光網絡具有傳輸速率高，大頻寬，抗電磁干擾，傳輸損耗低等優點。我們採用光分碼多址(OCDMA)中的頻譜幅度編碼(SAC)作為我們的系統架構。
在這篇論文中，我們的目標是利用智能運算以減輕來自高斯分佈的雜訊和雜亂的累積多載波傳輸干擾。我們將介紹兩種智能運算方法。一種是遞歸干擾消除(RIC)，該機制基於多階段的相關解碼和平衡檢測以抑制雜訊，RIC從概念上包含三個階段：雜訊減輕，逐碼比較和數據解碼。且我們提出了一種深度學習干擾消除(DLIC)的方法，它由卷積神經網絡(CNN)和一些深度學習概念組成。DLIC從概念上包含三個階段：干擾信號資訊抽取，信號重建和數據解碼。
我們將比較這兩種智能計算方法，並評估這兩種方法在光碼存取接收器的效能。兩種方法的模擬比較圖會由信噪比(SNR)與誤碼率(BER)的圖表加以呈現。

Optical network has a lot of advantage such as high transmit rate, large bandwidth, immunity to electromagnetic interference, low transmission loss and so on. We adopt Spectral-Amplitude Coding (SAC) of Optical code-division multiple-access (OCDMA) as our system framework.
In the thesis, we aim at intelligent computations to mitigate Gaussian distribution noises and interference from noisy accumulated multi-carrier transmissions. Two methods would be introduced. One is recursive interference cancelation (RIC) which is based on multi-stage correlation decoding and balanced detection to suppress the noise. The RIC conceptually consists of three phase: Noise Mitigation, Code-by-Code Comparison and Data decoding. We proposed the deep learning interference cancellation (DLIC) which is the other method that it composed of convolutional neural network (CNN) and some deep learning concept. Also, the DLIC conceptually consists of three phase: Noisy signal extraction, Signal reconstruction and Data decoding.
We will compare the both intelligent computations method and evaluate the performance over optical coding access receiver. The comparison of both methods in simulation would be presented as the chart of signal-to-noise ratio (SNR) versus bit error rate (BER).

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