為了在下一代 5G 無線通訊系統中達到更高的資料傳輸率，毫米波通訊被視為解決方法。然而，在如此高的頻段中，毫米波信號會遭受嚴重的路徑損耗，為了彌補這一缺點，多輸入多輸出系統中的波束成形技術提供了相當大的功率增益。然而，此類波束成形需要基地台與用戶設備之間的準確通道狀態資訊，在毫米波通訊中獲取這樣的資訊是具有挑戰性的。為了獲取通道狀態資訊，波束訓練是其中一種方法，我們可以利用波束訓練獲得通道的估計值。然而，在毫米波系統中，通道不斷變化，所以我們需要波束追蹤來追蹤通道的變化。以下是總結的步驟：基地台向特定方向發送同步信號，該方向是基於先前的通道估計值，用戶設備再掃描同步信號。給定通道變化模型的情況下，用戶設備可以使用最大後驗機率法則來估計當前的通道。使用波束追蹤的優勢在於，與常見的波束訓練方法相比，它需要較少的訓練符號，因此有更多的符號可用於數據傳輸。因此，本文提出了一種用於毫米波追蹤系統的波束設計方法。為了減少時間和頻寬資源，我們提出了一種多模態波束設計，相比其他波束型態，可以減少均方誤差和決策錯誤機率。模擬結果顯示，對於相同的符號時間，使用我們設計的波束的性能更好。

In order to achieve higher data rates in the next-generation 5G wireless communication system, millimeter-wave (mmWave) communication is considered the solution. However, within such abundant frequency bands, millimeter-wave signals also suffer from severe path loss, to compensate the disadvantage, beamforming technique in the multi-input multi-output (MIMO) systems provide the considerable power gain. However, accurate channel state information (CSI) between the base station (BS) and the user equipment (UE) remains essential for implementing such beamforming techniques, which is challenging to obtain in mmWave communications. In order to obtain CSI, beam training is one of the methods, we can use beam training to get the channel estimation. However, in mmWave systems, the channel is constantly changing, so we need beam tracking to track the variation of the channel. The steps are summarized below: The BS transmits synchronization signals (SS) in certain direction, which is based on the previous channel estimation and the UE scans the synchronization signals. With the assumption of the channel variation model, the UE can estimate the current channel by using maximum a posteriori (MAP) criteria. The advantage of using beam tracking is that it requires fewer training symbols compared to conventional beam training methods, thus, there are more symbols left to do the data transmission. Therefore, in this thesis, a beam pattern design method is proposed for mmWave tracking systems. To reduce the time and bandwidth resources, we propose a multi-modal beam pattern design, which can reduce the mean-square error (MSE) and decision error probability (DEP) compare to other beam pattern. The simulation results show that for the same symbol durations, the performance is better with the beam pattern we design be used.

ABSTRACT II
中文摘要 III
致謝 IV
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 System Model 5
2.1 Massive MIMO Hybrid Precoding System 5
2.2 Channel Model 7
2.3 Temporal Correlation of the mmWave Channel 8
Chapter 3 Beam Tracking Strategy 11
3.1 Beam Search Scheme 11
3.2 MAP Estimation Based on Received Signal Complex Gain 12
3.3 MAP Estimation Based on Received Signal Power Gain 16
Chapter 4 Multi-modal Beam Pattern Design 19
4.1 Multi-modal Pattern Design Requirement 19
4.2 Multi-modal Beam Pattern Design 22
4.3 Second Group of Multi-modal Beam Pattern Design 28
Chapter 5 Simulation Results 32
5.1 Evaluation of MSE and DEP of Conventional MAP-Based Estimation 32
5.2 Evaluation of MSE and DEP of Power MAP-Based Estimation 39
5.3 Evaluation of MAP-Based Estimation Focus on High Transition Probability Area 42
5.4 Average Power Gain of Conventional MAP-Based Estimation 43
5.5 Average Power Gain of Power MAP-Based Estimation 44
Chapter 6 Conclusion 45
References 46

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