巨量天線多使用者系統已被視為是未來無線通訊網路的關鍵技術，因為該系統可以提供高傳輸率以及滿足使用者對穩健傳輸的需求。在該系統中，通道資訊扮演重要的腳色，且通道估測的準確度將大大的影響系統的效能。其中，由於導頻汙染(pilot contamination)的影響，在該系統中實現準確的通道估測是十分困難的。因此，在本篇論文中，我們提出一種低複雜度半盲式通道估測演算法來解決導頻汙染所造成的問題，並藉此增進通道估測的準確度。一開始我們先利用修訂冪方法(modified power method)來找出主要訊號存在的子空間，並將接收訊號投影至該空間藉此降低鄰近蜂巢使用者所造成干擾。接著，我們利用少許的導頻訊號來產生初始的通道估測值，並利用該估測值偵測出傳送訊號。最後，我們將解調出的傳送訊號當作新的導頻訊號並依序代入所提出的迭代通道估測法中，藉此更新通道估測值並進一步的降低導頻污染的影響。由於所提出的演算法並不需要使用奇異值分解來求取子空間，因此維持了低複雜度。另外，我們也分析出所提出的演算法其均方誤差與資料長度成反比。因此，增加資料長度能改善通道估測的準確度，並消除導頻汙染的影響。模擬結果顯示所提出的演算法可有效的解決導頻汙染所造成的問題，且其效能比現有的演算法更好。

Massive multi-user multiple-input multiple-output systems have become a key technology to achieve requirements of high throughput and robust transmission for next generation mobile communications. Optimum transceiver design in such systems highly relies on accurate channel state information, while the problem of pilot contamination will severely degrade the accuracy of channel estimates. In this thesis, we propose a low-complexity semiblind channel estimation algorithm to mitigate the effects of pilot contamination. We first project the received signals onto the subspace which is suffered with less interference. To reduce computational complexity, the bases of the subspace are obtained recursively by the modified power method. By exploiting a small number of pilot symbols, we can find the initial estimate of the projected channel coefficients. Finally, we detect data symbols and refine the channel estimate alternatively to eliminate the effect of pilot contamination. The complexity of our algorithm is low because of the subspace projection and innovation process. We also analyze mean square error (MSE) of the proposed channel estimate asymptotically, and it shows that the MSE are inversely proportional to the length of data symbols. Therefore, the proposed algorithm can be enhanced by increasing data length. Simulation results demonstrate that the proposed algorithm outperforms the existing works and can alleviate the effect of pilot contamination effectively.

Chinese Abstract ii
Abstract iii
List of Tables vi
List of Figures vii
1 Introduction 1
1.1 Related works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3 Organizations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2 Fifth Generation (5G) Wireless Systems 8
2.1 Key Technologies to Achieve High Data Rate . . . . . . . . . . . . . . 10
3 System Model 13
3.1 Massive MU-MIMO Systems . . . . . . . . . . . . . . . . . . . . . . . 13
3.2 Pilot Contamination . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4 Proposed Recursive Semiblind Channel Estimation Algorithm 19
4.1 Low-complexity subspace projection algorithm . . . . . . . . . . . . . 20
4.2 Initial channel estimation . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.3 Recursive data-aided estimation algorithm . . . . . . . . . . . . . . . 32
5 Complexity and Asymptotic Analysis 41
6 Simulation Results 45
6.1 Threshold determination for proposed algorithm . . . . . . . . . . . . 46
6.2 Effects of data length for proposed algorithm . . . . . . . . . . . . . . 49
6.3 Effects of antenna numbers for proposed algorithm . . . . . . . . . . 51
6.4 Compare proposed algorithm with other methods . . . . . . . . . . . 53
7 Conclusions 61
Bibliography 62

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