自組織網路函數小區間干擾協調與覆蓋範圍最佳化協調

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研究生：	蕭叡澤 Jui-Tse Hsiao
論文名稱：	自組織網路函數小區間干擾協調與覆蓋範圍最佳化協調 SON Function Coordination between Inter-cell Interference Coordination and Coverage and Capacity Optimization
指導教授：	黎碧煌 Bih-Hwang Lee
口試委員:	馬奕葳 Yi-Wei Ma 黃意婷 Yi-Ting Huang
學位類別：	碩士 Master
系所名稱：	電資學院 - 電機工程系 Department of Electrical Engineering
論文出版年：	2022
畢業學年度：	110
語文別：	英文
論文頁數：	43
中文關鍵詞：	自組織網路、自組織網路功能協調、決策樹、小區間干擾協調、覆蓋範圍最佳化、5G
外文關鍵詞：	5G, self-organizing network, elf-organizing network function coordination, decision tree, inter-cell interference coordination, coverage optimization
相關次數：	點閱：262 下載：2
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上一筆

隨著第五世代行動通訊網路的發展，越來越多的設備需要被提供服務。網路運營商在提供高品質服務的同時，又需要減少運營成本。然而，將行動通訊網路交由人工管理的效率較低且成本較高，因此，行動通訊網路的自動化管理是很重要的，而自組織網路被3GPP認可做為一個可行的解決方案。
自組織網路中不同的功能負責達成不同的目標，然而這些功能可能因為參數或邏輯的相互關聯影響，導致他們之間發生衝突，而使網路無法順利運行。為了處理不同功能衝突的問題，或是避免問題的發生，提供自組織網路一個協調方案是不可缺少的。
本文針對了兩個自組織網路的功能，ICIC(Inter-cell Interference Coordination)以及CCO(Coverage and Capacity Optimization)之間的參數衝突進行協調。對於ICIC而言，可能會想要降低傳輸功率，以減少干擾，而CCO可能會想增加傳輸功率，以改善覆蓋範圍，這會導致輸出參數衝突。
協調機制透過收集從使用者回報的資訊RSRP (Reference Signal Received Power)以及SINR (Signal to Interference plus Noise Ratio)交給決策樹，經由決策樹產生一個被認為合適的傳輸功率。再將此傳輸功率應用回系統中，觀察得到之結果。研究的結果顯示，在使用了調整之後的傳輸功率後，儘管提升的不多，但對於某些訊號處於非常差，即RSRP小於-100dbm的使用者，可以提升一個階段的訊號層級為差，即RSRP介於-90至-100dbm。

With the development of the fifth generation mobile communication network (5G), more and more devices require service. Network operators must reduce operating expenses (OPEX) while offering high-quality services. However, handing over networks to manual management is less efficient, and the cost is high. Therefore, the automatic management of the mobile communication network is essential, and a self-organizing network (SON) is recognized by the 3rd Generation Partnership Project (3GPP) as a feasible solution.
Different functions in the network are responsible for achieving different goals, but these functions may conflict with each other due to the interrelated effects of parameters or logic. The network does not typically work when there is a conflict. To deal with the problem of function conflict or to avoid the problem, it is indispensable to provide a coordinated scheme of the self-organizing network.
This paper aims to coordinate the parameter conflicts between the functions of two self-organizing networks, ICIC (Inter-cell Interference Coordination) and CCO (Coverage and Capacity Optimization). For ICIC, it may be desirable to reduce transmission power to reduce interference, while CCO may wish to increase transmission power to improve coverage, which results in conflicting output parameters.
In this paper, the information RSRP (Reference Signal Received Power) and SINR (Signal to Interference plus Noise Ratio) reported from the user are collected and sent to the decision regression tree. Then a suitable transmission power is generated through the decision regression tree, and the transmission power is applied. Then we go back to the system and observe the results obtained.
The study results show that for some users with impoverished signals with RSRP lower than -100dbm, after using the adjusted transmission power, the signal level can be improved by one stage to be poor, with RSRP between -90dbm and -100dbm, although the improvement is not much.

