正交分頻多工存取技術近年來主要應用在無線通信技術上, 而在無線
通信技術中, 合作式通訊可以經由中繼端傳送彼此訊息至目的端形成一個
分散式天線陣列, 進而提供空間多樣性增益。
在本論文中, 我們在正交分頻多工存取技術解碼前送合作式網路系統
中作比例式公平的資源分配。此最佳化問題包含了中繼端選擇、子載波配
置和功率分配。由於其原始問題是一個離散問題, 因此我們將此問題放寬,
並且利用對偶分解方法使之成為對偶問題並求解, 再利用KKT 條件來找
出此問題的次佳解。另外討論中繼端至兩端的子載波配對, 繼而得到更好
的系統效能, 並且藉由其資源分配, 我們考慮了中繼端公平的演算法。
我們所提出的演算法是藉由迭代運算來找出適當的子載波分配, 其運
算複雜度會隨著迭代次數的增加而大幅提升。為了降低此缺點, 所以我們
提出了降低複雜度的方法, 其方法為給予每個使用者最低速率的要求, 因
此需要迭代運算的子載波個數就會大幅減少。相關模擬結果顯示在不同的
限制和各種的情況下, 我們所提出之方法相較於前人的方法, 確實能提供
較低的複雜度且獲得更好的系統效能。

The orthogonal frequency division multiple access (OFDMA) sys-
tems can offer high spectral efficiency and mitigate the problem of
frequency-selective fadings, and have received lots of attention re-
cently. Cooperative networks, meanwhile, can form a distributed
antenna array and provide the spatial diversity gains to enhance the
converge, better performance can be reached by combining their in-
dividual advantages.
In this thesis, we consider resource allocation with proportional
fairness (PF) in the OFDMA-based decode-and-forward (DF) relay
networks. In order to attain the fairness of the relay nodes, we pur-
pose an algorithm which jointly consider relay selection and power
allocation. Because the formulated primal problem is nondetermin-
istic polynomial time-complete, we first make continuous relaxation,
then solve the dual problem by dual decomposition method. The
near optimal solution can then be found by the KKT condition.
Moreover, we also take into consideration the subcarrier matching
between the relay nodes to the source nodes and the relay nodes to
the destination nodes to further enhance the overall performance.
Since the algorithm proposed was iterative, it calls for lots for
computational complexity. To alleviate this setback, we also first
impose each user to have the minimum required data rate before
proceeding the recursive. As such, the computational load can be
substantially mitigated. Simulation results show that the proposed
method provides both higher throughput and better fairness among
relay nodes in comparison with previous algorithms.

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