Reinforcement Learning for Dynamic Channel Assignment Using Predicted Mobile Traffic

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：13

、訪客IP：18.218.184.214

姓名

翁柏肯(Natpakan Wongchamnan) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

(Reinforcement Learning for Dynamic Channel Assignment Using Predicted Mobile Traffic)

相關論文

★ 具多重樹狀結構之可靠性群播傳輸	★ 在嵌入式行動裝置上設計與開發跨平台Widget
★ 在 ARM 架構之嵌入式系統上實作輕量化的手持多媒體播放裝置圖形使用者介面函式庫	★ 基於網路行動裝置所設計可擴展的服務品質感知GStreamer模組
★ 針對行動網路裝置開發可擴展且跨平台之GSM/HSDPA引擎	★ 於單晶片多媒體裝置進行有效率之多格式解碼管理
★ IMS客戶端設計與即時通訊模組研發：個人資訊交換模組與即時訊息模組實作	★ 在可攜式多媒體裝置上實作人性化的嵌入式小螢幕網頁瀏覽器
★ 以IMS為基礎之及時語音影像通話引擎的實作:使用開放原始碼程式庫	★ 電子書嵌入式開發: 客制化下載服務實作, 資料儲存管理設計
★ 於數位機上盒實現有效率訊框參照處理與多媒體詮釋資料感知的播放器設計	★ 具數位安全性的電子書開發：有效率的更新模組與資料庫實作
★ 適用於異質無線寬頻系統的新世代IMS客戶端軟體研發	★ 在可攜式數位機上盒上設計並實作重配置的圖形使用者介面
★ Friendly GUI design and possibility support for E-book Reader based Android client	★ Effective GUI Design and Memory Usage Management for Android-based Services

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

在蜂窩網絡中，經典的問題是信道分配，它為小區中的每個請求選擇要分配的信道。移動流量數據和移動設備的數量每年都在增長，但頻道數量有限。大多數作品將傳統的強化學習應用於信道分配而沒有預測的移動流量，並且與實際情況無關。
另外，流量預測結果對於行動流量的動態性質也很好。因此，我們提出一種強化學習，提出了一種針對動態信道分配的強化學習框架，該框架考慮了流量預測，旨在最大程度地降低服務阻塞概率。我們使用近端策略優化算法（PPO）模型將阻塞概率和信道利用率與傳統方法進行比較，在意大利米蘭的144個基地台中創建了1350個信道，並使用了2013年11月1日至2013年12月31日的移動流量數據，使用DCA算法和其他強化學習模型進行模擬。

摘要(英)

In cellular networks, the classic problem is channel assignment, which selects a channel to allocate for each request in a cell. However, the mobile traffic data and the number of mobile devices grow up every year, but the number of channels is limited. Most works apply traditional reinforcement learning in channel assignment without predicted mobile traffic and do not concern with real situations. In addition, mobile traffic prediction result works well for the dynamic nature of mobile traffic. Hence, we present a reinforcement learning framework for dynamic channel assignment which takes into account the mobile traffic prediction, which aims at minimizing the service blocking probability. In the simulation, we make 144 base stations in Milano, Italy with 1350 channels and using Mobile traffic data from November 1, 2013, to December 31, 2013, using Proximal Policy Optimization (PPO) model to compare blocking probability and channel utilization with traditional DCA algorithm and others reinforcement learning models.

關鍵字(中)

★ 強化學習
★ 頻道指派
★ 近端策略優化算法

關鍵字(英)

