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???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
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dc.contributor.advisor | 周俊廷(Chun-Ting Chou) | |
dc.contributor.author | Wen-Hao Wu | en |
dc.contributor.author | 吳文豪 | zh_TW |
dc.date.accessioned | 2021-05-20T00:48:51Z | - |
dc.date.available | 2020-10-27 | |
dc.date.available | 2021-05-20T00:48:51Z | - |
dc.date.copyright | 2020-10-27 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-10-21 | |
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Wang, 'On the optimal small cell deployment for energy-efficient heterogeneous cellular networks,' 2014 Sixth International Conference on Ubiquitous and Future Networks (ICUFN), Shanghai, 2014, pp. 172-175 W. Chen, H. Li, Z. Li, Z. Xiao and D. Wang, 'Optimization of small cell deployment in heterogeneous wireless networks,' 2016 International Conference on Computer, Information and Telecommunication Systems (CITS), Kunming, 2016, pp. 1-5 Holger Claussen; David Lopez-Perez; Lester Ho; Rouzbeh Razavi; Stepan Kucera, 'Optimization of Small Cell Deployment,' in Small Cell Networks: Deployment, Management, and Optimization, IEEE, 2018, pp.443-465 D. Pliatsios, P. Sarigiannidis, I. D. Moscholios and A. Tsiakalos, 'Cost-efficient Remote Radio Head Deployment in 5G Networks Under Minimum Capacity Requirements,' 2019 Panhellenic Conference on Electronics Telecommunications (PACET), Volos, Greece, 2019, pp. 1-4 G. Prasad, D. Mishra and A. Hossain, 'Coverage-constrained base station deployment and power allocation for operational cost minimization,' 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Montreal, QC, 2017, pp. 1-5 Y. Yoon and Y. Kim, 'An Efficient Genetic Algorithm for Maximum Coverage Deployment in Wireless Sensor Networks,' in IEEE Transactions on Cybernetics, vol. 43, no. 5, pp. 1473-1483, Oct. 2013 C. Dong, J. Xie, H. Dai, Q. Wu, Z. Qin and Z. Feng, 'Optimal deployment density for maximum coverage of drone small cells,' in China Communications, vol. 15, no. 5, pp. 25-40, May 2018 O. Zorlu and O. K. Sahingoz, 'Increasing the coverage of homogeneous wireless sensor network by genetic algorithm-based deployment,' 2016 Sixth International Conference on Digital Information and Communication Technology and its Applications (DICTAP), Konya, 2016, pp. 109-114 G. Yu and K. 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Kalayci and A. Uğur, “Genetic Algorithm-Based Sensor Deployment with Area Priority,” Cybernetics and Systems, Vol. 42, No. 8, 2011, pp. 605-620 Y. Lv, H. Zhang and S. Xueming, 'Analysis of base stations deployment on power saving for heterogeneous network,' 2017 IEEE 17th International Conference on Communication Technology (ICCT), Chengdu, 2017, pp. 1439-1444 Harri Holma; Antti Toskala; Jussi Reunanen, 'Learnings from Small Cell Deployments,' in LTE Small Cell Optimization: 3GPP Evolution to Release 13 , Wiley, 2015, pp.145-158 M. Matinmikko-Blue, S. Yrjoelae and M. Latva-aho, 'Micro Operators for Ultra-Dense Network Deployment with Network Slicing and Spectrum Micro Licensing,' 2018 IEEE 87th Vehicular Technology Conference (VTC Spring), Porto, 2018, pp. 1-6 J. F. Valenzuela-Valdés, Á. Palomares, J. C. González-Macías, A. Valenzuela-Valdés, P. Padilla and F. Luna-Valero, 'On the Ultra-Dense Small Cell Deployment for 5G Networks,' 2018 IEEE 5G World Forum (5GWF), Silicon Valley, CA, 2018, pp. 369-372 A. Dong, X. Luo and Q. Du, 'A small cell deployment strategy towards amorphous coverage in the cellular network,' 2015 International Wireless Communications and Mobile Computing Conference (IWCMC), Dubrovnik, 2015, pp. 745-750 International Communication Union, “Propagation data and prediction methods for the planning of short-range outdoor radiocommunication systems and radio local area networks in the frequency range 300 MHz to 100 GHz”, 2019 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/8092 | - |
dc.description.abstract | 在現今的網路中,對於高傳輸率以及低延遲的需求不斷增加,為了滿足這些需求,第五代行動通訊(5G)使用3到100 GHz的頻帶進行傳輸。更大的頻寬解決了高傳輸率以及低延遲的需求,但是執照頻譜的花費卻過於昂貴,要像一般的電信營運商(MNO)一樣提供服務是非常困難的,因此微營運商成為減少這類資本支出(CAPEX)的一項方法。微營運商不僅使用免費的免執照頻譜,其佈建的小基站成本也較低,同時小基站也可以彌補一般營運商在5G網路中涵蓋率不足的問題,所以微營運商在網路市場中變得越來越重要。 在本篇論文中,我們的目標是最大化微營運商在佈建小基站時的收益。雖然有很多相關資料在研究小基站的佈建,但大部分研究都只著重於一般營運商上。一般的營運商只提供大眾化服務,而微營運商卻能夠提供兩種不同服務,分別是針對特定地區的客製服務(site-specific service)以及中立主機服務 (neutral host service),一般來說針對特定區域的客製服務為微營運商之主要任務,而中立主機服務則較為次要。因此微營運商在小基站佈建中必須優先涵蓋到所有特定區域的用戶,以提供完整的客製服務,造成微營運商與一般營運商佈建基地台的方式有所不同。 為了最大化微營運商的佈建收益,我們採用了一般電信營運商的佈建方法。在小基站的佈建上雖然有很多種不同的方法能夠使用,但每個方法所適用的用戶分布不盡相同,根據本篇論文中的用戶分布,最後採用了基因演算法來進行佈建。在模擬當中,我們分別在三種不同利潤的情況中比較基因演算法與一般的貪婪演算法的收益,實驗結果顯示,基因演算法的表現在這三種情況中都比貪婪演算法好,根據不同的情境,收益的差距在4%到16%間。 | zh_TW |
