臺灣博碩士論文加值系統

在行動運算的時代，行動裝置能夠處理大量的資料，例如: 高畫質影片、瀏覽網頁、下載軟件，無線通訊是必備的裝置，人們可以使用智慧型手機，及時抓取資料，只要在網路沒有塞車的情況下，無線網路應該可以給予良好的服務品質，但是網路狀況並不如我們所願，且大多的時候，網路的頻寬不足我們使用，因此在需求較多的時候，使用上可以明顯感覺到較差的使用者體驗，因此必須有一個機制，能夠給予較好的觀看品質。

在本篇論文中提出視訊串流之封包控制設計，假設在網路流量大的情況下，發生佇列沒有空間配置封包，就會有佇列封包溢滿情況，造成封包遺失。Tail Drop演算法，碰到佇列溢滿是不配置封包到佇列，所以Tail Drop演算法的佇列管理是非常不智。根據IEEE 802.11e 協定下的TXOP機制，視訊串流之封包控制器設計記錄成功傳送封包的資訊，根據此資訊決定配置那些封包給佇列。由於我們所提出的演算法比Tail Drop演算法低了15.2%封包遺失率，所以視訊串流之封包控制設計可以提供更好的使用者體驗。

Currently, mobile devices can process large amounts of data for watching high-definition video, browsing the Web, and downloading software. Wireless communication devices have become necessary because using mobiles such as smartphones enable people to retrieve data anywhere. Such a high level of convenience is accessible when quality of service is excellent, particularly when wireless network traffic is light. However, for several reasons the actual network traffic will be really heavy. Therefore, designing a mechanism that improves the efficiency of queue management is crucial for enhancing quality of service.

This thesis proposes a mechanism for queue management in IEEE 802.11e enhanced distributed channel access (EDCA). When a queue has no space for allocating packets, overflow occurs. By designing Tail Drop, the packets that have recently arrived (i.e., the overflow) are discarded until a queue has sufficient room for allocation. According to the EDCA protocol, the transmission opportunity (TXOP) is the finite time during which a machine may send packets. Queue management systems record information regarding successful transmissions. Based on previously collected information, the proposed queue management system decides whether the packets to be assigned to the corresponding transmission queue or not, according to the number of retransmissions. Our experiment results show that this system can efficiently reduce the average number of the discarded packets by 11.6%.

致 謝 I
摘 要 II
ABSTRACT III
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 論文架構 3
第二章 文獻探討 4
2.1 無線網路硬體架構 4
2.2 資料連接層 6
2.3 EDCA 8
2.4 CSMA/CA碰撞方法 12
2.5 TXOP 17
2.6 TAIL DROP 19
2.7 RANDOM EARLY DETECTION 20
2.8 LEAST ATTAINED SERVICE SCHEDULING 23
2.9 TAIL DROP、RED、LAS 方法比較 23
第三章 問題描述 25
3.1 問題背景 25
3.2 理論基礎 26
3.2.1 行動模型 29
3.2.2 泊松分佈模型 30
3.3 演算法分析 32
3.4 問題解決機制分析 36




第四章 實驗效能評估 38
4.1 實驗模擬環境 38
4.2 TRAFFIC CLASS實驗不同流量探討 39
4.2.1 VIDEO AND VOICE 流量 41
4.2.2 VIDEO, VOICE, BEST EFFORT AND BACKGROUND 流量 43
4.3 BUFFER不同大小探討 44
4.4 THROUGHPUT 47
第五章 結論 49
參考文獻 50

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