| 摘要(英) |
This thesis investigates the deployment of 28GHz millimeter wave (mmWave) technology, suitable for indoor coverage in 5G mobile communication networks. The research employs two different intelligent algorithms—Fuzzy C-Means (FCM) clustering and Particle Swarm Optimization (PSO)—to rapidly identify the optimal locations for base stations in large indoor spaces.
Before deploying a 5G mobile communication base station, site surveys are necessary to assess the environment, including coverage areas, obstacles, and spatial dimensions. Hence, the choice of base station location is crucial. Millimeter waves, characterized by short wavelengths, high path loss, and low penetration, are more suited for indoor environments compared to outdoor spaces. These properties also enable 5G networks to achieve higher bandwidth, lower latency, and enhanced connectivity.
This research utilizes the powerful simulation software Matlab to design various indoor scenarios and generate random user coordinates. Fuzzy C-Means is used to identify the centroids of user clusters, which serve as potential deployment points for mmWave base stations. FCM is frequently applied in communication network scenarios to deploy nodes by clustering user populations around optimal centroids. PSO, another widely used intelligent algorithm, is common in sensor deployment applications. It guides particles in a population towards better regions based on their adaptability to the environment, efficiently searching through large solution spaces to find optimal base station locations.
The goal of this study is to evaluate the impact of these algorithms on key performance indicators of wireless communication networks, including user throughput, Signal to Interference plus Noise Ratio (SINR), and overall coverage area. By simulating mmWave base station deployments in Matlab, the performance of FCM and PSO under identical scenario designs is compared. Additionally, the study contrasts these algorithmic results with the traditional deployment strategies used by engineers to identify their advantages and disadvantages, helping to optimize base station layouts quickly and enhance the overall performance and reliability of mobile networks.
Due to the high frequency of mmWave communication, several factors, including physical obstacles, diffraction, poor penetration capabilities, and reduced coverage area compared to lower frequency bands, must be considered along with the mobility of end users. Currently, telecommunications operators focus on deploying mmWave networks in large indoor spaces, aiming to maximize coverage and user density. Indoor environmental factors, such as partition materials, decor, and electrical equipment, can also interfere with signal quality. By comparing FCM and PSO, this study assists engineers in quickly identifying better mmWave base station locations based on existing building dimensions and user density. The focus is on optimizing signal quality and strength at deployment points to ensure maximum coverage, network consistency, and stability, thereby improving the overall performance and reliability of mobile networks for users. |
| 論文目次 |
論文摘要 i
Abstract iii
致 謝 vi
目 錄 vii
圖 目 錄 xi
表 目 錄 xiv
第一章 緒 論 1
1-1 研究背景 1
1-2 研究動機與目的 2
1.3 論文架構 4
第二章 相關文獻探討與背景知識 5
2-1 5G毫米波行動通訊技術簡介 5
2-1-1 毫米波通訊技術展現的優點 8
1. 更大頻寬 8
2. 1毫秒更低延遲 8
3. 更高容量用於高密度區域 9
4. 帶動創新應用的發展 9
5. 固定無線接取（FWA） 10
6. 增強型行動寬頻（eMBB） 10
7. 進化的波束成形和巨量天線(Massive MIMO) 10
8. 高密度物聯網支援 11
2-1-2毫米波挑戰與限制 11
2-2 毫米波頻段在室內性能與無線電規範探討 13
2-2-1 室內涵蓋性能 13
1.短波長與指向通訊 13
2.(MIMO) 與波束成形技術 14
3.視線距離（Line-of-Sight） 15
4.傳播與衰減 18
5.多路徑效應 19
6.有限範圍的覆蓋 19
7.傳播遭遇障礙物的高敏感性 20
8.干擾與噪聲 20
9.佈署場景 21
2-2-2 QAM訊號調變 21
2-2-3 SINR, CQI, MCS 25
2-3 室內基站佈署優化評估 29
2-4 人工智慧演算法在基站佈署中的應用 32
1.基站位置點部署遭遇的挑戰： 33
2.人工智慧演算法的應用： 34
第三章 研究方法 40
3-1基站規劃及架設基本方法 40
3-2 Fuzzy C-Means模糊聚類演算法 41
3-3 群體智慧(PSO)粒子群演算法 44
3-4 模擬場景方法與參數設定 48
模擬場景一 , 49
250mX150m大型室內演唱會(情境一) 49
250mX150m大型室內演唱會(情境二) 52
250mX150m大型室內演唱會(情境三) 55
模擬場景二 , 58
超大型購物中心(情境一) 58
超大型購物中心(情境二) 61
超大型購物中心(情境三) 64
模擬場景三 , 67
300mX300m超大型綜合體育館(情境一) 67
300mX300m超大型綜合體育館(情境二) 70
300mX300m超大型綜合體育館(情境三) 73
第四章 結果與討論 77
4-1方法的結果比較 77
4-2演算法對基站佈署和用戶吞吐量的分析 79
第五章 結論與未來展望 81
參考文獻 82 |
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至系統瀏覽論文 (2028-1-1以後開放)
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