Channel Identification of OFDM System in Very High Mobility Environment

[[abstract]]本論文主要探討超高速移動環境。一般中低速移動的環境，我們通常假設在每一個正交分頻多工符元內通道為非時變的，然後我們進行傳送與解調。因高速鐵路的超高速移動環境( 300km/h )，導致嚴重的都卜勒效應，系統受到快速衰減影響相當大。在高點數OFDM傳輸架構，在每一個正交分頻多工符元內就會發生通道的變化，傳統的非時變通道傳送解調會造成錯誤率大幅提昇，因此領航符元如何傳送以及在接收端如何解調能夠降低其錯誤率是個相當重要的議題。本論文使用最大概似通道估測將估測出的通道值再作時域通道內插來改良通道情況。最後，我們用線性最小均方差等化器來對抗ICI效應。另外在真實的環境中，通道?可能是已知的，因此利用功率偵測臨界值來識別出最長的通道脈衝響應格數與雜訊位置更是另一個重要的議題。[[abstract]]This thesis probes for the very high mobility environment mainly. In medium or low mobility environment，we usually assume that the channel is time-invariant during each OFDM symbol which we transmit and demodulate. Because the very high mobility environment for TAIWAN HIGH SPEED RAIL (300km/hr) leads to critical doppler effect，the system suffers from the fast fading effect severely. In OFDM used large FFT points transmission architecture，the channel will change during each OFDM symbol.The bit error rate of conventional transmission and demodulation for time-variant channel will increase substantially ,so the major issues that how to transmit pilots and demodulate in receiver to decrease the bit error rate. This thesis uses ML channel estimation to estimate channel value and do interpolation in time domain to modify the channel situation. Finally ，we Use linear minimum mean square error equalizer to resist ICI effect. Besides , the channel is impossible to be known in real environment,so using power detection to distinguish the longest channel impulse length and noise location is the other major issue.[[note]]碩