本文主要目標在探討深度學習極化碼(Polar Code)解碼器是否可以在脈衝雜訊(Impulse Noise)干擾下，能透過全連接式神經網路的學習，讓接收器可以有更好的更正錯誤能力。
對於有線通訊或是無線通訊，在傳輸的過程中，脈衝雜訊的出現與干擾是無可避免的，脈衝雜訊瞬間強力的干擾不但會破壞傳送中的訊號，更會使得接收端在收到傳輸訊號後解讀錯誤的機率大增。
本文將介紹雜訊通道模型有兩種 : 第一種是常見的可加性高斯白雜訊(Additive White Gaussian Noise, AWGN)通道模型，第二種是白努利-高斯脈衝雜訊(Bernoulli-Gaussian impulse, BG-impulse)通道模型。
本文將使用全連接式深度神經網路的架構，來模擬訊號經過極化碼(Polar Code)編碼後，通過雜訊通道干擾後解碼的性能。以AWGN所訓練出來的模型作為比較，再介紹微調(Fine-Tuning)的訓練方法，來訓練脈衝雜訊模型，提升受脈衝干擾的抵抗性。

The main goal of this paper is to investigate whether a deep learning Polar Code decoder can provide better error correction capability to the receiver by learning from a fully-connected neural network under the interference of Impulse Noise.
For wired or wireless communications, the appearance of impulse noise and interference during transmission is inevitable. The transient and powerful interference of impulse noise not only destroys the transmitted signal, but also increases the chance of misinterpretation at the receiving end after receiving the transmitted signal.
In this paper, we introduce two types of noise channel models: the first is the common Additive White Gaussian Noise (AWGN) channel model, and the second is the Bernoulli-Gaussian impulse(BG-impulse) channel model.
In this paper, we use a fully-connected deep neural network (FCDNNs) architecture to simulate the performance of decoding signals after they have been encoded by polarized codes and interfered by noise channels. The model trained by AWGN is used as a comparison, and Fine-Tuning is introduced to train the impulse noise model to improve the resistance to pulse interference.

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