在數位傳輸的通道中,有著一些無法以傳統AWGN來表達的脈衝環境,我們稱之為脈衝雜訊,常見的有Class A model和Bernoulli-Gaussian通道模型。但以上兩種雜訊皆屬於無記憶型,無法確切的表達出實際通道的特性(i.e. 連續性的脈衝雜訊),因此就衍生出基於馬可夫鏈特性的記憶型Markov - Gaussian(MG)通道模型。此外,在接收端解碼的過程中所知道脈衝雜訊的統計資訊有限,因此本文也將在未知資訊的條件下進行模擬。

許多文獻皆是針對改進維特比演算法中分支度量以對抗脈衝雜訊的影響,這也是在不改變解碼器架構下最有效的方法。一般針對無記憶型通道使用維特比演算法做解碼時不需考慮到傳輸通道中的資訊,只頇著重在求出各分支最大似然總和並選取最恰當的路徑來還原傳輸訊號即可,雖然能有不錯的位元錯誤率但流失掉的通道資訊實在可惜。基於以上理由,我們只需在維特比演算法中的格狀圖增加通道狀態和虛擬狀態的概念就能充分利用這些流失的資訊。

本文主要利用電腦軟體以Markov-Gaussian通道模型來模擬傳送通道中的脈衝雜訊環境,並使用BPSK調變的方式,搭配常見的迴旋碼編碼器和維特比演算 法,並增加通道狀態和虛擬狀態及改進分支度量來達到降低位元錯誤率(BER)的 目標。

It is well known that communication systems are prone to impulse noise; it becomes more and more common to have coexistent systems overlap partial or full bandwidth, inevitably introducing interference to each other if no sophisticated coordinating mechanism is enabled. Nevertheless, the cost arising from the refined coordinating algorithm can be mounting, especially when the number of devices increases to a certain extent, plaguing the coordinator to, if not impossible, maintain a stable network.

In this thesis, a self-arbitrating mechanism is introduced to a low-cost communication device in the presence of impulse noise, which can either instantly occur in each time instant or take place based on whether or not the occurrence of impulse was true in the previous time instant. The study aims at blunting the effect of occurrence of impulses while the statistics of impulse noise is not assumed at the decoder. In the scenario regarding the memory channel noise model, a first-order Markov chain, characterized by the transition probabilities and the probability of impulse occurrence, is used. Without assuming the aforementioned probabilities as well as the power strength of impulse noise, the decoder, additionally taking into account the noise (or channel) state, implements a two-dimensional trellis search owing to virtual state’s help, in a manner similar to the Viterbi algorithm. When compared with other existing methods forgoing the statistics of impulse under the same simulation setups, the proposed decoding algorithm enjoys several decibel gain in terms of signal-to-noise ratio at a bit error probability of 1.0E-5 . Furthermore, the proposed decoder is attested to be robust in numerous scenarios and even performs fairly close to the maximum likelihood decoder, which nevertheless assumes the statistics of impulse are available.

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