在本篇論文中，我們探討多中繼點合作式網路的時變通道估測。由於通道隨時間變化，我們需要不斷週期性地插入訓練符元區塊以便追蹤通道變化;在這篇論文中，我們估測方式採用卡門濾波器來持續追蹤通道變化，主要是由於卡門濾波器只需要儲存前一訓練符元上的通道估測值，再對於當下的接收訊號經過適當的處理，與前一估計子相加，即可更新為該訓練符元的估計值，這一個低複雜度的更新特性，能在盡量減少系統的負擔下，就可以擁有該訓練符元之前所有接收資料用來估測通道的能力，避免浪費掉過去具有改善通道品質的資訊。此外，我們是採取直接估測從訊號源經中繼站至目的端的等效通道；若分別估測訊號源至中繼站與中繼站至目的端的通道，代表中繼站也需要幫忙估測通道，並且回授給目的端以便幫助解調，這樣會增加系統複雜度，而且回授的過程會遭受通道衰減以及接收雜訊的汙染，造成中繼站的估測值失真，反而誤差更大，所以我們在論文中直接進行等效通道的估測。而訓練符元上的通道是以卡門濾波器持續追蹤，我們設計各個中繼站傳送訓練符元時採用的預編碼機制，以減少通道誤差，獲得更準確的通道資訊。為了解調出傳送資訊，我們需要通道資訊來幫助解調。因此我們利用傳送訓練符元上的估計值以線性最小均方(linear minimum mean squared error, LMMSE)的方式線性內插求出通道資訊。由於用來內插的估測值是由卡門濾波器所產生，代表此估測值運用了當下時刻之前所有接收的訓練符元來估測，相當於利用擁有相關性更高的接收資料估測，所以能提高通道估測品質。因此在本篇論文主要貢獻是設計中繼站的傳輸順序降低估測誤差及利用卡門濾波器的估計子線性內差估測通道估測，增加通道的準確性。

In this thesis, we study channel estimation in time-varying cooperative network. Since channels vary with time, we insert training blocks periodically to trace channel variation. In this work, we adopt Kalman filter to trace channel variation due to its low complexity. By storing previous channel estimate, Kalman filter simply requires to process next received vectors to update current channel estimate. We use all past observations to estimate current channel state to avoid wasting information. In content of cooperation, we directly estimate effective channel from source through relay to the destination. The reason is that, we separately estimate the source-relay and relay-destination links, relays need extra efforts to estimate the channel and feedback estimates to the destination. It will increase the computational loading on relays, and the feedback channel may suffer channel fading, resulting in more distortion of estimates. Therefore, the destination directly estimate effective channel, using Kalman filter to trace variation. Furthermore, we design pre-coding scheme on relays for forwarding training symbols in order to reduce channel estimation errors and obtain more accurate channel information. To detect data symbols, we need to channel state information over each data block as well. Therefore, estimates over previous training blocks are interpolated to estimate channel over data blocks based on LMMSE criterion. Since estimates over training blocks are obtained from Kalman filter, it consequently improves estimation quality of the channel over the data blocks. The main contributions of the thesis are optimal training design to reduce the estimation error, the estimation based on Kalman filter, and linearly combing the estimates to provide more accurate estimates of the channels over data blocks.

論文審定書-i
誌謝-ii
中文摘要-iii
英文摘要-iv
第1章 簡介-1
第2章 相關背景-5
第2.1節 相關文獻簡介-6
第2.2節 多個中繼站的合作式網路通道估測-8
第3章 系統模型-12
第3.1節 等效通道模型-17
第4章 通道估測及訓練符元的設計-18
第4.1節 運用訓練符元時估計時變通道係數-18
第4.2節 訓練符元的設計-29
第4.3節 資料符元上的通道估計-32
第5章 電腦模擬及探討-35
第6章 結論-41
參考文獻-42
附錄A-44
附錄B-46
附錄C-49
附錄D-53
附錄E-55

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