臺灣博碩士論文加值系統

近年來，隨著行動通訊技術邁向第五代行動通訊(5G)，標準雖仍持續制訂中，但在提升網路頻譜的發展上，3GPP Release 10已提出異質網路架構的應用，藉由佈署不同發送功率大小的基地台，藉此提高頻譜在空間中的使用效率。然而，在異質網路的發展上也面臨著許多挑戰，包含服務基地台選擇、無線資源配置、基地台間干擾協調技術等。為了提升小型基地台運行效率，3GPP Release 12提出一個雙重連線技術，允許一台用戶裝置同時連線兩個不同的基地台，增加了無線資源利用的彈性。另一方面，用戶裝置在不同的服務和乘載上，會有各自不同的頻寬需求，在雙重連線技術下，如何整合跨基地台間的頻寬資源來提升用戶裝置的資料傳輸速率，利用無線資源配置來達到服務品質(QoS)要求將會是一項新的議題。
有鑑於此，本論文研究在同頻雙重連線網路中，基於無線資源管理議題，提出有助於提升服務品質的方法。在無線資源排程方面，我們以最佳化為原則設計Max-min Threshold Scheduler (MTS)，考慮整合跨基地台間的頻寬資源，並將每個無線資源區塊(Resource Block)分配給通道品質最好的用戶裝置。為了避免資源分配不均，在基地台選擇機制方面，我們設計HeNB Congestion Indicator (HCI)達到基地台間負載平衡，除了考慮用戶裝置連線品質之外，也評估基地台本身剩餘多少資源。再者，在干擾協調機制方面，結合小型基地台範圍擴展(CRE)和近乎空白子訊框(ABS)，我們設計Q-learning方法依環境條件動態調整ABS比率，避免不必要的無線資源浪費。
模擬結果顯示在服務品質滿足率上，我們的MTS排程相較Proportional Fairness排程提升了33.31%；我們的HCI基地台決選擇機制相較SINR基地台選擇機制提升了4.17%；我們的Q-learning干擾協調機制相較Static ABS則是提升了2.18%；最後，我們結合雙重連線、CRE、ABS的應用，提升鄰近基地台訊號交接處的用戶裝置5.34%滿足率。

In recent years, with the mobile communication technology towards 5G, the standards of mobile communication continue to evolve. In the Release 10 of 3GPP, the heterogeneous network (HetNet) architecture was proposed to increase the spectrum efficiency by deploying base stations with different transmission power. However, the development of HetNet still facing many challenges, including cell selection, resource allocation, inter-cell interference coordination and so on. In order to improve the efficiency of the small cells, 3GPP Release 12 proposed a Dual Connectivity technology that allows one user equipment to connect two different base stations at the same time, to increase the flexibility of resource utilization. On the other hand, different user equipment tends to have different bandwidth requirements. In Dual Connectivity, a challenging issue is how to integrate resources from two stations to enhance the quality of service (QoS) as well as data transfer rate of each user equipment.
In this thesis, we proposed novel resource management mechanisms to improve the quality of service of user equipment in the co-channel dual connectivity network. In terms of resource allocation, we designed the Max-min Threshold Scheduler (MTS) which, in principle, allocates a resource block to the user equipment with the best channel quality while considering the issues of inter-cell resource allocation and the QoS requirement of each user equipment. In order to balance the load of different cells, we designed a novel cell selection scheme based on the HeNB Congestion Indicator (HCI) which considers not only the signal quality of user equipment but also the remaining resources of each base station. To improve the QoS of cell edge user equipment, Cell Range Expansion (CRE) and the Almost Blank Subframe (ABS) were proposed in 3GPP. In this thesis, based on Q-learning, we designed an adaptive mechanism which dynamically adjusts the ABS ratio according to the environmental conditions to improve resource utilization.
The ultimate goal of the proposed mechanisms is to improve the quality of service satisfaction rate of user equipment. Our simulation results showed that our MTS scheduler is able to achieve 31.44% higher rate than the Proportional Fairness scheduler; our HCI cell selection scheme yields 2.98% higher rate than the SINR cell selection scheme; the QoS satisfaction rate of our Q-learning dynamic ABS scheme is 4.06% higher than that of the Static ABS scheme. Finally, by integrating the mechanisms of Dual Connectivity, CRE and ABS, the QoS satisfaction rate of cell edge user equipment can be increased by 10.76% as compared to the traditional approach.

緒論
1.1 研究背景
1.2 研究動機及目的
1.3 研究貢獻
1.4 論文架構
第二章 相關文獻
2.1 雙重連線
2.2 基地台選擇與干擾協調機制
2.3 無線資源排程
2.4 流量模組
第三章 系統架構與設計
3.1 異質網路頻譜配置
3.2 網路環境
3.3 無線資源配置
3.3.1 訊框結構
3.3.2 資源排程
3.4 服務品質
第四章 研究方法
4.1 Max-min Threshold Scheduler (MTS)資源排程機制
4.1.1 Max-min排程
4.1.2 Threshold：資源優先權及限制
4.1.3 MTS流程
4.2 HeNB Congestion Indicator (HCI)基地台選擇機制
4.2.1 HeNB Congestion Indicator基地台選擇
4.2.2 分散式決策時機
4.2.3 HCI流程
4.3 Q-learning dynamic ABS 干擾協調機制
4.3.1 強化學習(Reinforcement Learning)：Q-learning
4.3.2 Q-learning dynamic ABS
第五章 模擬分析
5.1 環境參數
5.2 模擬方案與數據分析
5.2.1 驗證HCI服務基地台選擇
5.2.2 驗證Max-min結合Threshold機制
5.2.3 驗證PF無線資源排程
5.2.4 驗證Q-learning dynamic ABS
5.2.5 驗證雙重連線結合CRE/ABS應用
第六章 結論與未來展望
第七章 參考文獻

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