近年來合作式通訊網路越來越受到重視，其主要原因為可達成空間分集的效果。在
解碼傳遞的合作式通訊中，由於每個中繼點的震盪器會產生頻率誤差，會導致目的端接收到訊號會有多重的頻率偏移的影響。本文提出了一個半盲式的多重頻率偏移估測演算法來解決這個問題，此方法主要為各中繼點的訓練符元使用交錯式的子通道來傳送訊號，並在接收端以MUSIC 演算法估測頻率偏移，使用此方法主要好處為可以將頻率偏移的量值區隔在不同的區間之內，因此可以簡化頻率偏移匹配的問題，同時解決以往MUSIC 演算法在頻率偏移值相近時，效果不佳的情況。電腦模擬結果中，模擬了使用正交的訓練符元，與本文提出的半盲式訓練符元配置比較，其結果證明使用半盲式的訓練符元與使用正交的訓練符元效果是一樣好的，因此使用本文所提出的演算法架構可以增進傳輸效益，同時也能減少運算的複雜度。

The cooperative networks recently become attractive because it can achieve spatial diversity. Multiple decode-and-forward (DF) relay nodes result in multiple carrier frequency offsets (CFOs) because each relay node owns its local oscillator. This paper presents a novel semi-blind multiple-CFO estimator for DF OFDM co-operative network. A procedure is designed for relay nodes in order to effectively derive the semi-blind multiple-CFO estimator. Each relay node occupies it own subchannels and its CFO falls in a nonoverlapped spectrum. The CFO mapping becomes simple to match their corresponding relay nodes. In addition, the proposed method can reduce hardware and computational complexity. Semi-blind random sequences are derived by the characteristic of signal matrix in MUSIC algorithm. Comprehensive simulations show that the random sequences can replace periodic training sequences. Furthermore, some information can be conveyed by the random sequences to increase the spectral efficiency.

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