臺灣博碩士論文加值系統

在現今資訊量增長的時代裡，多使用者多輸出多輸入（MU-MIMO）已成為越來越受到重視的議題，所以我們希望可以藉由IEEE 802.11ac這個無線區域網路(WLAN)的通訊標準，來獲得解決這個問題的辦法。在IEEE 802.11ac標準中，波束成形(Beamforming)技術被使用於此標準中，我們分析在IEEE 802.11ac有多使用者多輸入多輸出但無波束訓練演算法支援和有波束訓練演算法支援的網路下，當使用者有不同的通道流量時，使用者應該在何種天線傳輸下進行傳輸較佳。並透過研究使用者於無干擾的情況下所需要耗費的波束訓練過程時間的長短，來評估使用者於多人時所需要花費的波束訓練過程時間長短。當使用者人數過少或檔案過小時，則對於使用者來說，使用波束訓練演算法所節省的時間過少，因此使用者不需要使用波束訓練演算法支援；而當使用者人數過多或檔案過大時，因為耗費相對較少的波束訓練過程時間，達到節省較多的傳輸時間，則對於使用者來說需要使用波束訓練演算法支援，以此評估來達到讓系統的吞吐量最大化。

In the day of information explosion, multi-user multi-output multi-input (MU-MIMO) has become an issue of increasing attention, so we hope to communicate via IEEE 802.11ac wireless local area network (WLAN) this standard to get a solution to this problem. In the IEEE 802.11ac standard, beamforming technology is used in this standard. We analyze networks with multiple user input and multiple output with beam training algorithm or with beam training algorithm in IEEE 802.11ac. Under the network, when users have different channel traffic load, it is better for users to transmit under which antenna transmission method. By studying the length of the beam training process that users need to spend without interference, the length of the beam training process that users need to spend on multiple people topology can be evaluated. When the number of users is too small or the file is too small, users do not need to use the beam training algorithm; when the number of users is too large or the file is too large, users would need to use the beam training algorithm. We use the evaluation to maximize the throughput of the system.

Contents
摘要 i
Abstract ii
致謝 iii
Contents iv
List of Figures vi
List of Tables viii
Chapter 1 Introduction 1
1.1 Research Background 1
1.2 Beamforming 3
1.3 MU-MIMO 5
1.4 Motivation 6
Chapter 2 Related Work 8
Chapter 3 System Model and Problem Description 9
3.1 System Architecture 9
3.2 Problem Description 9
3.3 Beam Training Algorithm 10
3.4 IEEE 802.11ac DCF Performance Analysis 13
Chapter 4 Simulation Results 22
4.1 Parameters and environment settings 22
4.2 Beam Training algorithm application simulation results 24
4.2.1 SU-SISO 24
4.2.2 SU-MIMO 27
4.2.3 MU-MIMO 31
4.2.3 MU-MIMO with different package size 40
4.2.3 Threshold setting 41
Chapter 5 Conclusion 43
Reference 44

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