近年來，空間調變技術的巨量天線多重輸入多重輸出系統利已成為熱門的研究目標。因為空間調變技術可使在傳送端只有一根天線發射訊號的情況下達到很高的傳輸速率。然而，對於傳統空間調變技術的通道估計，所有傳送天線必須依序的傳送導航訊號，此情況非常消耗大量的時間和大量的導航訊號，尤其在巨量天線多重輸入多重輸出系統更是嚴重。
在我的研究中，我們利用通道間的相關性，會使通道在角域上有稀疏的特性在，因此提出了一種使用壓縮感知技術的通道估計方法，以利於改善導航訊號消耗和時間效率。所以我們只利用一部分的傳送天線去發送導航訊號就可以估計整個完整的通道環境。此外，我們還提出了盲估計之通道估測方法，以利於進一步減少導航訊號的消耗。在此方法中，我們利用已經被解出的接受訊號來估計整個通道。所以在下一次更新通道時，就可以不需要使用導航訊號。就模擬結果顯示，在高訊雜比之下，我們提出壓縮感知技術的通道估計方法比傳統的估計方法還要好。在低訊雜比之下，因為雜訊的干擾影響太大，越多的導航訊號還是有助於通道估計。另外比較盲估計的方法，在相同的傳輸量比較之下，提出的盲估計方法的傳輸性能會優於傳統的估計方法，結果證明我們提出的通道估計方法更優於傳統的估計方法。

Massive multiple-input multiple-output (MIMO) with spatial-modulation (SM), only one active transmitted antenna at any time instance, recently has been proposed. However, for conventional channel estimation (CCE) of SM, all transmitted antennas have to be sequentially activated for sending pilots, which consumes large time and pilots. In this work, for the limited scatters environment, by exploiting the channel correlation, which contribute to the sparse channel in angular domain, we propose a channel estimation method using compressed sensing (CS) technology, named CS-based channel estimation (CS-based CE), to improve pilot consumption and time efficiency. Specifically, only the part of transmitted antennas is used to send pilots, which can estimate entire channel information between transmitter and receiver. In addition, we also propose the blind CS-based channel estimation method to further reduce pilot consumption. In this proposed method, we used enough detected symbols to estimate channel for updating channel information in time-variant systems. Simulation results show that the signal detection performance of proposed CS-based CE is better than CCE at high SNR. To compare with the same throughput, the BER performance of the proposed blind CS-based CE is better than CCE and CS-based CE.

ABSTRACT........II
摘要........III
誌謝........IV
Table of Contents........V
LIST OF FIGURES........VI
LIST OF TABLES........VIII
Chapter 1 Introduction........1
1.1 General background information........1
1.2 Literature review........2
Chapter 2 Spatial Modulation for Massive MIMO........5
2.1 Spatial modulation MIMO systems........5
2.2 Transmitter for SM........6
2.3 Receiver for SM ---compressed sensing........8
Chapter 3 MIMO Multipath Channel Model........11
3.1 Channel model in spatial domain........11
3.2 Channel model in angular domain........12
3.3 The relation between phase coherence and distance between antennas........15
Chapter 4 Proposed Channel Estimation Scheme........17
4.1 System model........17
4.2 Proposed CS-based channel estimation........21
4.3 Proposed blind CS-based channel estimation........25
4.4 Complexity of SOMP and LS........29
Chapter 5 Simulation results........30
5.1 Theoretical analysis of channel estimation errors........30
5.2 Performance for proposed CS-based channel estimation........33
5.3 Performance for blind CS-based channel estimation........38
Chapter 6 Conclusion........44
References........45

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