由於無線通訊技術的成熟與人們對網路需求量的增加，下一代行動通訊技術5G預期將要比現今的4G行動通訊技術有更高的資料量、更低的延遲、更低的成本與涵蓋更多的使用者，因此，如物聯網或觸覺互聯網(tactile Internet)等新興技術得以蓬勃發展。然而，越多的服務將提高系統的複雜度，與多個簡單的同質網路(homogeneous network)相比，不同的服務或協議組成的異質網路(heterogeneous network)提供更高的靈活性及成本效益，因此，近年來，異質網路成為增加資料容量與覆蓋範圍的方案之一。在本論文中，以偏振態多工(polarization division multiplexing, PDM)與密集分波多工（dense wavelength division multiplexing, DWDM）來實現多服務的毫米波異質網路。在分波多工中，相同的波長通道內可再透過兩個正交偏振態傳輸不同的信號。因此，數據容量增加了一倍。但是，偏振態解多工技術通常採用複雜的技術，例如偏振態追蹤(polarization tracking)或多輸入多輸出（multi-input multi-output, MIMO）偏振態估計，這些技術需要訓練來自兩種偏振態的訓練數據進行解多工，這降低了系統的靈活性也增加了接收端的硬件成本。基於實驗至之前的研究，提出了偏振態無感的PDM/DWDM整合系統，並使用基於人工神經網路(artificial neural network, ANN)的等化器改善其表現。實驗分別在back-to-back、25公里單模光纖與3米無線通道下傳輸，比較了PDM、DWDM、PDM/DWDM整合系統的位元錯誤率(bit error rate, BER)的表現，驗證了此PDM/DWDM光纖無線整合系統在未來多服務異質網路架構下的可行性。

Due to the maturity of wireless communication technology and the increasing demands of network, the next generation mobile communication technology, 5G is expected to have higher data capacity, higher coverage, lower latency and lower cost than 4G mobile communication technology. Therefore, emerging technologies such as Internet of Things or tactile Internet have evolved. However, multi-services will increase the complexity of the system. Compare to multiple simple homogeneous networks, heterogeneous network that consists of different services or protocols provide greater flexibility and cost efficiency. Therefore, in the recent years, heterogeneous network (HetNet) has emerged as a new trend to enhance the data capacity and the coverage. A HetNet is a network consisting of different services or protocols. In this paper, we have implemented polarization division multiplexing (PDM) and dense wavelength division multiplexing (DWDM) to realize a multi-service mm-wave heterogeneous network. In a DWDM system, different PDM signals can be transmitted through two orthogonal polarization states in the same wavelength channel. Therefore, data capacity has doubled. However, PDM demultiplexing technologies typically employ laborious technologies, such as polarization tracking or multi-input multi-output (MIMO) polarization estimation. These demultiplexing techniques require training sequences from two polarization states, which reduces the flexibility of the system and increases the hardware cost of the receiver. Based on our previous researches, we proposed a polarization-insensitive PDM/DWDM system with neural network-based equalizer. The experiments transmitted over back-to-back, 25km single-mode fiber and 3m wireless link, and the performances of PDM, DWDM and PDM/DWDM systems are discussed. Results shows that the proposed system could possibly be used in future multi-service heterogeneous network.

第1章 緒論 1
1.1 前言 1
1.2 研究動機與目的 2
1.3 論文架構 3
第2章 調變形式介紹 4
2.1 正交分頻多工(Orthogonal frequency division multiplexing, OFDM) 4
2.1.1 OFDM數位訊號的產生與接收 6
2.2 偏振態多工(Polarization division multiplexing, PDM) 8
2.3 分波多工(Wavelength division multiplexing, WDM) 10
第3章 實驗想法與理論 12
3.1 偏振態無感的PDM/DWDM傳送機制 12
3.2 光學濾波器解偏振多工原理 13
3.3 機器學習 16
第4章 人工神經網路 18
4.1 人工神經網路架構 18
4.2 損失函數(Loss function) 21
4.3 反向傳播(Back propagation, BP) 24
4.4 梯度下降(Gradient descent) 28
4.5 基於人工神經網路的等化器 31
第5章 實驗架構與結果 35
5.1光纖與無線整合網路 35
5.2 PDM系統的實驗架構與結果 38
5.2.1 Back-to-back與25km SMF的實驗表現 38
5.2.2 無線通道的實驗表現 46
5.3 DWDM系統的實驗架構與結果 49
5.3.1 Back-to-back與25km SMF實驗架構設計 49
5.3.2 無線通道下的實驗架構設計 51
5.3.3 結果討論 52
5.4 PDM與DWDM的整合(PDM/DWDM) 53
5.4.1 Back-to-back與25km SMF實驗架構設計 53
5.4.2 無線通道實驗架構設計 55
5.4.3結果討論 56
第6章 結論 59
參考文獻 60

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