正交分頻多工為現今一種相當有潛力的無線網路傳輸技術，搭配合作式網路，提供使用者之間互相協助傳遞訊息至目的端來形成一分散式天線陣列，進而提供空間分集增益。此外，基於已知各傳輸路徑的通道狀態，選擇合適的中繼端來協助傳輸，並考量在有限的頻譜與不干擾主用戶訊號過多的情形下，做適當的資源分配，將能有效提升系統整體效能。

本論文考慮一個正交分頻多工暨合作式感知無線電解碼傳遞網路，並在共同頻譜存取機制上做資源分配，目的是為了在對主要使用者干擾適當管理下，最大化次要使用者的最佳傳輸率以及整體能源效率最佳化。其中最佳化問題包含了子載波配對、功率分配與中繼端選擇，因此最佳化問題形成一混合整數規劃問題。為了在合理的成本下解決此複雜的混合整數規劃問題，本論文提出了一新穎的混合型基因演算法，在混合型基因演算法中，染色體可以切割成三個部分，第一部分為整數型的中繼端選擇基因，第二部分為整數型的子載波配對基因，第三部分為實數型的功率分配基因。本論文亦提出新式的交配運算及突變運算，以適應所增加的主要使用者干擾限制式。除此之外，本論文同時也設計了一新型之染色體初始化方法，包含凸面最佳化方法產生的基準染色體與實數基因演算法產生的染色體，使問題能收斂至最佳值。相關的模擬結果顯示，在不同的限制考量下，混合型基因演算法相較於其他論文的方法而言，擁有更加優越的系統效能與較低的複雜度。

The orthogonal frequency division multiplexing (OFDM) systems are a promising communication technique. Meanwhile, cooperative networks are an emerging transmission technique in which a distributed antenna array can be created and provide the spatial diversity gains by relaying each other's messages to the destination based on the channel state information. Also, cognitive radio (CR) networks can enhance the spectrum efficiency.

This thesis presents an efficient algorithm for joint subcarrier pairing, power allocation, and relay selection in an OFDM-based cognitive decode-and-forward (DF) multi-relay network. Such a joint consideration entails a mixed integer programming (MIP) problem. To efficaciously resolve this complicated MIP problem, we propose a novel blended genetic algorithm (BGA), which tackles all of these three issues as a whole and is free of extra assumptions made in previous works to simplify the problem. In our BGA, every chromosome is divided into a number of integer strings for relay selection and subcarrier pairing, and a real number string for power allocation. Moreover, new crossover and mutation operations are devised for the new chromosomes as well as to manage the interference to the primary users (PUs). A new initialization scheme based on a properly chosen baseline chromosome and a real GA is also addressed, so that the BGA converges more likely to the optimum. Conducted simulations show that the developed BGA provides superior performance, yet with lower computations compared with the main state-of-the-art works.

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