正交分頻多工是現今一種相當有潛力的無線網路傳輸技術, 搭配合作式網路能
藉由使用者之間互相協助傳遞訊息至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。

賽局理論是一套有系統的策略分析工具, 本來是在應用在經濟學中, 它讓每一
位參賽者或參賽團體根據不同的考量因素選擇對自己最有利的策略, 而近年來
在各領域中越來越受重視與應用。

在本論文中, 我們考慮正交分頻多工合作式網路系統中的資源分配問題。在
系統模型裡, 與以往不同的是我們讓一個來源端可以使用多個子通道來
傳遞訊息, 透過這個方法使得每一個來源端可以獲得更高的速率及更低的位
元錯誤率。此最佳化問題包含了功率分配、子通道分配及子載波配對。

由於我們是在一個合作式網路系統下, 所以我們利用賽局理論中的奈許談判解來當
我們的目標式, 用其解決我們資源分配的問題。 我們為了降低計算量也提出了次佳解, 它
簡化了奈許談判解的目標式, 並用注水法來分配功率問題。奈許談判解和以往不
同的是它考慮到了參賽者的資源分配之公平性, 並且最後我們經由演算法達到
最佳化的解。

相對之前的方法, 我們使用的奈許談判解是未經簡化過的目標式且提出的演算法
是藉由迭代運算來找到最適當的功率分配、子通道分配及子載波配對, 所以我
們的得到的會是最佳解, 比前人的次佳解能得到更高的速率及更公平的分配資
源給每一位來源端。

Orthogonal frequency division multiplexing (OFDM) is a promising wireless communication transmission technology; whereas, cooperative networks can provide spatial diversity gain by a distributed antenna array between the user and the destination. In this thesis, we consider a resource allocation problem, including power and subchannel allocation, and subcarrier pairing, in OFDM cooperative networks, in which user passes message through a set of subchannels, so that it can get lower bit error rate and attain higher transmission data rate.

Game theory, originated in the economics, is a systematic strategic analysis tool, which allows each contestants or participating groups to choose their own strategies depending on the most favorable considerations. Recently, it has received a vast amount of attentions for its potential applications in various disciplines of applications. In this work, we use the Nash bargaining solution (NBS) in game theory as our objective function and use it to solve the resource allocation problem considered. Furthermore, to alleviate the computational load, we also consider a suboptimal solution, which simplifies the NBS objective function, and use water filling to allocate the power. Compared to previous works, our NBS solution can attain higher transmission by joint and fair consideration of power and subchannel allocation with subcarrier pairing. The suboptimal solution exhibits slight performance loss with great mitigation of computational complexity.

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