室內無線電快速類神經網路分封等化器設計

Abstract

在本論文中, 我們利用一種基於類神經網路的偵測回饋等化器(decision feed back equalizer),以克服室內無線電通道的多重路徑衰退 (multipath fading)問題。並提出一種新的快速分封式二極狀態逆向傳遞 演算法(fast packet bipolar-state back propagation, fast PBSBP), 可以提高等化器的收斂速度,以及提供追蹤通道時變因素的能力。此外,由 於採用實數向量表示法處理複變數訊號, 可避免複變數運算與奇異點( singularity)問題,以簡化等化器結構與計算複雜度。經由電腦模擬的結 果得知,新式的演算法可以使類神經網路等化器達到更快的收斂速度與位 元錯誤率,因此更能符合室內高速通訊的需求。 In this thesis, a new decision feedback equalizer (DFE) based on neural network is proposed to overcome the multipath fading problem of the indoor radio channel. And the fast packet bipolar-state back propagation (fast PBSBP) algorithm is proposed for the training of the neural-based DFE. This algorithm is featured as: (1) high convergence rate, and (2) capable of tracking the time variations of the channel charateristics. Moreover, we use 2-D real-vector representation to process the complex-valued signals, thus no complex operation is needed and the problem of singularity can be avoided. For this reason, the complexity of the DFE architecture and the complexity of computation can be both reduced. From the computer simulation results, it can be shown that the new equalizer with the fast PBSBP algorithm can achieve lower error and lower bit error rate than the traditional DFE and training algorithm.