在現在人們普遍都擁有智慧型產品時，相對的也產生對網路的頻寬使用需求越來越大，而目前最新現行產品所使用的網路通訊技術IEEE 802.11ac，除了優化現有的Wi-Fi傳輸效能，也針對多天線MIMO (Multi-input Multi-output)技術最佳化應用，尤其新技術MU (Multi-User) -MIMO可支援無線接取點(Access Point:AP)同時支援傳送相異的資料串流至多組網路用戶裝置(Wireless Station)，以達到更高的傳輸效率表現。然而，當不同配對組合的網路用戶裝置同時接收無線接取點的資料串流時，用戶裝置會獲得不同的傳輸速率，因此為了獲得最大的傳輸效率，必須考慮用戶裝置彼此之間的channel orthogonality，而在802.11標準定義中，用戶裝置會選擇訊號最強的AP做連結，但是這並不一定代表用戶裝置所能獲得的網路表現是好的，因此，如何在multi-cell MU-MIMO的網路架構下，讓用戶裝置能夠選擇實際可提供大的網路頻寬的無線接取點是一個挑戰。而在本論文中，我們設計出client-AP association演算法，考慮用戶裝置之間的channel orthogonality和實際所能獲得的網路頻寬，來達到用戶裝置能獲得最佳的網路表現。而我們的實驗模擬結果顯示，在我們設計不同的環境變數因子下，我們所設計的演算法表現都優於其他方法。

In a Wireless Local Area Network (WLAN), clients typically associate with the
AP that offers the maximal signal strength. Later works on client-AP association then
further take load balancing and fairness into consideration. Those schemes however are
not directly applicable in a multiuser MIMO (MU-MIMO) WLAN since different
combinations of clients result in different throughput of each individual client. We show
that the AP association problem for maximizing sum rate (the summation of all clients’
data rates) in multi-cell MU-MIMO downlink networks is NP-hard. Therefore, in this
thesis, we present two client AP association algorithm customized for MU-MIMO
WLANs. The proposed algorithm jointly solves the problems of client-AP association
and MU-MIMO client grouping with consideration of channel correlation among
clients. It hence allows a good group of clients, i.e., those with low channel correlation,
to together associate with the same proper AP and achieve a high sum-rate. The first
algorithm makes a client select an proper AP based on sum rate, and the second
algorithm dynamically makes some clients associate with different APs for adapting
various network conditions (e.g., the change of channel quality, client joining and
leaving due to mobility). For practical use, we further extend the second algorithm to a
distributed scheme. The simulation results show that our MU-MIMO AP association
algorithm improves the aggregate throughput by about 11%-28% and 26%-45%, as
compared to two common association approaches, i.e., RSSI-based and load-based
schemes, respectively.

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