基於叢集的自組織網路之行動負載均衡與能源節約管理的最佳化資源分配之研究

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研究生：	張鈞堯 Chun-Yao Chang
論文名稱：	基於叢集的自組織網路之行動負載均衡與能源節約管理的最佳化資源分配之研究 A Study of Optimal Resource Allocation in a Cluster-Based Self-Organizing Network using Mobility Load Balancing and Energy Saving Management
指導教授：	馬奕葳 Yi-Wei Ma
口試委員:	馬奕葳 Yi-Wei Ma 陳俊良 Jiann-Liang Chen 楊竹星 Chu-Sing Yang 黎碧煌 Bih-Hwang Lee
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：	2023
畢業學年度：	111
語文別：	英文
論文頁數：	86
中文關鍵詞：	異質網路、基於叢集的自組織網路架構、自我最佳化、行動負載均衡、能源節約管理
外文關鍵詞：	Heterogeneous networks, Cluster-Based Self-Organizing Network Architecture, Self-Optimization, Mobility Load Balancing, Energy Saving Management
相關次數：	點閱：202 下載：1
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隨著各式行動裝置應用的多元化展開，無線通訊的需求量快速增加，為了滿足這龐大的使用需求，下一世代通訊系統採用異質蜂巢網路，結合macro cell和small cell，以滿足高密度人口的通訊需求。然而，在這樣高密度的網路中，由於使用者的行動性與集中性，導致基地台的負載不均衡的問題十分嚴重，進而造成網路傳輸量低落，未能有效運用資源。過載的基地台還會限制新的使用者進入，影響網路資源使用率。此外，在高密度網路中，還存在能源方面的隱患，由於使用者對於網路的依賴性逐漸提高，當災害發生時可能導致基地台的供電中斷。這時候，基地台成為了重要的通訊設備，其產生的能耗會影響系統運行時間。若能源耗盡，可能造成使用者無法求援，安全人員無法有效進行溝通。
為了解決上述問題，本研究採用了自組織網路 (SON) 中的行動負載均衡 (MLB) 和能源節約管理 (ESM) 功能，提出一個基於叢集的MLB與ESM的最佳化資源分配系統，並分別設計了Intra-Cluster和Inter-Cluster的MLB和ESM的機制。提出的方法能夠藉由分配每個叢集內的資源，達到局部的最佳化，並透過跨叢集的資源調度，達到全局的最佳化。提出的系統會基於預測進行決策，將傳統的反應式設計轉變為主動式設計，能夠避免集中式SON所造成的時間差。模擬結果顯示，與原始場景相比，Cluster-Based MLB機制能夠降低網路負載標準差約24.1 ％，並提升11.1 %的平均網路傳輸量；Cluster-Based ESM機制能夠降低small cells的平均能耗約20.2 %，並延長40 %的平均運行時間，雖然會造成macro cell的能耗略為上升，但提升幅度是可接受的。綜合以上，提出的系統具備可用性，且能夠有效提升網路的效能與資源使用率。

The growing diversity of applications for mobile devices has led to a rapid increase in demand for wireless communications. Next-generation communication systems employ heterogeneous cellular networks to serve high-density populations. However, high-density networks face the challenge of severe cell load imbalance due to user mobility and concentration, resulting in reduced network throughput and underutilized resources. In addition, there are energy concerns in high-density networks, where cells are critical communications equipment susceptible to power outages during disasters. Energy depletion can prevent users from seeking help and emergency personnel from communicating. To address these challenges, this study utilizes Self-Organizing Networks (SON) functions, employing Mobility Load Balancing (MLB) and Energy Saving Management (ESM). A cluster-based MLB and ESM resource allocation system is proposed, with mechanisms for Intra-Cluster and Inter-Cluster MLB and ESM. The method achieves local optimization through resource allocation within each cluster and global optimization through cross-cluster resource scheduling. The system adopts proactive decision-making based on predictions, avoiding time discrepancies caused by centralized SON.
The simulation results show that compared with the original scenario, the cluster-based MLB mechanism reduces the network load standard deviation by approximately 24.1 % and increases the average network throughput by 11.1 %. The cluster-based ESM mechanism reduces the average energy consumption of small cells by about 20.2 % while increasing the average operation time by 40 %. Although there is a slight increase in the energy consumption of macro cells, it remains acceptable. Overall, the proposed system proves to be feasible and significantly improves network performance and resource utilization.

摘要    I
ABSTRACT    II
ACKNOWLEDGEMENT    III
LIST OF FIGURES    VII
LIST OF TABLES    IX
LIST OF NOTATIONS    X
Chapter 1    Introduction    1
1    Motivation    1
2    Contributions    4
3    Chapter Structure    4
Chapter 2    Background and Related Works    5
1    Background    5
1.1    Self-Organizing Network    5
1.2    Mobility Load Balancing    7
1.3    Energy Saving Management    8
1.4    Cluster    8
2    Related Work    9
2.1    Recent Research on Mobility Load Balancing    9
2.2    Recent Research on Energy Saving Management    14
Chapter 3    Proposed System Architecture    18
1    Infrastructure Layer    19
2    Service Layer    20
2.1    Monitor Module    20
2.2    Data Analysis Module    22
2.3    Data Predict Module    27
2.4    Load Configuration Management Module    30
2.5    Energy Configuration Management Module    33
3    Self-Organizing Network Control Layer    34
3.1    Mobility Load Balancing Module    35
3.2    Energy Saving Management Module    39
Chapter 4    Performance Analysis    46
1    Simulation Environments    46
2    Simulation Parameters    47
3    Performance Evaluation Metrics    48
3.1    Performance Evaluation Metrics for MLB    48
3.2    Performance Evaluation Metrics for ESM    49
4    Simulation Analysis and Verification    51
4.1    Intra-Cluster Mobility Load Balancing    51
4.2    Inter-Cluster Mobility Load Balancing    53
4.3    Cluster-Based Mobility Load Balancing    55
4.4    Intra-Cluster Energy Saving Management    57
4.5    Inter-Cluster Energy Saving Management    59
4.6    Cluster-Based Energy Saving Management    61
Chapter 5    Conclusions and Future Works    63
1    Conclusions    63
2    Future Works    63
References    64
                                

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