contributor.author | 楊士聖 | zh_TW |
| dc.contributor.author | Shih-Sheng Yang | en |
| dc.date.accessioned | 2025-08-20T16:33:50Z | - |
| dc.date.available | 2025-09-10 | - |
| dc.date.copyright | 2025-08-20 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-08-14 | - |
| dc.identifier.citation | [1] 3GPP, “Technical Specification Group Services and System Aspects; Release de-scription; Release 15,” 3rd Generation Partnership Project (3GPP), Technical Report 3GPP TR 21.915, https://www.3gpp.org/ftp/Specs/archive/21_series/21.915/.
[2] T.-S. Lin, C.-Y. Chen, Y. Chiang, Y.-J. Chen, and H.-Y. Wei, “Multi-radio selective band locking for railway communications reliability enhancement: An experimental approach,”電機工程學刊, vol. 30, no. 1, pp. 27–41, Jun 2023. [3] A. Frommgen, T. Erbshäußer, A. Buchmann, T. Zimmermann, and K. Wehrle,“Remp tcp: Low latency multipath tcp,” in 2016 IEEE International Conference on Communications (ICC), 2016, pp. 1–7. [4] E. Obiodu, A. Raman, A. K. Abubakar, S. Mangiante, N. Sastry, and A. H. Aghvami,“Dsm-moc as baseline: Reliability assurance via redundant cellular connectivity in connected cars,” IEEE Transactions on Network and Service Management, vol. 19, no. 3, pp. 2178–2194, 2022. [5] A. Hassan, A. Narayanan, A. Zhang, W. Ye, R. Zhu, S. Jin, J. Carpenter, Z. M. Mao, F. Qian, and Z.-L. Zhang, “Vivisecting mobility management in 5G cellular net- works,” in Proceedings of the ACM SIGCOMM 2022 Conference, ser. SIGCOMM ’22. New York, NY, USA: Association for Computing Machinery, Aug. 2022, pp.86–100. [6] A. Narayanan, E. Ramadan, R. Mehta, X. Hu, Q. Liu, R. A. K. Fezeu, U. K. Dayalan, S. Verma, P. Ji, T. Li, F. Qian, and Z.-L. Zhang, “Lumos5G: Mapping and Predicting 47 doi: 10.6342/NTU202504235 Commercial mmWave 5G Throughput,” in Proceedings of the ACM Internet Mea- surement Conference. Virtual Event USA: ACM, Oct. 2020, pp. 176–193. [7] Z. Liu, Q. Deng, Z. Tan, Z. Qian, X. Zhang, A. Swami, and S. V. Krishnamurthy,“M2HO: Mitigating the Adverse Effects of 5G Handovers on TCP,” in Proceedings of the 30th Annual International Conference on Mobile Computing and Networking. Washington D.C. DC USA: ACM, Dec. 2024, pp. 1089–1103. [8] H. Deng, Q. Li, J. Huang, and C. Peng, “iCellSpeed: increasing cellular data speed with device-assisted cell selection,” in Proceedings of the 26th Annual International Conference on Mobile Computing and Networking. London United Kingdom: ACM, Sep. 2020, pp. 1–13. [9] A. Hassan, W. Ye, A. Zhang, J. Carpenter, R. Zhu, S. Jin, F. Qian, Z. M. Mao, and Z.-L. Zhang, “The Case for Boosting Mobile Application QoE via Smart Band Switching in 5G/xG Networks,” in Proceedings of the 25th International Workshop on Mobile Computing Systems and Applications. San Diego CA USA: ACM, Feb. 2024, pp. 127–132. [10] D. Xu, A. Zhou, X. Zhang, G. Wang, X. Liu, C. An, Y. Shi, L. Liu, and H. Ma, “Understanding operational 5g: A first measurement study on its coverage, performance and energy consumption,” in Proceedings of the Annual Conference of the ACM Special Interest Group on Data Communication on the Applications, Technologies, Architectures, and Protocols for Computer Communication, ser. SIGCOMM ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 479–494. [11] C. Xu, J. Wang, Z. Ma, Y. Cheng, Y. Ni, W. Li, F. Qian, and Y. Li, “A First Look at Disconnection-Centric TCP Performance on High-Speed Railways,” IEEE Journal on Selected Areas in Communications, vol. 38, no. 12, pp. 2723–2733, Dec. 2020. [12] A. Narayanan, X. Zhang, R. Zhu, A. Hassan, S. Jin, X. Zhu, X. Zhang, D. Rybkin, Z. Yang, Z. M. Mao, F. Qian, and Z.-L. Zhang, “A variegated look at 5G in the 48 doi: 10.6342/NTU202504235 wild: performance, power, and QoE implications,” in Proceedings of the 2021 ACM SIGCOMM 2021 Conference. Virtual Event USA: ACM, Aug. 2021, pp. 610–625. [13] K. Sun, Q. Han, Z. Yang, W. Huang, H. Zhang, and V. C. M. Leung, “Proactive Handover Type Prediction and Parameter Optimization Based on Machine Learning,” IEEE Transactions on Wireless Communications, vol. 24, no. 4, pp. 3515–3528, Apr. 2025. [14] J. Xu, B. Ai, L. Wu, and L. Chen, “Handover-aware cross-layer aided tcp with deep reinforcement learning for high-speed railway networks,” IEEE Networking Letters, vol. 3, no. 1, pp. 31–35, 2021. [15] A. Nikravesh, Y. Guo, F. Qian, Z. M. Mao, and S. Sen, “An in-depth understanding of multipath tcp on mobile devices: measurement and system design,” in Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking, ser. MobiCom ’16. New York, NY, USA: Association for Computing Machinery, 2016, p. 189–201. [16] Y. Pan, R. Li, and C. Xu, “The first 5g-lte comparative study in extreme mobility,” Proc. ACM Meas. Anal. Comput. Syst., vol. 6, no. 1, Feb. 2022. [17] T.