★ Reinforcement learning
★ Channel assignment
★ Proximal Policy Optimization

論文目次

Abstract ii
Acknowledgement iii
Contents iv
List of Figures vi
List of Tables vii
1 Introduction 1
1.1 Motivation..................................................................... 1
1.2 Background.................................................................... 1
1.3 Contribution................................................................... 2
1.4 Framework .................................................................... 2
2 Related Work 3
2.0.1 Channel Assignment................................................... 3
2.0.2 Mobile Traffic Prediction .............................................. 3
2.0.3 Mobile Data analysis .................................................. 4
3 Preliminary 5
3.0.1 Deep Reinforcement Learning ......................................... 5
4 System design 8
4.0.1 Past Mobile Traffic .................................................... 9
4.0.2 Current Mobile Traffic................................................. 9
4.0.3 Prediction Model ...................................................... 9
4.0.4 DCA Environment..................................................... 9
4.0.5 Deep Reinforcement Learning ......................................... 11
4.0.6 System Execution Algorithm.......................................... 11
5 Result 13
5.0.1 Dataset................................................................. 13
5.0.2 Division of datasets.................................................... 14
5.0.3 Simulation settings .................................................... 14
5.0.4 Evaluation Metrics..................................................... 15
5.0.5 Hyperparameter tuning................................................ 15
5.0.6 Evaluation Results..................................................... 20
6 Conclusion 22
Bibliography 23