dc.description.abstract | In current networks, the demands of data rates and low latency are increasing. To meet the requirements, the Fifth Generation (5G) mobile network turns to use a high frequency between 3 and 100GHz. Although larger bandwidth can solve the requirements of high-speed data rates and low latency, the investment of the licensed band is very huge. It is hard for a service provider to provide service as a mobile network operator (MNO). To reduce the capital expenditure (CAPEX), micro operators become an attractive solution. They use free unlicensed bands. The cost of the small cells they deployed is relatively low and the small cells can also complement the coverage problem in 5G networks. Hence, micro operators become more and more important in the market. In the thesis, we solve the cell deployment of micro operators for profit maximization. The small cell deployment has been discussed a lot. Most of the related work focus on the deployment of conventional MNOs, not micro operators. Different from the existing solutions, we deploy small cells from micro operator’s perspective. MNOs only provide general services. However, micro operators can provide two different services. including site-specific services and neutral host services. In general, the site-specific service is primary, and the neutral host service is secondary. Therefore, the priority of micro operators is to cover all site-specific users to provide complete site-specific services. Due to the reason, the deployment of micro operators is different from MNOs. In order to maximize the profit of micro operators, we adopt and modify the approaches in the deployment of MNOs. There are many approaches to deploy small cells, and different approaches fit different user distribution. According to the user distribution in the thesis, we adopt the genetic algorithm to solve the problem. We compare the performance in terms of profit with the general greedy algorithm in three scenarios. The genetic algorithm outperforms the greedy algorithm in all scenarios. The performance of the genetic algorithm is better than the greedy algorithm by 4% to 16% depending on the scenarios. | en |
dc.description.provenance | Made available in DSpace on 2021-05-20T00:48:51Z (GMT). No. of bitstreams: 1 U0001-2110202010555000.pdf: 3332778 bytes, checksum: 00dce1a7b03bd2598f382ad1335edba8 (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 誌謝 i 中文摘要 ii ABSTRACT iii LIST OF TABLES vii LIST OF FIGURES viii Chapter 1 INTRODUCTION 1 1.1 Difference between MNOs and Micro Operators 1 1.2 Two Distinct Services for Micro Operators 2 1.3 Problem Statement 2 1.4 Related Work 3 1.5 Contributions of the Thesis 6 1.6 Thesis Organization 6 Chapter 2 SYSTEM SETTINGS AND ASSUMPTIONS 7 2.1 User Distribution 7 2.2 Propagation Model 8 2.3 Signal to Interference Plus Noise Ratio (SINR) 9 2.4 Dynamic Power Allocation of Small Cells 10 2.5 Sector Splitting 12 2.6 Assumptions of Small Cells and Users 13 Chapter 3 PROPOSED SOLUTION 17 3.1 The First Stage of the Genetic Algorithm 17 3.1.1 Parameter Setting (First Stage) 18 3.1.2 Solution Initialization (First Stage) 20 3.1.3 Fitness Evaluation (First Stage) 23 3.1.4 Offspring Generation (First Stage) 25 3.2 The Second Stage of the Genetic Algorithm 32 3.2.1 Parameter Setting (Second Stage) 32 3.2.2 Solution Initialization (Second Stage) 34 3.2.3 Fitness Evaluation (Second Stage) 34 3.2.4 Offspring Generation (Second Stage) 36 Chapter 4 PERFORMANCE EVALUATION 37 4.1 Three Scenarios 37 4.2 Simulation Results of the Three Scenarios 38 Chapter 5 CONCLUSIONS 45 REFERENCE 46 | |
dc.language.iso | en | |
dc.title | 微營運商在5G網路中的小基站佈建最佳化 | zh_TW |
dc.title | Cell Deployment Optimization for Micro Operators in 5G Networks | en |
dc.type | Thesis | |
dc.date.schoolyear | 109-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 張時中(Shi-Chung Chang),謝宏昀(Hung-Yun Hsieh),蔡志宏(Zsehong Tsai) | |
dc.subject.keyword | 微營運商,小基站佈建,收益最佳化, | zh_TW |
dc.subject.keyword | micro operator,cell deployment,profit optimization, | en |
dc.relation.page | 49 | |
dc.identifier.doi | 10.6342/NTU202004298 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2020-10-21 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
Appears in Collections: | 電信工程學研究所 |
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U0001-2110202010555000.pdf | 3.25 MB | Adobe PDF | View/Open |
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