基於深度展開之MIMO-OFDM系統下混合式波束成形設計

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研究生：	陳管元 Chen, Kuan-Yuan
論文名稱：	基於深度展開之MIMO-OFDM系統下混合式波束成形設計 Hybrid Beamforming Design in MIMO-OFDM Systems via Deep Unfolding
指導教授：	鍾偉和 Chung, Wei-Ho
口試委員:	張佑榕 Chang, Ronald Y. 吳仁銘 Wu, Jen-Ming 翁詠祿 Ueng, Yeong-Luh
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 通訊工程研究所 Communications Engineering
論文出版年：	2021
畢業學年度：	109
語文別：	英文
論文頁數：	42
中文關鍵詞：	MIMO-OFDM 、混合式波束成形、深度學習、深度展開、流形最佳化
外文關鍵詞：	MIMO-OFDM, hybrid beamforming, deep learning, deep unfold, manifold optimization
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在毫米波(millimeter wave)多輸入多輸出正交分頻多工(MIMO-OFDM)系統架構之下設計混合式波束成形(Hybrid beamforming) 時，需要考量到其效能以及運算複雜度，要達到足夠的效能同時兼具合宜的運算複雜度是一個極具挑戰的設計問題。其中，加權最小均方誤差法搭配流形最佳化(WMMSE-MO)之演算法可以提供充足的效能，然而其運算複雜度相當地高。所以，在本論文中，我們打算將深度展開(Deep unfolding)技術運用到WMMSE-MO演算法上，以此來設計出一深度展開模型。相較於原本的WMMSE-MO演算法，我們提出的深度展開模型擁有更快的收斂性並且可以達到更好的效能，此外，我們也提出一個共同設計的方法，此方法可以產生合適的初始點作為深度展開模型的輸入，透過此共同設計的方法，我們可以在更少量的迭代數目下進一步提升深度展開模型的效能。最後，經由在巨量天線MIMO-OFDM系統架構下設置不同的硬體參數(例如，射頻鏈路)以及演算法參數(例如，內外迭代數) 進行模擬，模擬結果顯示出我們的方法能夠在縮減的運算時間複雜度之下提供顯著的頻譜效益(Spectral efficiency)。

Designing hybrid beamforming transceivers in millimeter wave (mmWave)MIMO-OFDM systems with satisfactory performance and acceptable complexity is a challenging problem. The well-known weighted minimum mean square error manifold optimization (WMMSE-MO) algorithm offers desired performance but has high computational complexity. In this thesis, we propose to apply the deep unfolding technique to the WMMSE-MO algorithm. The proposed deep unfolding model yields faster convergence to better solutions as compared to the original algorithm. Additionally, we also propose the joint design method to generate the appropriate initial point as the input of the deep unfolding model. This design method can further improve the performance of the deep unfolding model within fewer iteration number. Simulation results demonstrate remarkable spectral efficiency performance with reduced computational time complexity for the proposed scheme, under different hardware (RF chains) and algorithmic (inner/outer iterations)settings for a massive MIMO-OFDM system.

摘要 ------------------------------------------------------------------i
Abstract -------------------------------------------------------------ii
誌謝 -----------------------------------------------------------------iii

Chapter 1 Introduction ---------------------------------------------1
1.1 Background and Motivations -----------------------------------1
1.2 Literature Review -----------------------------------------------3
1.3 Contribution -----------------------------------------------------5
1.4 Organization of the Thesis -------------------------------------5

Chapter 2 Technical Background -----------------------------------7
2.1 MIMO Systems  --------------------------------------------------7
2.2 Hybrid Beamforming -------------------------------------------8
2.3 Deep Learning --------------------------------------------------9
2.4 Deep Unfolding ------------------------------------------------11

Chapter 3 Hybrid Beamforming Design Method ------------------13
3.1 Signal Model ---------------------------------------------------14
3.2 Problem Formulation ------------------------------------------16
3.3 The WMMSE-MO Algorithm -----------------------------------18
3.4 The Proposed Deep Unfolded WMMSE-MO Algorithm -------19
3.4.1 MO RF Precoder Block --------------------------------------20
3.4.2 WMMSE BB Precoder Block ---------------------------------22
3.4.3 MO RF Combiner Block --------------------------------------22
3.4.4 WMMSE BB Combiner and Weight Matrix Block -----------23
3.4.5 Loss Function ------------------------------------------------24
3.5 Initial Point Joint Design --------------------------------------24
3.5.1 IIG Model ----------------------------------------------------26

Chapter 4 Simulations Results and Discussion ------------------28
4.1 Simulation Environment --------------------------------------28
4.2 Training Data Generation and Parameter Setting -----------28
4.3 Performance of Deep Unfolding WMMSE-MO with
Different Number of Iterations -----------------------------------29
4.4 Performance of Deep Unfolding WMMSE-MO with
IIG Initial Point ---------------------------------------------------32
4.5 Execution Time Analysis -------------------------------------34

Chapter 5 Conclusion and Future Work -------------------------36

References --------------------------------------------------------38
                                

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