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研究生: 王家男
Ja-Nan Wang
論文名稱: 頻移相位同調光纖通信系統的效能分析
Performance Analysis of Coherent Optical Fiber Systems Using
指導教授: 賀嘉律
Chia-Lu Ho
口試委員:
學位類別: 博士
Doctor
系所名稱: 資訊電機學院 - 電機工程學系
Department of Electrical Engineering
畢業學年度: 89
語文別: 中文
論文頁數: 110
中文關鍵詞: 同調光通訊頻移調變
外文關鍵詞: Coherent optical communication, FSK
相關次數: 點閱:6下載:0
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  • 加入雷射模組作為本地震盪器 (local oscillator),
    引用了光的波動性質, 使得雷射的相位噪音
    (phase noise), 會顯著影響接收機的特性。
    因相位噪音的統計特性不易計算,
    使得同調光纖系統的效能分析變得具有挑戰性。
    其中頻移 (FSK) 調變在同調光纖系統中,
    有兩個不同的範疇: 一種是窄的頻移調變,
    其頻移的差額小於資料傳輸量, 其接收機的形式,
    以相位分散 (phase-diversity) 方式為主。
    另一種是寬的頻移調變,
    其頻移的差額大於兩倍的資料傳輸量,
    則可使用兩個中頻濾波器解調。
    本文即針對這二種調變方式, 提出了效能的分析計算和模擬方法。
    在分析計算方面,
    利用隨機過程上的技巧,
    求得了高斯近似法的收斂解。
    因在數值計算上無累進誤差,
    使之應用於評估各類的操作參數, 和更複雜的系統模型。
    且更進一步,
    求得矩量生成函數 (moment generating function),
    並率先採用特殊變數變換的技巧,
    使得數值積分能夠有效的收斂。
    在模擬方法方面,
    所發展出的模擬方式,
    可由一般的個人電腦執行。
    我們又引入了 importance sampling 的方法,
    使得電腦模擬可以延伸到計算極低的機率。



    are numerical computed efficiently.
    The effects of shot and laser
    phase noise are taken into account.
    For a particular realizations of laser phase noises,
    the BER is expressed as a Laplace
    inversion integral. It is then averaged with respect
    to the distribution of the phase noise. The method of changes
    of variable is used twice in the computation, and the results
    converge rapidly.
    Gaussian approximation of outputs of CPFSK receivers
    is derived.
    However, for ideal and Gaussian frequency shapes,
    Gaussian approximation methods
    are shown to be not so accurate for the case of low BER.
    We have established the models of noises and FSK signals
    in coherent communication systems.
    These simulations can be implemented
    systematically to compute the BER of the receivers and
    histograms of probability distributions of outputs.
    They can also be applied to checking the validity of performance analyzation.
    By using importance sampling method
    we computed the low BER without consuming much
    computer CPU time.
    So we can use these simulation methods to assistant the
    practical system design, which is considered for
    very low BER.
    In fact, it is so efficient
    that we only utilized common personal computers
    as simulation platforms.
    Furthermore, a random number generator with a very long period
    is no longer necessary for the shorter convergent time.
    From the results of computer simulation,
    we found that Gaussian approximation
    overestimate the system performance.
    One reason is that shot noises in coherent frequency
    modulation systems may not obey any type of large number theory
    well enough to utilize Gaussian approximation.

    封面 1Coherent optical fiber system 1.1Introduction 1.2Basic system structures 1.2.1Coherent modulation schemes 1.2.2Coherent demodulatior schemes 1.2.3Optical synchronized schemes 1.3Coherent system structures with FSK scheme 1.4Multichannel optical systems 1.5Performance degradation sorce 1.5.1Laser phase noise and intensity noise 1.5.2Fluctuation of polarization 1.5.3Chromatic dispersion 1.5.4Fiber nonlinear phenomena 1.5.5Optical amplifier noise 1.6Contribution of the dissertation 1.6.1Theoretical analysis 1.6.2Efficient simulation 1.7Architecture of the dissertation 2 System analysis of CPFSK phase-diversity receivers 2.1Introduction 2.2System model 2.3Computing BER by numerical integration 2.4The Gaussian approximation 2.5Analysis for Gaussian frequency pulse shape 2.6The BER floor 2.7Choosing the bandwidth of the low pass filters 2.8Optimum delay time 2.9Moment generating function of phase noise 3 System analysis of asynchronous FSK receivers 3.1Introduction 3.2System model 3.3Electrical filtering 3.4The moment generating function of phase noise 3.5Saddlepoint approximation 4Computer simulations 4.1Introduction 4.2Generation of multivariate Gaussian random variables 4.3Modified Monte Carlo method 4.4Simulation of CPFSK phase-kiversity systems 4.5Simulation of asynchronousFSK systems 5Conclusions A Programs A.1CPFSK Simulation Unde Low BER A.2Gaussian Approximation

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