因為現在網路的蓬勃發展，但由於頻譜資源有限，如何分配這些頻譜資源是現在無線通訊系統的關鍵問題。
本論文研究主題之ㄧ，即為在感知無線電網路使用彈性的頻譜共享技術，獲得更有效率的頻譜利用，我們考慮在感知無線電網路一個主要使用者能將其頻譜租給次要使用者達到共享頻譜的目的，利用定價方案和賽局理論共同解決網路頻譜共享的議題。此篇論文我們首先提出將非線性一個主要使用者，與多個次要使用者(NLMF)共享頻譜方案，視為一個多目標最佳化問題，售價由主要使用者同時提供給多個次要使用者競爭，並利用群體智慧解決這個問題，次要使用者依據對手前一次的策略的觀測資料，逐次的調整他們各自的策略，直到奈許平衡。
然後我們提出另一個以奈許平衡為基礎，非線性二階一個主要使用者多個次要使用者(NBMF)最佳化問題，和一個新型應用二階規劃和群體智慧最佳定價策略的最佳化技術，對於一個領導者跟隨者賽局的頻譜共享機制，除了考慮次要使用者的最佳利益，更進一步尋求主要使用者的最佳定價解。我們用粒子群演算法迭代的方式搜尋策略定價最佳化解這兩個模型，效率評估結果顯現感知無線電網路兩個定價模型的行為。
本論文中研究主題之二，即為針對無線網路通訊，研究一個結合重傳和多使用者分集的跨接式最佳化機制，對此我們提出多使用者加權公平排程架構，為了有效率的資源配置，此架構結合適應性調變編碼機制和具重傳自動重複請求機制。此方案設計是利用多使用者分集和重傳技術增加系統頻譜增益，並在未知時變衰減通道分佈下，提出隨機傳輸排程策略，使能在公平資源配置限制下，最大化系統頻譜效率，然後設計評量實用的排程逼近這些目標，並作為未來研究方向。

Because of the rapid growing demand of high quality service over wireless networks, spectrum resources are more and more inadequate in current communication systems. Since the bandwidth is very precious, allocation of spectrum resources becomes an important research issue.
Cognitive radio network is expected to use flexible radio frequency spectrum sharing techniques for achieving more efficient frequency spectrum usage. In this thesis, we consider the spectrum sharing problem that one primary user (PU) can share its frequency spectrum by renting this spectrum to multiple secondary users (SUs). The pricing scheme is a key issue for spectrum sharing in cognitive radio network.
We first propose a nonlinear one-leader-multiple-follower (NLMF) sharing spectrum scheme as a multi-object optimization problem; the prices are offered by PU to SUs at the same time. This problem can be solved by using particle swarm optimization (PSO); SUs gradually and iteratively adjust their strategies respectively based on the observations on their opponents’ previous strategies until Nash equilibrium is completed.
We then present a general nonlinear bilevel one-leader-multiple-follower (NBMF) optimization problem to further consider the revenue of the PU and a new optimal strategic pricing optimization technique which applies bilevel programming and swarm intelligence. A leader-follower game is formulated to obtain the Stackelberg-Nash equilibrium for spectrum sharing that considers not only the revenue of a PU but also the SUs utility.
We develop a swarm particle algorithm to iteratively solve the problem defined in the NBMF decision model for searching the strategic pricing optimization. The behaviors of two pricing models have been evaluated, and the performance results show that the proposed algorithms perform well to solve the spectrum sharing in a cognitive radio network.
In this thesis, we then consider a new wireless communication system which a cross-layer optimization scheme combining retransmission with multiuser diversity is investigated. To this end, we address the issue of cross layer design in the proposed multiuser weighted fair scheduling framework which combines adaptive modulation and coding (AMC) with the retransmission-based automatic repeat request (ARQ) protocol in order to provide efficient resource allocation.
This design is then employed to devise multiuser scheduling schemes that can optimally capture the available multiuser diversity and retransmission caused spectral efficiency gain. We present the stochastic transmission scheduling policy that exploits the underlying fading channel distribution which is unknown a priori and maximizes system spectral efficiency under a certain resource allocation fairness constraint. We design and evaluate practical schedulers that approximate these objectives and regard it as a research direction in the future.

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