臺灣博碩士論文加值系統

在Cognitive Radio (CR)網路中，各次級用戶secondary users(SU)使用的通道是動態且多變的，因此需要彈性的方法讓SU彼此相連。本篇論文參考既有的分散式網路建立架構方法，提出兩種在CR中考慮連線機率的SU架構群組方法。一種名為probability-based(PB)，藉由精確算出各節點的連線期望值，來決定節點的優先權。另一種方法則名叫approximated probability-based(APB)，在考慮裝置的計算限制與實體天線的限制下，概略計算出傳送的機率。此兩種方法藉由計算primary users(PU)出現的機率訊息，讓斷線機率最小的SU來形成clusterhead，藉此提高cluster的連線能力。我們也進行模擬來比較所提出方法與傳統方法的效能。模擬結果顯示，在各種不同條件下(如: SU密度、PU密度、傳輸範圍、通道數量等條件的變化)，PB與APB的連線機率一直比傳統的ID-based方法的效能好上很多。模擬結果證明，我們提出的兩種方法能考量不同節點的連線機率，用更有效率的方法選擇群組頭，提高網路的效能。

In cognitive radio (CR) networks, available channels of each secondary user (SU) is dynamic and time-variant, thus a flexible approach which lets SUs connect with each other is needed. In this paper, we base on an existing framework for ad hoc networks, and propose two types of cluster-constructing methods which consider connect probability. The first method called probability-based (PB), by accurately calculating each node''s expected connectivity, and it determines each node''s priority of becoming clusterhead (CH). The other method call approximated probability-based (APB), which considers the limitation of physical antennas, and calculates the approximated connectivity. We also perform simulations to compare the performance between the proposed methods and the tradition one. The simulation results show that, in different conditions (ex. SU density, PU density, transmission range, channel number etc.,) the performances of PB and APB are always better than the traditional one, which indicates that the two proposed methods can enhance network performance by considering the connectivity.

書名頁 i
論文口試委員審定書 ii
授權書 iii
中文摘要 iv
英文摘要 v
誌謝 vi
目錄 vii
表目錄 viii
圖目錄 ix
第一章、緒 論 1
1.1 Cognitive Radio Network 2
1.2 CH的選取與建立 3
1.3 Related Works 4
第二章、提出的方法 10
2.1 系統模型system modeling 10
2.2 Probability-based(PB) 14
2.3 Approximation Probability-based(APB) 18
第三章、模擬結果 21
3.1 Simulation Settings 21
3.2 模擬一－SU數量與PU數量 22
3.3 模擬二－不同通道數量 24
3.4 模擬三－不同傳輸範圍 26
3.5 模擬四－可用通道數與效能的關係 28
3.6 模擬五－可能通訊範圍與效能的關係 30
3.7 Simulation conclusions 32
第四章、結 論 33
參 考 文 獻 34

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