毫米波多輸入多輸出系統中使用深度學習技術應用於混合預編碼與合併器設計

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姓名

宋嘉喆(Jia-Jhe Song) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

毫米波多輸入多輸出系統中使用深度學習技術應用於混合預編碼與合併器設計
(Hybrid Precoding and Combining Designs by Using Deep Learning in Millimeter Wave MIMO Systems)

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至系統瀏覽論文 (2025-9-1以後開放)

摘要(中)

本論文將深度學習(DL)技術結合於毫米波多輸入多輸出系統中的混合預編碼與合併器設計，透過適當的訓練過程可以提升預測的準確性。我們的訓練資料包含毫米波通道以及射頻預編碼和射頻合併器矩陣。藉由大量的訓練資料與Adam演算法根據當前參數所得到的均方誤差(MSEs)調整參數，以此提升神經網路預測的準確性。訓練結束後將測試集通道餵入訓練好的神經網路(NN)並得到預測結果也就是射頻預編碼與射頻合併器的相位，在給定一個射頻預編碼的情況下我們可以利用最小平方法求解獲得基頻預編碼，相似的方法應用在射頻合併器得到基頻合併器矩陣。在不同數據流數量的情況下，根據深度神經網路的結果計算頻譜效率並從頻譜效率中可以說明我們設計的方法是有競爭力的。

摘要(英)

In this thesis, we apply deep learning (DL) techniques to solve hybrid precoding and combining design problems in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. To increase the accuracy of prediction is achieved through an appropriately training process. Our training data set includes mmWave channels, and solutions of RF analog precoders along with combiners. By utilizing a lot of training data and the Adam optimizer to adjust the parameters based on the mean square errors (MSEs), we can improve the accuracy of prediction. After training process, we feed testing data set into neural network (NN) and obtain the solutions of the phases of RF analog precoder and combiner. Given the RF analog precoder, we can acquire baseband precoder by using least square solutions; and the similar methodology is applied to the design of the RF analog combiner along with acquiring the baseband combiner. In the cases of different numbers of data streams, we calculate the spectral efficiency based on the outputs of DNN in the simulations results, and it can be observed that our method is competitive to the existing schemes.

關鍵字(中)

★ 混合預編碼與合併器
★ 多輸入多輸出
★ 深度學習

關鍵字(英)

論文目次

論文摘要 i
Abstract ii
致謝 iii
Contents iv
List of Figures vi
List of Tables vii
Chapter1. Introduction 1
1.1 Precoding 1
1.2 Hybrid Precoding and Combining 1
1.3 Deep Learning 2
1.4 Organization 3
1.5 Abbreviations 4
1.6 Notation 4
Chapter2. System model and Training data 6
2.1 Deep Neural Network 6
2.2 Input data 8
2.3 Labels 10
Chapter 3. Propose Scheme 16
3.1 Weights and Biases 16
3.2 Adam Optimizer 16
3.3 Dropout 17
3.4 Training Process 18
3.5 Baseband Precoder and Combiner 22
Chapter 4. Simulation Results 24
4.1 Results 24
4.2 Algorithm 29
Chapter 5. Conclusion 31
Reference 32

參考文獻

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指導教授

陳永芳

審核日期

2020-8-18

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