摘要    i
Abstract    ii
Acknowledgments    iv
Table of Contents    v
List of Abbreviations    vii
List of Notations    viii
List of Figures    ix
List of Table    x
Chapter 1     Introduction    1
1.1     Research Motivation    1
1.2     Organization of Thesis    2
Chapter 2     Background and Related Works    3
2.1     5G Network    3
2.2     Self-organizing Network    4
2.3     SON Solutions    5
2.3.1     Self-Configuration    5
2.3.2     Self-Optimization    6
2.3.3     Self-Healing    7
2.4     Decision Tree    7
2.5     Related Work    8
Chapter 3     Decision Tree Based SON Coordination Mechanism    10
3.1     Problem Description    10
3.2     System Environment    11
3.3     Decision Tree Based Coordination Mechanism    13
3.3.1     Collect RSRP and SINR from UEs Report    15
3.3.2     Generate TXP from Decision Tree    17
Chapter 4     System Simulations    19
4.1     Simulation Parameters    19
4.2     Simulation Results    20
4.2.1     Results of Fixed Position UEs    20
4.2.2     Results of Mobile UEs    22
4.3     Result Evaluation    24
Chapter 5     Conclusion and Future Work    27
References    28















                                

[1] 3GPP TS 32.500 “Telecommunication Management; Self-Organizing Networks (SON); Concepts and Requirements,” V11.1.0, 2011.
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[7] ITU, "IMT Vision-Framework and Overall Objectives of the Future Development of IMT for 2020 and Beyond," ITU-R M.2083-0, 2015.
[8] 3GPP TS 32. 501, "Telecommunication Management, Self-Organizing Networks (SON), Concepts and requirements (Release 11)," v.11.1.0, 2011.
[9] Hindawi Publishing Corporation, "Benefits of Self-Organizing Networks (SON) for Mobile Operators" Journal of Computer Networks and Communications" Volume 2012, Article ID 862527, 16 pages.
[10] 3GPP.org [online]. Available: https://www.3gpp.org/technologies/keywords-acronyms/105-son.
[11] C. V. Murudkar and R. D. Gitlin, "QoE-driven Anomaly Detection in Self-Organizing Mobile Networks using Machine Learning," 2019 Wireless Telecommunications Symposium (WTS), pp. 1-5, 2019.
[12] T. Bag, S. Garg, D. F. P. Rojas, and A. Mitschele-Thiel, "Machine Learning-Based Recommender Systems to Achieve Self-Coordination Between SON Functions," in IEEE Transactions on Network and Service Management, vol. 17, no. 4, pp. 2131-2144, 2020.
[13] X. Gelabert, B. Sayrac and S. Ben Jemaa, "A Heuristic Coordination Framework for Self-Optimizing Mechanisms in LTE HetNets," in IEEE Transactions on Vehicular Technology, vol. 63, no. 3, pp. 1320-1334, 2014.
[14] K. Tsagkaris, N. Koutsouris, P. Demestichas, R. Combes and Z. Altman, "SON Coordination in a Unified Management Framework," 2013 IEEE 77th Vehicular Technology Conference (VTC Spring), pp. 1-5, 2013.
[15] S. Parameswaran, T. Bag, S. Garg and A. Mitschele-Thiel, "Cognitive Network Function for Mobility Robustness Optimization in Cellular Networks," 2022 IEEE Wireless Communications and Networking Conference (WCNC), pp. 2035-2040, 2022.
[16] Ahamed, Md Maruf, and Saleh Faruque. “5G Network Coverage Planning and Analysis of the Deployment Challenges,” Sensors (Basel, Switzerland) vol. 21, 19 6608, 2021.
[17] [IMT-2020.EVAL], “Guidelines for Evaluation of Radio Interface Technologies for IMT-2020. 2017,” Available online: https: //www.itu.int/md/R15-SG05-C-0057. Accessed on: September 1, 2022.
[18] 3GPP R4-092042, " Simulation assumptions and parameters for FDD HeNB RF requirements) " , 2009.
[19] Byung-Jin Lee, Ju-Phil Cho, In-Ho Ra, Kyung-Seok Kim, "Propagation Characterization Based on Geographic Location Variation for 5G Small Cells", Mobile Information Systems, vol. 2017, 8 pages, 2017.
[20] 3GPP TS 37. 320, “Technical Specification Group Radio Access Network; Radio measurement collection for Minimization of Drive Tests (MDT); Overall description,” V17.1.0, 2011.
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[22] 3GPP TS 36.331, ”3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA); Radio Resource Control (RRC); Protocol specification,” V17.1.0, 2022.
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