-S. Lin, J.-Y. Yan, and H.-Y. Wei, “Experiments and observations of 5g nsa re-liability and latency performance in metro train environment,” in 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), 2022, pp. 1–5. [18] A. Narayanan, E. Ramadan, J. Carpenter, Q. Liu, Y. Liu, F. Qian, and Z.-L. Zhang,“A first look at commercial 5g performance on smartphones,” in Proceedings of The Web Conference 2020, ser. WWW ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 894–905. [19] M. Ghoshal, I. Khan, Z. J. Kong, P. Dinh, J. Meng, Y. C. Hu, and D. Koutsoniko-las, “Performance of cellular networks on the wheels,” in Proceedings of the 2023ACM on Internet Measurement Conference, ser. IMC ’23. New York, NY, USA: Association for Computing Machinery, 2023, p. 678–695.49 doi: 10.6342/NTU202504235 [20] C. Wang, H. Wang, F. Qian, K. Zheng, C. Wang, F. Mao, X. Guo, and C. Xu, “Expe-rience: A three-year retrospective of large-scale multipath transport deployment for mobile applications,” in Proceedings of the 29th Annual International Conference on Mobile Computing and Networking, ser. ACM MobiCom ’23. New York, NY, USA: Association for Computing Machinery, 2023. [21] European Telecommunications Standards Institute, “System Reference Document (SRdoc); Technical Characteristics of Mission Critical Systems Using LTE,” ETSI, Tech. Rep. TR 103 442 V1.2.1, September 2018. [Online]. Available: https://www.etsi.org/deliver/etsi_tr/103400_103499/103442/01.02.01_60/tr_103442v010201p.pdf [22] Y. Li, C. Peng, Z. Yuan, J. Li, H. Deng, and T. Wang, “Mobileinsight: extracting and analyzing cellular network information on smartphones,” in Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking,ser. MobiCom ’16. New York, NY, USA: Association for Computing Machinery, 2016, p. 202–215. [Online]. Available: https://doi.org/10.1145/2973750.2973751 [23] J. Martin, J. Burbank, W. Kasch, and P. D. L. Mills, “Network Time Protocol Version 4: Protocol and Algorithms Specification,” RFC 5905, Jun. 2010. [Online]. Available: https://www.rfc-editor.org/info/rfc5905 [24] T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016, pp. 785–794. [25] G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y. Liu, “Light-gbm: A highly efficient gradient boosting decision tree,” Advances in neural infor-mation processing systems, vol. 30, 2017. [26] R. Dey and F. M. Salem, “Gate-variants of gated recurrent unit (gru) neural net-works,” in 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS). IEEE, 2017, pp. 1597–1600.50 doi: 10.6342/NTU202504235 [27] R. Hasani, M. Lechner, A. Amini, D. Rus, and R. Grosu, “Liquid time-constant net-works,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 9, 2021, pp. 7657–7666. [28] S. Ö. Arik and T. Pfister, “Tabnet: Attentive interpretable tabular learning,” in Pro-ceedings of the AAAI conference on artificial intelligence, vol. 35, no. 8, 2021, pp.6679–6687. [29] L. Collado, “Advanced hexagon diag,” https://media.ccc.de/v/rc3-11397-advanced_hexagon_diag, 2021, talk at Remote Chaos Experience (rC3). [30] L. Grinsztajn, E. Oyallon, and G. Varoquaux, “Why do tree-based models still out-perform deep learning on typical tabular data?” in Proceedings of the 36th Interna-tional Conference on Neural Information Processing Systems, ser. NIPS ’22. Red Hook, NY, USA: Curran Associates Inc., 2022. [31] R. Shwartz-Ziv and A. Armon, “Tabular data: Deep learning is not all you need,”Information Fusion, vol. 81, pp. 84–90, 2022. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98990 | - |
| dc.description.abstract | 穩定的無線連線對於鐵路運行的安全與效率至關重要。在高移動性的 5G 非獨立組網環境中，通訊經常遭遇無線連結失敗（Radio Link Failures，RLF）與頻繁的切換（Handovers，HOs），導致傳輸層延遲增加與封包遺失。透過在地鐵與高速鐵路網路的廣泛實地測量，我們清楚觀察到這些挑戰。為了解決這些問題，我們提出 DBLC（雙頻鎖定與控制），一種結合機器學習為基礎的 RLF 預測與動態頻段切換的機制，以提升連線穩定性。我們開發了一個模擬器，透過重播真實數據機記錄並模擬傳輸層損傷，精準反映實地行為，對 DBLC 進行驗證。模擬與實際測試均證明，DBLC 相較於傳統單無線電及雙無線電方案，在縮短斷線時間與提升封包傳送率方面具有明顯優勢。本研究突顯了結合控制平面感知與數據驅動預測，提升安全關鍵行動通訊性能的優勢。 | zh_TW |
