為了未來通訊系統高傳輸、容量之需求，巨量天線多重輸入多重輸出系統是現在的研究熱門。而對於無線通訊系統的通道估計上，由於毫米波的高耗損特性，僅僅只有少量的有效路徑能由傳送端到接收端，因此，其整體通道在角域上具有稀疏之特性。基於上述，在做通道估計時，我們可以利用壓縮感知這項技術，其原理是藉由少量的資訊來還原出數量龐大的未知訊號，以使少量的資訊達到有效率的利用。
傳統通道估計方法上，傳送數個正交化波束作為導引訊號，而後依據所接受到的信號大小來決定環境中有效路徑的位置。然而，其所估計到的輻射角、入射角的精確性會為正交化波束之數目所限制。而在相關角度解析度研究上，研究已提出編碼簿來提升角度及通道估計的精確性。然而，對於巨量天線而言，其編碼簿的設計太大，因而致使整體估計的計算複雜度太高。有鑑於此，本文提出一個新的方案，設計一個較小的編碼簿來提高對入射角的估計，而後再利用估計出來的數值更進一步地分析傳送端的輻射角。模擬的結果顯示，相對於他人提出的通道估計方法，本文提出的方法可以讓估計中的計算複雜度大幅下降，而整體通道估計的正確性上也有較好的表現。

In order to achieve higher throughput in next generation communication system, massive MIMO is popular topic in recent study. For the channel estimation in wireless system, due to severe path loss of millimeter wave in the propagation environment, there is only a few effective path between transmitter and receiver. Therefore, the total channel has a characteristic of sparsity in angular domain. Based on above mentioned, for the issue of channel estimation, compressed sensing technique is useful to solve it. The principle of this technique is that we can use a few data information to recover the more unknown signal and it can achieve an efficient use of data source.
For the conventional channel estimation, we estimate the location of effective path in channel according to the strength of received signal by training several orthogonal beams at transmitter. However, there is a limitation of the estimated angle of arrival (AoA) and departure (AoD) in precision with the limited number of orthogonal beams. In order to enhance the precision of these estimated angle, some researcher proposed a codebook to do with this. But, the size of codebook they proposed is too large and make it more complex on computation. Given that this problem, we propose a new scheme for channel estimation – Two-stage scheme: One for estimating AoA by using a small size codebook, and the other for estimating AoD by analyzing the signal value after processing in the former stage. Simulation results show that our proposed channel has a lower computational complexity and better performance for channel estimation compared to the other works.

ABSTRACT II
摘要 IV
誌謝 V
CONTENTS VI
LIST OF FIGURES VII
LIST OF TABLES IX
Chapter 1 Introduction 1
1.1 Background 1
1.2 Related Works 3
Chapter 2 System and Channel Model 5
2.1 System Model 5
2.2 Channel Model 8
2.2.1 Beamforming 8
2.2.2 The Representation of Channel Model 10
2.2.3 The Transformation of Channel in Spatial Domain to Angular Domain 11
Chapter 3 Channel Estimation 13
3.1 Preliminaries: Compressed Sensing 13
3.2 The Existing Channel Estimation Method 16
3.3 Proposed Channel Estimation Scheme 19
3.3.1 The Formulation of Compressed Sensing Problem 19
3.3.2 Recovery 21
Chapter 4 Simulation Results 29
4.1 System Model and the Distribution of Samples 30
4.2 The Performance of Channel Estimation Analysis 36
Chapter 5 Conclusion 51
References 52

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