LTE上行通道傳輸之強健通道估計

Abstract

在未來通訊的發展中，無線通訊扮演了很重要的腳色，而其中以為LTE(3GPP)以及WiMAX(IEEE)這兩個分支為基礎個別發展。這兩個系統最主要的差異在於，LTE在上傳採用的是單載波分頻多工存取(SC-FDMA)的技術。相較於WiMAX所用的正交多頻多工存取(OFDMA)，其優點在於能降低峰均值比(PAPR)進而節省手機端的能量消耗。本篇論文介紹LTE單載波分頻多工存取(SC-FDMA)中，多通道傳輸估計的問題、演算法、分析及模擬等議題。

在通道估測中，我們首先採用最小平方差(least squre)的估計器，然後再利用兩種方法去估測載波的頻率響應。第一種方式是利用最小平方差估計器所求得的參考訊號上的載波頻率響應來估計通道間的互相關矩陣，並利用推導出的互相關矩陣得到線性最小均方根誤差(LMMSE)矩陣，來使參考訊號上的通道響應更平滑。第二種方法是利高斯窗口(GWD)，即是使用高斯分布當作權重來使參考訊號更平滑。在完成了頻域上的參考訊號通道估計，我們接下來利用線性內插的方式來得到資料載波上的頻域響應。

在模擬中，我們先利用AWGN通道驗證我們的模擬模型，之後再進行多重路徑的通道模擬。我們發現在低訊號雜訊比(SNR)的形況下，線性最小均方根誤差估計器的效能與高斯窗口的效能差不多，但後者的複雜度比起前者還要低。

Wireless communication will play an important role in the evolution of communication in the future, especially basing on WiMAX (IEEE) and LTE (3GPP) to develop individually. The major difference between the two systems is that LTE using single carrier frequency division multiple access(SC-FDMA)technique in uplink transmission, while WiMAX using orthogonal frequency division multiple access(OFDMA).The advantage of using SC-FDMA is to reduce the peak-to-average power ratio (PAPR) which saves the power consumption of user equipment (UEs). This thesis will introduce the subjects of channel estimation problems, algorithms, analysis of multi-path transmission in SC-FDMA. In channel estimation, we first use the least square estimator, then use two different methods to estimate the channel frequency response. The first method is to estimate the correlation matrix of channel by the frequency response of reference signals estimated by least square estimator. Then smooth the channel response of reference signals by linear minimum-mean square error (LMMSE) matrix derived by correlation matrix. The second method is using Gaussian distribution window (GWD) to make reference signal smoother. After estimating frequency response of reference signals, we use linear interpolation to get frequency response of data subcarriers.

In simulation, we test and verify the simulate model which we proposed in additive white Gaussian noise(AWGN) channel. Then we simulate in multi-path channel. We find that LMMSE has similar performance as Gaussian distribution window in low SNR, but Gaussian distribution window has lower complexity than LMMSE.