合作通訊是一個新興的傳輸技術，其可以藉由轉送彼此的訊息到目的端來形成一個分散式天線陣列，進而提供空間分集增益。而基於在中繼端點或目的端的通道狀態訊息 (channel state information, CSI)，一個合作式通訊網路的系統效能能夠藉由選擇合適的中繼端點或功率資源分配來獲得進一步的提升。在本論文中，對於放大前送(amplify-and-forward ,AF)中繼網路，吾人提出兩種兩級式功率分配機制。其中，為了能夠適應於非對稱通道，此兩種機制皆首先決定介於資訊源端與中繼端點之間的功率分配，之後再考量所有資訊源端與中繼端點上的功率分佈情況。

針對一個在接收端配置有多根天線的分散式GABBA空時編碼(distributed GABBA space-time coded)與放大前送中繼網路，吾人所提出的第一種功率分配機制首先最大化瞬時傳輸速率來決定資訊源端與中繼端點之間的功率分配。其後再利用注水法(water-filling)來最佳化中繼端點間功率分佈的情況。並且吾人也利用GABBA的編碼結構來降低最大概似(maximum likelihood, ML)偵測機制的計算複雜度。此外，本論文也探討了此系統所獲得的陣列增益(array gain) 與分集增益(diversity gain)。

對於第二種兩級式功率演算法，吾人首先在多使用者放大前送中繼網路(multi-user AF relay network)中利用基因演算法(genetic algorithm, GA)來輔助解決中繼端點選擇與介於資訊源端和中繼端點之間的功率分配兩個結合機制所造成的問題。其中，根據中繼端點的選擇數目固定與否，吾人分別利用混合式基因演算法(hybrid genetic algorithm, HGA)或傳統基因演算法來解決此高度非線性最佳化問題。而在此演算法的第二級中，基於第一級所解出的資訊源端總功率和中繼端點總功率，吾人利用凸集最佳化(convex optimization)在最大化傳輸速率的考量下，使用一個疊代機制來交替的決定資訊源端與中繼端點上的功率分佈情形。

相關模擬結果顯示吾人所提之方法較之前人的方法，能獲得更好的系統效能且又大量地降低運算複雜度。

Cooperative communications are an emerging transmission technique in which a distributed antenna array can be created and provided the spatial diversity gains by relaying each other's messages to the destination. The performance of cooperative networks can be further enhanced by selecting an appropriate set of relays or by appropriately allocating the power resource based on channel state information (CSI) at the relays or at the destination. In this thesis, we propose two two-stage power allocation schemes for the amplify-and-forward (AF) relay networks. In order to be applicable to asymmetric channels, both schemes first determines the power allocation between the source and the relay, and then the power distribution among all source node(s) and relay nodes.

The first power allocation scheme, devised for AF relay networks equipped with distributed GABBA space-time code and with multiple antennas at the destination, first determines the power allocation between the source and the relays by maximizing the instantaneous rate, and then optimizes the power distribution among the relay nodes via water-filling. Also, by making full use of the GABBA encoding structure, an ML detection scheme is addressed to reduce the complexity. Furthermore, the array gain and the diversity gain are determined to provide further insights into the new AF relay network.

The second power allocation scheme is a two-stage algorithm devised for multi-user AF relay networks. First, a genetic algorithm (GA)-based approach is employed to solve the relay selection and the power allocation between the source and the relay at the same time, where, depending on whether the number of the cooperating relay nodes is either fixed or varied, we use a novel hybrid
GA (HGA) or the conventional GA to resolve the highly nonlinear optimization
involved. In the second stage, we consider an iterative scheme to determine the
power distribution alternately among all source nodes and among all relay nodes
using convex optimization to maximize the sum rates based on the source sum power and relay sum power determined in first stage.

Conducted simulation results show that both of the proposed approaches yield satisfactory performance with reduced computational complexity overhead compared with pervious works in various scenarios.

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