應用於正交分頻多工系統之重複結構訓練符元之頻率偏移與符元時序最大相似估計

Abstract

中文摘要 在下一代商業寬頻通訊系統的建構中，正交分頻多工系統(OFDM)是相當受歡迎的規格，主要是因為正交分頻多工系統可以有效的減少一些無線傳輸環境所帶來的限制。不過，正交分頻多工系統同樣也存在著一些問題。其中，正交分頻多工系統對於信號同步時產生的錯誤相當的敏感，例如:載波頻率偏移與符元時序偏移。因此，在接收端，這些同步訊息的偏移都必須有效精準地被更正。 針對兩個重複訓練序列所設計的頻率偏移與符元時序最大相似估計已經被提出。在這篇論文中，我們針對擁有Q個重複格式的訓練序列，延伸導出了其適用的頻率偏移與符元時序最大相似估計方法。此外，我們同時也提出一個能有效增加載波頻率偏移估計範圍的循環機制。電腦模擬結果顯示出，在白高斯雜訊下，我們所提出來的估測機制能精準的逼近使用Cramer-Rao 下界定理所得出的最低極限值。我們也將此機制運用IEEE 802.11a無線區域網路系統中，獲得比現存ML估計機制更顯著的效能改善

Abstract OFDM is a favorable modulation scheme for the next generation commercial broadband communication systems since it can effectively mitigate the wireless channel effect. However, OFDM has higher sensitivity to the carrier frequency and symbol timing offsets. A maximum likelihood (ML) method has been proposed in literature to estimate these offsets using two repeated training symbols. In this thesis, we extend the method to obtain the ML frequency offset and symbol timing estimation for Q repeated training symbols where Q is greater than two. An iteration method is also proposed to extend the frequency offset estimation range. Simulations show that the performance of the proposed method can closely approach the Cramer-Rao lower bound. We also apply the proposed method to the 802.11a wireless LAN system and show that the performance of the proposed method can outperform the existing ML method.