「通道估測」(channel estimation)在多天線正交分頻多工系統中扮演的角色極為吃重，若將系統置於具速度影響的時變通道環境中更是尤其。一直以來，這都是受到相當鑽研的議題。
一般通到估測的方法即在特定的子載波(subcarrier)上安插入已知的引導數據(pilot data)，在估測出相對應的通道衰減增益後再用內插的方式進一步估得傳送資料所對應的通道增益。也就是我們所熟知的引導數據輔助的通道估測法(pilot-aided)。此一方法在慢數衰減的環境中或許行的通，但若是內插的趨近無法跟上傳送資料通道衰減的改變，引導數據上的通道衰減就是估測的再準確也是徒勞無功。若是想跟上通道的快數衰減變化勢必得使用更複雜的內插方法(複雜度提升)，又或者是插入更密集的引導數據(傳輸效率下降)。
此篇論文中，我們利用空間頻率格子碼(SFTC)對傳送資料作編碼，在接收端使用適應性殘存路徑最相似序列估計(PSP-MLSE)來對下一個時刻相對應子載波上的通道衰減做預估。於其中提及的適應性方法有卡爾曼濾波器(Kalman filter, KF)及最小期望值平方(LMS)。在考慮複雜度及準確性，更進一步提出新型傳輸符號架構(symbol structure)，將此二方法(KF & LMS)做適當程度的結合。
在整體封包的起始，預先插入少量的訓練符號(training symbol)來獲得一個不全為零的通道衰減增益矩陣當作接下來估測方法的起始，此後便進入PSP-MLSE通道估測更新程序，不需再插入任何引導數據，屬於資料輔助通道估測(data-aided)，對於傳輸效率具一定程度的提升。

In this thesis, we combine the least-mean square (LMS) and
Kalman filter (KF) during channel (time varying frequency
selective fading, modeled by AR(2)) estimation base on per-survivor processing-maximum likelihood sequence estimation (PSP-MLSE) algorithm in MIMO OFDM system. In transmitter, data stream is encoded by space-frequency trellis codes, then map into constellation points and modulated by OFDM. At the receiver, receiving data is degraded by fast fading channels (cause by Doppler effect). Using adaptive filter estimate the channel matrix H which is an important information to PSP-MLSE detection. Different from conventional recursive processing, it estimates and
update the channel of for one step prediction. According to the velocity of MS, we can adapt the ratio of using KF or LMS algorithm. Finally, we can decode the information bits by utilizing the PSP-MLSE via Viterbi-algorithm with lower complexity than conventional pilot-aided channel estimation.

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