在可重構智慧表面(RIS) 輔助的毫米波(mmWave) 多輸入多輸出(MIMO)
正交頻率分割多工(OFDM) 收發機系統下，目標是共同設計收發器混合
波束成形(Hybrid beamforming) 和RIS 的相位偏移(Phase shift) 來最大化頻
譜效率(Spectral efficiency)。我們採用混合波束成形中部分連接(Partiallyconnected)
結構，雖然會犧牲一些性能，但能夠有效地降低硬體成本是較具
有經濟效益的架構。我們將加權最小均方誤差流形優化(WMMSE-MO) 算
法應用到RIS 輔助系統上，由於WMMSE-MO 算法需要經過多次迭代才會
收斂到不錯的性能上，這是造成其運算複雜度較高的原因，因此，我們進
一步利用神經網路對WMMSE-MO 算法進行深度展開(Deep unfolding)，以
減少算法的計算複雜度並加速其收斂。最後，我們會改變不同內外層迭代
次數以及系統設定參數，並與不同的算法做比較，而從模擬結果來看，所
提出的深度展開WMMSE-MO 算法相較於沒有深度展開的對應算法與不同
的算法，能達到更優異的頻譜效率表現、收斂速度和計算效率。

In a millimeter-wave (mmWave) multiple-input multiple-output (MIMO) orthogonal
frequency-division multiplexing (OFDM) transceiver system assisted by
a reconfigurable intelligent surface (RIS).The goal is to jointly design transceiver
hybrid beamforming and RIS phase shifts to maximize spectral efficiency (SE). By
utilizing a partially connected structure in the hybrid beamforming, though sacrificing
some performance, it effectively reduces hardware costs. We employ the
weighted minimum mean square error manifold optimization (WMMSE-MO) algorithm
for the RIS-assisted system. However, the WMMSE-MO algorithm requires
multiple iterations to achieve satisfactory performance, leading to high computational
complexity. In order to mitigate this, we further utilize neural networks
to perform deep unfolding on the WMMSE-MO algorithm. This approach significantly
reduces computational complexity and accelerates convergence. Additionally,
we adjust the number of iterations for both the inner and outer layers, along
with system configuration parameters. These adjustments are made while comparing
other algorithms. Simulation results demonstrate that the proposed deepunfolded
WMMSE-MO algorithm outperforms counterparts without deep unfolding
and other algorithms. It achieves superior SE performance, convergence speed,
and computational efficiency.

摘要i
Abstract ii
誌謝iii
contents v
List of Figures vii
List of Tables viii
1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Literature Review and Related Works . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Technical Background 6
2.1 MIMO System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Hybrid Beamforming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3 Reconfigurable Intelligent Surface . . . . . . . . . . . . . . . . . . . . . . . . 8
2.4 Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.5 Deep Unfolding Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3 Jointly Design Hybrid Beamforming and RIS Optimization Method 15
3.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.2 mmWave Channel Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.3 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.4 The Proposed Hybird Beamforming Design for RIS-aided MIMO-OFDM Systems
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4 Simulations Results and Discussion 26
4.1 Simulation Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.2 Simulation Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.3 Performance of the Propsed Method and Compared Algorithm . . . . . . . . . 27
4.3.1 Discussion of Algorithm Converge . . . . . . . . . . . . . . . . . . . 27
4.3.2 Performance of Different Inner Iteration with NRF
r = 4 . . . . . . . . . 29
4.3.3 Performance of Different Inner Iteration with NRF
r = 2 . . . . . . . . . 31
v
4.3.4 Performance of Different Numbers of Data Streams . . . . . . . . . . . 33
4.4 Execution Time Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
5 Conclusion and Future Work 35
References 37
vi

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