第五代行動網路中移動性負載平衡和節能管理的自協調方案

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研究生：	林育輝 Yu-Hui Lin
論文名稱：	第五代行動網路中移動性負載平衡和節能管理的自協調方案 A Self-Coordination method between Mobility Load Balance and Energy Saving Management in 5G Networks
指導教授：	黎碧煌 Bi-Huang Li
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：	2023
畢業學年度：	111
語文別：	英文
論文頁數：	59
中文關鍵詞：	自組織網路功能、負載平衡、能源效率、小區縮放、Q學習
外文關鍵詞：	SON Functions, Load Balancing, Energy Efficiency, Cell Scaling, Q learning
相關次數：	點閱：322 下載：1
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隨著第五代行動通訊的演進，作為基地台設備建立基礎的異構蜂窩網路(Heterogeneous Networks; HetNets)設置變得更加密集。為了尋找可持續的、具有成本效益的自主管理維護方案，使用自組織網路(Self-Organizing Network; SON)功能被設想為最佳解決方案之一。在以往對於小型基站的討論上，相關研究中提出以小區休眠和小區縮放等方法提高異構網路的能源效率。
在以往的研究中，為了最佳化能源效率而對小區進行調整將會牽涉到移動性負載平衡(Mobility Load Balance; MLB)和節能管理(Energy Saving Management; ESM)這兩種自組織網路功能。然而，這兩項功能往往會因為對各自目標的盲目看法，在調整覆蓋範圍上彼此衝突，導致過度調整或是調整效果不彰，因此，需要一個基於兩種功能上協調且互補的方案。
本論文針對上述兩項自組織網路功能在調整小區覆蓋範圍的設置上提出一個協調方案。通過小型基站(Small Base Station; SBS)統計並分析覆蓋範圍內使用的負載流量，使用Q-learning計算隨時間調整的平均負載和負載的浮動值，將兩者結合作為SBS上兩個自組織網路功能在範圍調整上的協調依據。這些模擬結果表明，所提出的機制在高密度使用設備的環境下能在確保小型基站有效分擔大型基站(Macro Base Station; MBS)負載流量並降低網路總功耗，以及減少小型基站邊緣UE被封包遺失的問題。

With the evolution of the fifth-generation mobile communications, the settings of heterogeneous cellular networks (HetNets), which are the foundation of base station equipment, become denser. To find a sustainable, cost-effective self-management maintenance solution, the use of Self-Organizing Network (SON) functions is conceived as one of the best solutions. In previous discussions on small base stations, related studies proposed methods such as cell sleeping and cell zooming to improve the energy efficiency of HetNets.
Among previous studies, cell adjustment for optimal energy efficiency will involve two functions of SON, Mobility Load Balance (MLB) and Energy Saving Management (ESM). However, these two functions often conflict with each other in terms of adjustment coverage due to blind views of their respective goals, resulting in over-adjustment or ineffective adjustment. Therefore, a solution based on the coordination and complementarity of the two functions is needed.
This thesis proposes a coordination scheme for adjusting the cell coverage setting for the above two self-organizing network functions. The coordination of the coverage adjustment of the two SON functions on the Small Base Station (SBS) is according to the combination of the traffic load in the coverage collected by the Base Station (BS) and the time-adjusting duration average and floating value of the load calculating by applying Q-learning. These simulation results show that the proposed mechanism can reduce the total power consumption of the network while ensuring that the SBS can effectively share the load traffic of the Macro Base Station (MBS) in a high-density environment. Moreover, the proposed mechanism can also reduce UE packet loss at the edge of the SBS.

摘要    i
Abstract    ii
Acknowledgment    iv
Table of Contents    v
List of Abbreviations    vii
List of Notations    ix
List of Figures    x
List of Tables    xii
Chapter 1 Introduction    1
1 Research Motivation    1
2 Contributions    3
3 Organization of Thesis    4
Chapter 2 Background and Related Works    5
1 Cellular Network Overview    5
1.1 5G New Radio    5
1.2 Specification Introduction    6
1.3 Communication Model    9
2 Signal Attenuation and Signal Interference    10
2.1 Path Loss    10
2.2 Signal to Interference and Noise Ratio    11
2.3 Shannon's Theorem    11
3 OAM Overview    11
3.1 Self-Organizing Network (SON)    12
3.2 SON Function    13
4 Machine Learning (ML) Overview    14
4.1 Reinforcement Learning (RL)    15
4.2 Q-Learning    15
5 Related Research    17
5.1 SFs Coordination    17
5.2 Cell Sleeping and Cell Zooming    19
6 Problem Description    21
Chapter 3 FLBCS Method    23
1 Research Method    23
2 Initial Situation Setting    23
3 Communication Model    25
4 FLBCS Mechanism    27
4.1 Target Load Calculate    28
4.2 Cell Zooming Mechanism    30
4.3 System Flow Chart    31
Chapter 4 System Simulation    32
1 Simulation Environment and Parameter    32
1.1 Assumptions of Simulation    34
2 Analysis and Comparison of Simulation Results    34
2.1 System Power    34
2.2 System Throughput    36
2.3 Variation of SBS Load Under High Load Scenarios Using FLBCS Scheme and Conventional SFs Decision    38
Chapter 5 Conclusions and Future Work    40


                                

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