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研究生： 郭凌均
Kuo, Ling-Chun
論文名稱： 使用深度學習之無線通訊系統的通道估測方法
A Channel Estimation Scheme Using Deep Learning for Wireless Communication Systems
指導教授： 王晉良
Wang, Chin-Liang
口試委員: 馮世邁
Phoong, See-May
鐘嘉德
Chung, Char-Dir
歐陽源
Ouyang, Yuan
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： 2022
畢業學年度： 110
語文別： 英文
論文頁數： 23
中文關鍵詞： 通道估計 、深度學習 、無線通訊系統
外文關鍵詞： Channel Estimation, Deep Learning, Wireless Communication Systems
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    • 在本論文中，我們針對無線通訊系統提出一個基於深度學習(DL)的通道估測方法，其中通道的時間/頻率響應被視為符合3GPP進階長期演進技術(LTE-A)規範的二維圖像資料；所提出之DL通道估測方法被稱為AttenChNet，乃由一個具有超分辨率之卷積神經網路(CNN)以及一個包含一個自注意力層和幾個全連接層的神經網路所組成。我們首先依據LTE-A規範之參考訊號安排方式，並利用傳統的最小平方(LS)演算法和適當的內插方法產生初步通道估測結果；接著，產生足夠大量的初步通道估測結果以組成一個資料集，再據以對AttenChNet進行離線訓練，然後將訓練好的模型應用至線上階段。基於各種通道模型之電腦模擬結果顯示，與傳統的LS通道估測演算法以及一個被稱為ChannelNet (由一個具有超分辨率的CNN與一個去噪CNN所組成)的相關作法相比，所提出之AttenCNet可提供明顯較佳的通道估測均方誤差效能。

    In this thesis, we propose a channel estimation scheme based on deep learning (DL) for wireless communication systems. It treats the time/frequency response of a channel as two-dimensional image data that conforms to the 3GPP Long Term Evolution-Advanced (LTE-A) specifications. The DL-based scheme is referred to as AttenChNet that is composed of a super-resolution convolutional neural network (CNN) and a neural network with a self-attention layer and several fully-connected layers. According to the LTE-A reference signals’ arrangement, preliminary channel estimation results are generated by using the conventional least squares (LS) algorithm and an appropriate interpolation method. The AttenChNet is trained offline by a sufficiently large dataset with each element formed by such preliminary channel estimates, and the well-trained model is then applied at the online stage. Simulation results based on various channel models demonstrate that the proposed AttenCNet offers much better channel estimation performance in terms of the mean-squared error, as compared with the conventional LS algorithm and a previous related ChannelNet formed by a super-resolution CNN and a denosing CNN.

    Abstract Contents List of Figures List of Tables I. Introduction----------------------------------------1 II. System Model---------------------------------------4 A. Transmitted Signal Architecture-------------------4 B. Downlink FDD-LTE System---------------------------5 III. Proposed Method-----------------------------------7 A. Structure of Convolutional Neural Network (CNN)---7 B. AttenChNet----------------------------------------8 C. Time Complexity----------------------------------13 IV. Simulations---------------------------------------15 A. Simulation Settings------------------------------15 B. Simulation Results-------------------------------17 V. Conclusion-----------------------------------------21 References--------------------------------------------22

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