本論文針對IEEE 802.11ax中的省電機制Target Wake Time(TWT)，基於使用Orthogonal Frequency Division Multiple Access (OFDMA)傳輸的情況下若Access Point(AP)並未合理的安排節點甦醒順序，可能造成大量節點同時甦醒導致傳輸成功率下降問題。近年來物聯網裝置數量急速上升，許多能耗較低的物聯網裝置均使用電池驅動，且這些能耗較低的物聯網裝置通常並沒有大量傳輸資料的需求，綜合以上兩點許多物聯網裝置只在固定的時間點切換至甦醒模式，其餘時間均保持睡眠模式，當一個無線區域網路(WLANs)環境有許多裝置進入省電模式時，若AP沒有合理的安排裝置甦醒的時間，可能會導致許多裝置在同一時間甦醒，過多的裝置同時甦醒並參與競爭將導致傳輸成功率下降，許多學者透過排程的演算法來避免此問題的發生，但由於部分學者提出的機制假設過於強烈，不適用於現實場景，因此本論文提出一種動態的TWT排程方案，藉此解決上述之問題，也能夠提升(Resource Unit)RU的利用率以及提升網路效能。

This paper is aimed at the power saving mechanism Target Wake Time (TWT) in IEEE 802.11ax. Based on the use of Orthogonal Frequency Division Multiple Access (OFDMA) transmission, if the Access Point (AP) does not arrange the node wake-up sequence reasonably, it may cause a lot of The nodes to wake up at the same time, which leads to the problem of a decrease in the transmission success rate. In recent years, the number of IoT devices has increased rapidly. Many IoT devices with lower energy consumption are powered by batteries, and these low-energy IoT devices usually do not have the need to transmit a large amount of data. Combining the above two points, many IoT devices Only switch to wake-up mode at a fixed time point and stay in sleep mode for the rest of the time. When a wireless local area network (WLANs) environment has many devices enter power-saving mode, if the AP does not properly arrange the wake-up time of the device, it may be Many devices wake up at the same time. Too many devices wake up at the same time and participate in a competition, which will result in a decrease in the transmission success rate. Many scholars use scheduling algorithms to avoid this problem. However, the mechanism assumptions put forward by some scholars are too strong. Applicable to real-world scenarios, this paper proposes a dynamic TWT scheduling solution to solve the above-mentioned problems, and can also improve the utilization of (Resource Unit) RU and improve network performance.

第1章	介紹	1
第2章	背景知識	9
2.1	UORA簡介	10
2.2	網路模型	11
2.3	問題陳述	12
2.4       相關研究	15
第3章	動態TWT排程機制	19
3.1	問題分析	20
3.2	容量值(α)	22
3.3	檢驗方案	25
3.4	動態TWT排程機制演算法	27
第4章	效能評估	32
4.1	實驗格式與場景設定	32
4.2	排程機制比較及評估	33
第5章	結論	37
參考文獻	37

圖目錄
圖一、Individual TWT流程示意圖 (圖片來源:Aruba Company IEEE 802.11ax White Paper)	2
圖二、 Broadcast TWT流程示意圖 (圖片來源:Aruba Company IEEE 802.11ax White Paper)	3
圖三、 Opportunistic TWT流程示意圖 (圖片來源:Aruba Company IEEE 802.11ax White Paper)	3
圖四、各項TWT模式	5
圖五、Individual TWT示意圖	6
圖六、網路模型示意圖	11
圖七、節點甦醒時間不平均	12
圖八、排程表示意圖	13
圖九、大量節點同時甦醒示意圖	14
圖十、TSS排程過程一	16
圖十一、TSS排程過程二	17
圖十二、TSS排程過程三	18
圖十三、TSS排程過程四	18
圖十四、Pattern變化示意圖	21
圖十五、a值計算示意圖	24
圖十六、TSS與DTSS排程示意圖	25
圖十七、β值計算示意圖	26
圖十八、DTSS排程示意圖	30
圖十九、各項排程演算法驗證及比較	34
圖二十、DTSS、TSS排程過程影響節點數量	35
圖二十一、DTSS、TSS排程差異比較	36

表目錄
表一、模擬參數	32

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