隨著無線傳輸技術與手持式設備的發展與演進，適應行動環境之系統已成為當今極為重要的研究課題，一般固定式無線網路的研究中，使用特定數學通道模型模擬傳輸資料時的雜訊，進一步對參數作最佳化的調整，例如以高斯雜訊模擬傳輸通道便是十分常見的作法。然而在行動無線網路中，使用者處於不斷移動的狀態下，隨著週遭環境的改變，導致傳送端與接收端之間的傳輸通道難以預測，進而造成系統分析者難以使用特定數學式制定目標函數以及條件來正確地分析系統之行為，甚至制定出之目標函數過於複雜，使得最佳化之複雜度過大而無法適用於即時性的無線網路環境。
針對此問題，本篇論文提出以模糊回授控制系統為主軸的模型，透過模糊控制器只參考系統回授的特性，不需事先計算目標函數以及條件，完全適用於行動無線網路。進一步我們將此系統設計成調和跨協定多重目標問題之階層式模組，我們稱之為跨協定階層式模糊控制（HCLFC）。階層式架構不僅使行動無線網路系統完全相容於既有網路分層協定，更大幅降低運作時模糊控制器的時間複雜度，另一方面透過模糊決策理論同時達到多目標的控制，可同時達成無線網路中不同協定層級的控制目標。最後，我們以實體層和應用層跨協定（PHY-APP）控制為例，我們展示此HCLFC同時在PHY層和APP層達成各自的穩定控制目標，並應用在車間無線傳輸時，驗證此跨協定層式模糊控制器（HCLFC）在傳輸通道隨機快速變動的環境下依然可以即時調和PHY和APP層的控制。透過理論架構設計、分析、和模擬，本論文提出的HCLFC控制軟式計算方法優於傳統最佳化的硬式計算，更能適應於隨時變動且具有高度不確定性的行動式無線網路。

With the development and growing of wireless transmission techniques and handheld equipments, the system adapting mobile environment is an important research issue recently. In traditional researches in static wireless networks, we use specific mathematical channel models to model the channel noise, and then optimize the parameters. For example, a common way to model the transmission channel is to use Gaussian noise model. However, in mobile wireless networks, users are always moving so that the transmission channels between users and the base station are altering all the time. Moreover, transmission channels among users are also difficult to predict. Thus, it is hard to adopt optimization process by designing specific objective functions and constraints. Besides, constantly detecting channel models is infeasible in mobile environment.
To solve the problem, this thesis provides a fuzzy feedback control system model. Because a fuzzy controller only refers to the system feedback, we do not have to design objective functions and constraints beforehand. This characteristic makes a fuzzy controller totally apply to mobile wireless networks. Furthermore, we design the system model as a hierarchical model compromising the cross-layer multi-objective problem, called hierarchical cross-layer fuzzy control (HCLFC). The hierarchical architecture not only makes the system perfectly compatible in the open systems interconnection (OSI) protocol layers but greatly reduces the computing complexity. At the same time, we adopt fuzzy decision making method to compromise multi-objective control satisfying multiple control targets with highest satisfaction among different protocol layers in wireless networks. Eventually, we adopt HCLFC for collaboration of physical (PHY) and application (APP) layers where physical layer modulation and application layer packet adaptation are compromised to meet traffic requirements. The PHY-APP controller is implemented on inter-vehicle communication (IVC) systems providing a random and rapid changing transmission channels. By the architecture design, analysis and simulations, the HCLFC model in this thesis, which is soft computing, is a better choice to accommodate to highly uncertain mobile wireless networks than the traditional optimization method, which is hard computing.

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