參考文獻

[1] K. Zheng, Z. Yang, K. Zhang, P. Chatzimisios, K. Yang, and W. Xiang, “Big data-driven
optimization for mobile networks toward 5g,” IEEE network, vol. 30, no. 1, pp. 44–51, 2016.
[2] G. M. D. T. Forecast, “Cisco visual networking index: global mobile data traffic forecast
update, 2017–2022,” Update, vol. 2017, p. 2022, 2019.
[3] M. Shafi, A. F. Molisch, P. J. Smith, T. Haustein, P. Zhu, P. De Silva, F. Tufvesson,
A. Benjebbour, and G. Wunder, “5g: A tutorial overview of standards, trials, challenges,
deployment, and practice,” IEEE journal on selected areas in communications, vol. 35,
no. 6, pp. 1201–1221, 2017.
[4] M. Sivakumaran and P. Iacopino, “The mobile economy 2018,” GSMA Intelligence, 2018.
[5] S. P. Singh and D. P. Bertsekas, “Reinforcement learning for dynamic channel allocation in
cellular telephone systems,” in Advances in neural information processing systems, pp. 974–
980, 1997.
[6] J. Nie and S. Haykin, “A q-learning-based dynamic channel assignment technique for mobile communication systems,” IEEE Transactions on Vehicular Technology, vol. 48, no. 5,
pp. 1676–1687, 1999.
[7] N. Lilith and K. Dogancay, “Distributed reduced-state sarsa algorithm for dynamic channel
allocation in cellular networks featuring traffic mobility,” in IEEE International Conference
on Communications, 2005. ICC 2005. 2005, vol. 2, pp. 860–865, IEEE, 2005.
[8] G. Barlacchi, M. De Nadai, R. Larcher, A. Casella, C. Chitic, G. Torrisi, F. Antonelli,
A. Vespignani, A. Pentland, and B. Lepri, “A multi-source dataset of urban life in the city
of milan and the province of trentino,” Scientific data, vol. 2, no. 1, pp. 1–15, 2015.
[9] C.-W. Huang, C.-T. Chiang, and Q. Li, “A study of deep learning networks on mobile traffic
forecasting,” in 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and
Mobile Radio Communications (PIMRC), pp. 1–6, IEEE, 2017.
[10] C. Zhang and P. Patras, “Long-term mobile traffic forecasting using deep spatio-temporal
neural networks,” in Proceedings of the Eighteenth ACM International Symposium on Mobile
Ad Hoc Networking and Computing, pp. 231–240, 2018.
[11] G. K. Audhya, K. Sinha, S. C. Ghosh, and B. P. Sinha, “A survey on the channel assignment
problem in wireless networks,” Wireless Communications and Mobile Computing, vol. 11,
no. 5, pp. 583–609, 2011
[12] S.-H. Oh and D.-W. Tcha, “Prioritized channel assignment in a cellular radio network,”
IEEE transactions on communications, vol. 40, no. 7, pp. 1259–1269, 1992.
[13] R. Beck and H. Panzer, “Strategies for handover and dynamic channel allocation in microcellular mobile radio systems,” in IEEE 39th Vehicular Technology Conference, pp. 178–185,
IEEE, 1989.
[14] M. Zhang and T.-S. Yum, “Comparisons of channel-assignment strategies in cellular mobile
telephone systems,” IEEE Transactions on Vehicular Technology, vol. 38, no. 4, pp. 211–
215, 1989.
[15] D. Naboulsi, M. Fiore, S. Ribot, and R. Stanica, “Large-scale mobile traffic analysis: a
survey,” IEEE Communications Surveys & Tutorials, vol. 18, no. 1, pp. 124–161, 2015.
[16] F. Xu, Y. Lin, J. Huang, D. Wu, H. Shi, J. Song, and Y. Li, “Big data driven mobile traffic
understanding and forecasting: A time series approach,” IEEE transactions on services
computing, vol. 9, no. 5, pp. 796–805, 2016.
[17] H. Assem, B. Caglayan, T. S. Buda, and D. O’Sullivan, “St-dennetfus: A new deep learning approach for network demand prediction,” in Joint European Conference on Machine
Learning and Knowledge Discovery in Databases, pp. 222–237, Springer, 2018.
[18] H. D. Trinh, L. Giupponi, and P. Dini, “Mobile traffic prediction from raw data using lstm
networks,” in 2018 IEEE 29th Annual International Symposium on Personal, Indoor and
Mobile Radio Communications (PIMRC), pp. 1827–1832, IEEE, 2018.
[19] J. Hamilton, “Time series analysis. vol. 2 princeton university press,” Princeton, NJ, 1994.
[20] C. Zhang, H. Zhang, D. Yuan, and M. Zhang, “Citywide cellular traffic prediction based on
densely connected convolutional neural networks,” IEEE Communications Letters, vol. 22,
no. 8, pp. 1656–1659, 2018.
[21] G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE conference on computer vision and pattern
recognition, pp. 4700–4708, 2017.
[22] D. Zhang, B. Guo, and Z. Yu, “The emergence of social and community intelligence,”
Computer, vol. 44, no. 7, pp. 21–28, 2011.
[23] L. Wang, D. Zhang, Y. Wang, C. Chen, X. Han, and A. M’hamed, “Sparse mobile crowdsensing: challenges and opportunities,” IEEE Communications Magazine, vol. 54, no. 7,
pp. 161–167, 2016.
[24] C. Chen, D. Zhang, N. Li, and Z.-H. Zhou, “B-planner: Planning bidirectional night bus
routes using large-scale taxi gps traces,” IEEE Transactions on Intelligent Transportation
Systems, vol. 15, no. 4, pp. 1451–1465, 2014.
[25] A. Furno, D. Naboulsi, R. Stanica, and M. Fiore, “Mobile demand profiling for cellular
cognitive networking,” IEEE Transactions on Mobile Computing, vol. 16, no. 3, pp. 772–
786, 2016.
[26] B. Cici, M. Gjoka, A. Markopoulou, and C. T. Butts, “On the decomposition of cell phone
activity patterns and their connection with urban ecology,” in Proceedings of the 16th ACM
International Symposium on Mobile Ad Hoc Networking and Computing, pp. 317–326, 2015.
[27] L. Chen, J. Jakubowicz, D. Yang, D. Zhang, and G. Pan, “Fine-grained urban event detection and characterization based on tensor cofactorization,” IEEE Transactions on HumanMachine Systems, vol. 47, no. 3, pp. 380–391, 2016.
[28] V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, “Playing atari with deep reinforcement learning,” arXiv preprint arXiv:1312.5602,
2013.
[29] T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra,
“Continuous control with deep reinforcement learning,” arXiv preprint arXiv:1509.02971,
2015.
[30] V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and
K. Kavukcuoglu, “Asynchronous methods for deep reinforcement learning,” in International conference on machine learning, pp. 1928–1937, 2016.
[31] J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347, 2017.
[32] CellMapper, “Cellular tower and signal map.”

指導教授

吳曉光(Eric Hsiao-Kuang Wu)

審核日期

2020-7-29

推文