在這個論文中, 我們探討了在放大後前送(amplify-and-forward) 的合作式網路中, 運用分散式時空方塊編碼(DSTC) 對通道估測效能影響。在以中繼點為基礎的傳送分成兩個階段。在第一階段, 來源端會傳送一組包含了訓練符元(training symbols) 和資料(data) 的符元區塊至目的端。而中繼端接收到的訊號之後, 為了達到全多樣性, 中繼點會採用分散式時空方塊編碼重新編碼信號並在第二階段前送至目的端, 兩個相位所接收到的訊號作結合來偵測資料符元。在此篇論文中, 由於中繼點採用放大後前送, 在目的端所接收到訊號受來源端到中繼端之間以及中繼端到目的端之間連結的通道係數所影響。在訊號偵測之前, 需要估測所有的通道係數, 因此我們提出一個半盲式方法來估測來源端及目的端之間直接連結和中繼點連結的通道係數。半盲式(semi-blind) 通道估測方法是利
用一些訓練符元以及接收訊號的二階統計特性所求得。為了改善偵測品質, 可以藉由已偵測的資料符元來增加額外的訓練符元來修正通道估測。透過模擬可驗證, 我們提出的通道估測以及修正有明顯的效能改善。

In this thesis, we study the effect of channel estimation on the performance of distributed
space-time coding (DSTC) in amplify-and-forward (AF) cooperative networks. The relay based transmission takes two phase. In phase I, the source transmits a block of symbols, which include training symbols and data to destination. After receiving signals at relay, the DSTC is adopted to re-encode signals in order to achieve diversity gain at relay nodes. At destination, the signals received in two
phase are combined and used to detected data symbols. In the thesis, for AF cooperative networks, the signal received at destination is effected the multiplication of channel coefficients on the source to relay and relay to destination links. Before
detection, channel coefficients of all links need to be estimated. We propose a semiblind method to estimate the channel coefficients of direct link and the relay links. The semi-blind channel estimation scheme, exploits a small number of training symbols
and second-order statistics of received signals. To improve the detection quality, the channel estimation is modified by treating the detected symbols as extra training symbols. Through simulation, it shows that the proposed channel estimation and the modification leads to obvious performance improvement.

1 Introduction 5
2 Background Review 9
2.1 Training Based Channel Estimation (channel : Quasi-Static Rayleigh
Fading) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2 Training Based Channel Estimation (channel : Time-Varying Channel) 13
2.3 Semi-Blind Based Channel Estimation . . . . . . . . . . . . . . . . . 16
2.4 Training Based Channel Estimation In Multi-Relay Network . . . . . 18
3 System Model 22
3.1 Distributed Space-Time-Coding . . . . . . . . . . . . . . . . . . . . . 25
3.1.1 Orthogonal Space-Time-Coding with real constellations . . . . 26
3.1.2 Orthogonal Space-Time-Coding with Complex constellations . 28
3.1.3 Quasi Orthogonal Space-Time-Coding . . . . . . . . . . . . . 29
4 Channel Estimation 30
4.1 Channel Estimation for S-D Link . . . . . . . . . . . . . . . . . . . . 32
4.2 Channel Estimation for S-R-D Link . . . . . . . . . . . . . . . . . . . 37
5 Data Detection 44
6 Simulation Results and Performance Comparisons 49
6.1 QOSTC Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
6.2 OSTC Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
7 Conclusion 60

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