本文場景建立於毫米波網路中並基於有著高吞吐量及低延遲標準的AR/VR 應用下，採用當前網路中較常使用的傳輸層控制協定(Transmission Control Protocol, TCP)，將有多位使用者同時以每秒數百萬位元的吞吐量進行資料傳輸。在此場景下，位處高頻段的毫米波訊號面臨著高路徑損耗的物理特性。因此人們透過波束成形技術使得毫米波訊號具有高度定向性以緩解路徑損耗的影響。也因如此，波束成形所產生的定向窄波需要不斷地追蹤與同步訊號，且當前波束訓練將會採用窮舉法對每個波束進行同步，以此找到最佳的波束組合。然而，我們發現於現有的IEEE 802.11ay 的信標間隔(Beacon Interval) 架構下，波束訓練將會減少可用於進行資料傳輸的持續時間，並造成封包在傳輸上增加數十至數百毫秒的額外延遲，在應用延遲標準低於波束訓練持續時間的狀況下，將可能造成使用者應用中斷等情形。此外，在多位使用者同時與同一台毫米波無線接取點連接時，我們發現在毫米波無線接取點進入波束訓練狀態下，將會減少信標間隔中用於資料傳輸的時段，造成訊號雜訊比(Signal-to-Noise Ratio, SNR) 較佳或不須進行波束訓練的使用者其資料傳輸連帶受到影響。為了改善上述波束訓練所帶來的影響，我們提出了一種基於封包複製的波束訓練性能傳輸優化方案(Transmission Optimization scheme based on Packet duplication for Beam training performance, TOPBeam) 。所提出的TOPBeam 由三種機制所組合，分別為毫米波雙連線機制、封包複製機制與緩衝區管理機制。首先，使用者透過毫米波雙連線機制同時與兩台毫米波無線接取點進行連接。如此一來，即使一台毫米波無線接取點進入波束訓練狀態，也能透過毫米波雙連線機制中的另一台毫米波無線接取點維持資料傳輸，從而避免所有使用者的應用因為波束訓練而中斷。接著，透過網路閘道器執行封包複製機制，透過複製原始封包的方式生成冗餘封包，並交由毫米波雙連線機制透過兩台毫米波無線接取點將封包等比例地送至使用者端，並結合毫米波雙連線機制，如此便能確保使用者透過一台毫米波無線接取點也能滿足應用需求。另外，毫米波無線接取點進行波束訓練時，將造成封包堆積在緩衝區中，待波束訓練完成後再傳輸緩衝區內的封包也可能因為超過應用延遲標準而被丟棄並影響到後續有效封包的傳輸，因此我們進一步透過緩衝區管理機制動態的剔除對於應用已經過時的封包。為了評估TOPBeam 的性能，我們將TOPBeam 實現在網路模擬器3 (Network Simulator 3, ns-3) 上，並採用IEEE 802.11ay 模組來進行模擬及實驗。實驗結果表明，TOPBeam 在波束訓練狀態下能顯著的提高AR/VR 應用的端到端吞吐量以及低延遲的傳輸表現。

In this thesis, we consider multiple downlink transmission control protocol (TCP) flows are transmitted in the millimeter wave (mmWave) networks for high throughput and low latency applications such as augmented reality (AR) and virtual reality (VR) applications. In this scenario, mmWave signals in the high frequency band suffer the issues of high path loss and are blockage sensitive. Beamforming is a technology that can reduce the effects of path loss by making mmWave beams, and the narrow beams are needed to be tracked and synchronized continuously. During beam training, the exhaustive search is used to find the best beam configuration, and thus the latency of beam training will last for tens to hundreds of milliseconds. If the latency requirement of the application is lower than the required time for beam training, users may encounter interruptions in their applications. In addition, we find that when the mmWave wireless access point connects with multiple users, during beam training, the Data Transmission Interval (DTI) will be reduced, which also affects the data transmission for users with better Signal-to-Noise Ratio (SNR) or those who do not need to perform beam training. To solve the problem caused by beam training, we propose a Transmission Optimization scheme based on Packet duplication for Beam training performance (TOPBeam). The proposed TOPBeam consists of mmWave dual-connectivity strategy, packet duplication strategy, and buffer management strategy. First, the users connect to two mmWave wireless access points through the mmWave dual-connectivity strategy. Then, the packet duplication strategy duplicates packets in the gateway and delivers them equally through the dual mmWave links obtained by the mmWave dual-connectivity strategy. With these two strategies, even if a mmWave wireless access point performs beam training, the data transmission can be maintained through the other mmWave wireless access point. Moreover, if a mmWave wireless access point performs beam training, causing head of line blocking, the packets missed the delay requirement (i.e., expired packets) are dynamically dropped through the buffer management strategy. This prevents transmitting the expired packets. To evaluate the performance of TOPBeam, we implement TOPBeam on Network Simulator 3 (ns-3) and used
the IEEE 802.11ay module for simulation and experimentation. The evaluation shows
that TOPBeam can significantly improve transmission performance regarding end-to-end throughput and latency during beam training.

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