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作者(中文):林奕帆
作者(外文):Lin, Yi-Fan
論文名稱(中文):在5G異質網路中,基於模糊理論的換手方法
論文名稱(外文):Fuzzy-logic-based Handover Approach in 5G Heterogeneous Netwroks
指導教授(中文):蔡明哲
指導教授(外文):TSAI, MING-JER
口試委員(中文):郭建志
郭桐惟
口試委員(外文):Kuo, Jian-Jhih
Kuo, Tung-Wei
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊工程學系
學號:107062632
出版年(民國):109
畢業學年度:108
語文別:英文
論文頁數:22
中文關鍵詞:模糊理論換手
外文關鍵詞:Fuzzy logicHandover
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隨著快速發展和大量的服務產生(4K video, AR, VR),只有4G大基地台已經無法滿足網速的需求,因此,將會新建許多的小基地台 (微微小區,毫微微蜂巢式基地台, 毫米波 )搭配原本的大基地台在5G網路中。 然而,小基地台的小覆蓋範圍將會導致頻繁的換手,且大量的小基地台會因為彼此之間的干擾導致訊號變差,帶來了新的設計一個有效率的換手演算法挑戰在5G網路中。模糊邏輯可以透過人的專業知識來分析數據,來產生模糊集合以調整換手的參數。在這篇論文我們提出一種使用模糊邏輯的方法,發佈出來的演算法的主要目的是降低斷線的機率以及降低換手的次數在5G異質網路中。在我們的模擬環境下,數據顯示提出的演算法跟現有的方法相比有著良好的表現在斷線和換手的次數上。
With the rapid development of the tremendous increase in services (4K video, AR, VR), the 4G networks only consisting of big cells are unable to meet the data rate demands. Thus, numerous small cells (picocells, femtocells, mmWave cells) are established with original big cells glutted in 5G networks. However, the small coverage range of small cells introduces the frequent handover of user equipment (UE), and a large number of small cells worsen the signal received from a UE due to the interference between cells, which introduces the challenge of the design of an efficient handover algorithm in 5G networks. Fuzzy logic can take advantage of the human expert to analyze the data for the handover decision. In this thesis, a fuzzy logic-based handover algorithm is proposed. The goal is to minimize the radio link failure ratio while decreasing the number of handovers in 5G heterogeneous networks. Simulation results show that the proposed algorithm has good performance in the radio link failure ratio and the number of handover attempts, as compared to the state-of-the-art methods.
中文摘要 i
abstract ii
Contents iii
List of Figures v
List of Tables vi
1 Introduction and Related Works 1
1.1 Introduction 1
1.2 Related Works 3
2 Proposed Fuzzy Logic Algorithm 5
2.1 Traditional Handover Decision 5
2.2 Fuzzy Logic 6
2.3 Proposed Algorithm 8
3 Simulation 14
3.1 Simulation Setting and Model 14
3.2 Performance Matrics 16
3.3 Competitive Algorithm 17
3.4 Simulation Results and Evaluations 18
4 Conclusion 21
1] “A handover failure is declared when the criterion 2 is met in state 2.” 3GPP TR
36.839 (Release 11), 2012.
[2] K. D. C. Silva, Z. Becvar, and C. R. L. Frances, “Adaptive hysteresis margin
based on fuzzy logic for handover in mobile networks with dense small cells,”
IEEE Access, 2018.
[3] P.-C. Lin, L. F. G. Casanova, and B. K. Fatty, “Data-driven handover optimization in next generation mobile communication networks,” Mobile Information Systems, 2016.
[4] P. Mu˜noz, R. Barco, and I. de la Bandera, “Load balancing and handover joint
optimization in LTE networks using fuzzy logic and reinforcement learning,” Computer Networks, 2015.
[5] B. Shubyn, N. Lutsiv, O. Syrotynskyi, and R. Kolodii, “Deep learning based adaptive handover optimization for ultra-dense 5G mobile networks,” in 2020 IEEE
15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), 2020.
[6] J. Bai, S.-p. Yeh, F. Xue, and S. Talwar, “Route-aware handover enhancement for
drones in cellular networks,” in 2019 IEEE Global Communications Conference
(GLOBECOM), 2019.
 
 
 
 
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