本論文考慮在一感知網路中，無基礎建設支持，次級使用者（secondary user）在不干擾到主要使用者（primary user）的前提之下，藉由彼此之間的訊息交換，進行分散式的網路系統效能最佳化。本論文之系統模型，為包括實體層（physical layer）、資料鏈結層（data-link layer）、以及網路層（network layer）之跨層級設計，並且此系統模型將實作於分時系統（time slotted system）之中。其中實體層涉及傳輸功率之控制，資料鏈結層則含括了通道的分配與時間的排程，而網路層則涉及繞送路徑之選擇。本論文對於所考慮之系統模型，設計一內圈及一外圈演算法，分別用來解決非凸以及混和整數規劃問題。在內圈，我們以最佳化理論之對偶分解（dual decomposition），使用次梯度法（subgradient）來達到分散式網路的功效。在外圈，我們則實做了兩種分配時槽與通道的方法，啟發式地解決分散式網路最大流量問題。根據實驗結果，我們的演算法能夠對於次級使用者進行穩定而有效的網路資源分配。

We consider the secondary users have to carry out a distributed network optimization through the exchange of information between each other without infrastructure support in a cognitive radio network. Therefore, we propose the cross-layer system model including physical layer, data link layer and network layer. The physical layer involves the control of transmission power and data link layer includes the channel assignment and time scheduling while the network layer involves the selection of the routing path. For this system model, we design an inner loop and an outer loop algorithm to solve the nonconvex problem and the mixed-integer programming problem. In the inner loop, we use the gradient method to achieve the functionality of the distributed system. In the outer loop, we implement two kinds of method to allocate the time slots and channel to solve the network maximum throughput problem heuristically. According our modeling, the proposed algorithm is an effective resource allocator implemented distributed wireless cognitive radio network.

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