近年來，MIMO技術儼然成為通訊領域裡的核心技術， 且基於MIMO雷達的傳送波束圖(Transmit beampattern)設計已被大量探討以及研究。在傳送波束圖設計的實作中，波束圖通常會考慮恆定模量(Constant modulus)約束，此外由於恆定模量約束，波束圖的設計變成一個不易處理的問題。在現存的解決方法中，一個直接的方法是利用流形優化(Manifold optimization)演算法，然而流形優化是個高複雜度的演算法，因此如何在低複雜度和好的系統性能間取得平衡是一個具有挑戰性的演算法設計問題。在此，我們引入深度學習技術，由於其出色的解決問題能力。我們將應用深度展開(Deep unfolding)於流形優化演算法。跟原本的流形優化演算法相比，所提出的基於深度展開的演算法能夠大幅減少計算上的複雜度。此外，我們提出的模型設計方式結合三個懲罰權值項，可以有效避免系統的性能損失。最後，模擬結果顯示，相較於流形優化算法，所提出的方法少了將近25倍的執行時間以及干擾功率降低了40 dB，故我們提出的方法能以較低的複雜度達到好的系統效能。

In recent years, MIMO has become a core technology in communications, and the beampattern design based on the transmitter of the MIMO radar has been extensively discussed and studied. In the implementation of the beampattern design at the transmitting end, the constant modulus constraints are often considered. Furthermore, due to such constraints, the beampattern design has become an intractable problem. Among the existing solutions, a direct approach is to use the manifold optimization algorithm. However, the manifold optimization is an algorithm with high complexity. Therefore, striking a balance between low-complexity and good system performance is a challenging task in algorithm design. Here, we introduce deep learning technology due to its excellent problem-solving ability. We apply deep unfolding to the manifold optimization algorithm. In comparison with the original manifold optimization algorithm, the proposed deep unfolding-based algorithm can greatly reduce the computational complexity. Furthermore, our proposed model exploits three penalty weight terms to effectively avoid the performance loss of the system. Finally, the simulation results demonstrate that the execution time of the proposed method is approximately 25 times faster and that the interference power is 40 dB lower as compared to the manifold optimization, implying that our proposed method achieves outstanding system performance with significantly lower complexity.

摘要---------------------------------------i
Abstract-----------------------------------ii
圖次----------------------------------------v
表次----------------------------------------vi
第一章 緒論----------------------------------1
1.1研究背景與動機----------------------------1
1.2 論文章節內容安排-------------------------5
第二章 相關背景及系統模型--------------------6
2.1 多輸入多輸出系統-------------------------6
2.2 雷達波束圖設計---------------------------7
2.3 深度學習---------------------------------8
2.4 深度展開技術-----------------------------10
第三章 MIMO雷達傳送端波束圖設計--------------12
3.1 系統模型--------------------------------14
3.2 問題制定--------------------------------17
3.3 流形優化演算法---------------------------18
3.4 所提出基於深度展開波束圖演算法------------21
3.5 gamma設計-------------------------------24
第四章 模擬結果與分析------------------------25
4.1 模擬環境設置-----------------------------25
4.2 訓練資料產生與參數設定--------------------26
4.3 模擬比較對象與分析------------------------27
4.4 模擬比較對象-----------------------------28
4.5 計算複雜度之比較分析----------------------34
第五章 結論----------------------------------35
參考文獻-------------------------------------36

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