在未來的第五代行動通訊系統中，可以利用毫米波搭配巨量天線多輸入多輸出的技術來增加吞吐量和可靠度。因為毫米波訊號的短波長特性，同面積下可以放入更多的天線來克服嚴重的通道衰減。此研究提出三個創新的混合射頻與基頻的預編碼技術演算法，來減少射頻電路的個數與複雜度。此外，為了驗證演算法的整體效能，此研究改良了Quadriga通道模型並提出一個具有空間連續性的毫米波通道模型。與現有文獻相比，此研究提出的演算法能夠降低運算複雜度並增進硬體架構的平行化。在波束追蹤的情況下，藉由重複使用預編碼器的運算結果，運算複雜度可以有更進一步地化簡。根據在rank-reduced的毫米波通道模擬結果，此研究提出的預編碼演算法在16x16的一維線性天線陣列多輸入多輸出系統、使用者以時速十公里移動於線性路徑的環境中，可以減少60.3%至66.9%的運算複雜度。

In the future 5G system, the millimeter wave (mmWave) technology utilizes massive multiple-input multiple-output (MIMO) to increase the throughput and reliability. Due to the short wavelength of mmWave signals, more antennas can be adopted to alleviate severe channel pathloss. In this study, three novel algorithms of the joint RF and baseband precoding scheme are proposed to reduce the RF chain number and complexity. Moreover, a spatially-consistent mmWave channel model is proposed to verify the overall performance by the modified Quadriga channel. Comparing to the earlier works, the proposed algorithms lower the computation complexity and enable highly paralleled hardware architectures. In the beamtracking case, the computation complexity can be further cut down by reusing previous precoder results. In the rank-reduced mmWave channel simulation, the proposed precoding algorithms for the 16x16 linear antenna array MIMO systems can achieve the complexity reduction ratio of 60.3% to 66.9% for the case of 10 km/hr moving user in the linear track.

1 Introduction 1
1.1 Millimeter Wave MIMO Systems . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Research Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Organization of This Thesis . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.4 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Precoding Scheme for the Single-User MIMO Channel 5
2.1 MIMO Channel Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.1 Millimeter Wave Channel Model . . . . . . . . . . . . . . . . . . . 6
2.1.2 Quadriga Channel Model . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.3 Millimeter Wave Channel Model with Continuous Drifting . . . . 13
2.2 SVD-Based Precoding Method . . . . . . . . . . . . . . . . . . . . . . . . 26
2.2.1 Singular Value Decomposition . . . . . . . . . . . . . . . . . . . . 26
2.2.2 SVD-Based Precoding Scheme . . . . . . . . . . . . . . . . . . . . 27
2.3 Joint RF/Baseband Precoding Scheme via Simultaneous OrthogonalMatching
Pursuit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.3.1 Joint RF/Baseband Precoding Scheme . . . . . . . . . . . . . . . 28
2.3.2 Precoder Reconstruction via Simultaneous Orthogonal Matching
Pursuit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.3.3 Generation of Array Response Vectors . . . . . . . . . . . . . . . 31

2.4 Parallel-Index-Selection Matrix-Inversion-Bypass Simultaneous Orthogonal
Matching Pursuit Algorithm . . . . . . . . . . . . . . . . . . . . . . . 32
3 Proposed Precoder/Combiner Reconstruction Algorithms with Beam-
tracking Ability 35
3.1 Proposed Pre-Parallel-Index-Selection Matrix-Inversion-Bypass Simultaneous
Orthogonal Matching Pursuit Algorithm . . . . . . . . . . . . . . . 36
3.2 Proposed Bitstream-Based Pre-Parallel-Index-SelectionMatrix-Inversion-
Bypass Simultaneous Orthogonal Matching Pursuit Algorithm . . . . . . 39
3.3 Proposed RF-Based Pre-Parallel-Index-SelectionMatrix-Inversion-Bypass
Simultaneous Orthogonal Matching Pursuit Algorithm . . . . . . . . . . 42
4 Computation Complexity and Performance Analysis 47
4.1 Simulation environment . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.2 Proposed Pre-Parallel-Index-Selection Matrix-Inversion-Bypass Simultaneous
Orthogonal Matching Pursuit Algorithm . . . . . . . . . . . . . . . 53
4.3 Proposed Bitstream-Based Pre-Parallel-Index-SelectionMatrix-Inversion-
Bypass Simultaneous Orthogonal Matching Pursuit Algorithm . . . . . . 62
4.4 Proposed RF-Based Pre-Parallel-Index-SelectionMatrix-Inversion-Bypass
Simultaneous Orthogonal Matching Pursuit Algorithm . . . . . . . . . . 71
4.5 Comparison of the Proposed Algorithms . . . . . . . . . . . . . . . . . . 80
5 Conclusion and Future Work 83
5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

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