| dc.description.abstract | Reliable wireless connectivity is critical for safe and efficient railway operations. In high-mobility 5G Non-Standalone environments, communication often suffers from Radio Link Failures (RLFs) and frequent handovers (HOs), causing increased latency and packet loss at the transport layer. Based on extensive field measurements across metro and high-speed rail networks, we observe these challenges clearly. To tackle them, we propose DBLC (Dynamic Band Locking), a mechanism that combines machine learning-based RLF prediction with dynamic band switching to improve connection stability. We validate DBLC through an emulator that replays real modem logs and simulates transport-layer impairments, accurately reflecting field behaviors. Both emulation and real-world tests demonstrate DBLC’s superiority over traditional single- and dual-radio approaches by reducing disconnection duration and improving packet delivery. This study highlights the advantage of integrating control-plane awareness with data-driven prediction to enhance mobile communication in safety-critical contexts. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-20T16:33:50Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-20T16:33:50Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 摘要 iii Abstract iv Contents v List of Figures vii List of Tables viii Chapter 1. Introduction 1 Chapter 2. Related Work 5 Chapter 3. 5G Measurement 7 3.1 Experiment Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.1.1 5G Network Environment . . . . . . . . . . . . . . . . . . . . . 7 3.1.2 Hardware Setting . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.1.3 Traffic Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.1.4 Measurement Tools . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.1.5 Analysis Methods . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.2 Observation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Chapter 4. System Design 17 4.1 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.2 Dual Radio Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.3 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.3.1 Time and Band Definitions . . . . . . . . . . . . . . . . . . . . . 20 4.3.2 Band Selection Policy Based on Prediction Model . . . . . . . . 21 4.3.3 Band Switch Detection and Switching Penalty . . . . . . . . . . 22 4.3.4 Dual-Radio Model . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.3.5 Objective Functions . . . . . . . . . . . . . . . . . . . . . . . . 23 Chapter 5. Proposed Methods 25 5.1 DBLC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 5.1.1 RLF Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 5.1.2 DBLC Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 29 5.2 Data-Driven Emulator Design . . . . . . . . . . . . . . . . . . . . . . . 32 5.2.1 Emulator Architectures . . . . . . . . . . . . . . . . . . . . . . . 33 5.2.2 Band Change Design . . . . . . . . . . . . . . . . . . . . . . . . 34 Chapter 6. Evaluation 37 6.1 RLF Prediction Model Evaluation . . . . . . . . . . . . . . . . . . . . . 37 6.1.1 Model Performance Comparison . . . . . . . . . . . . . . . . . . 37 6.1.2 Cross Validation . . . . . . . . . . . . . . . . . . . . . . . . . . 40 6.2 DBLC Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 6.2.1 DBLC Evaluation through Emulator . . . . . . . . . . . . . . . . 41 6.2.2 DBLC Evaluation through Real World Measurement . . . . . . . 44 Chapter 7. Conclusion 45 Bibliography 47 | - |
| dc.language.iso | en | - |
| dc.subject | 雙無線電 | zh_TW |
| dc.subject | 無線連結失效預測 | zh_TW |
| dc.subject | 無線連結失效 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 頻段鎖定 | zh_TW |
| dc.subject | 5G非獨立組網 | zh_TW |
| dc.subject | Radio Link Failure | en |
| dc.subject | Dual-Radio | en |
| dc.subject | Machine Learning | en |
| dc.subject | RLF Prediction | en |
| dc.subject | Band Locking | en |
| dc.subject | 5G NSA | en |
| dc.title | 5G 非獨立雙無線電網路中的無線連結失效預測與自適 應頻段鎖定技術 | zh_TW |
| dc.title | Radio Link Failure Prediction and Adaptive Band Locking for Reliable 5G Non-Standalone Dual-Radio Networks | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 蔡華龍;施美如;葉佳宜 | zh_TW |
| dc.contributor.oralexamcommittee | Hua-Lung Tsai;Mei-Ru Shih;Chia-Yi Yeh | en |
| dc.subject.keyword | 無線連結失效,頻段鎖定,5G非獨立組網,雙無線電,機器學習,無線連結失效預測, | zh_TW |
| dc.subject.keyword | Radio Link Failure,Band Locking,5G NSA,Dual-Radio,Machine Learning,RLF Prediction, | en |
| dc.relation.page | 51 | - |
| dc.identifier.doi | 10.6342/NTU202504235 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2025-08-15 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電機工程學系 | - |
| dc.date.embargo-lift | 2030-08-08 | - |
| 顯示於系所單位： | 資訊工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-113-2.pdf 未授權公開取用 | 2.18 MB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。