Gui Guan, Xu Li, Shan Lin, Adachi Fumiyuki
Department of Communications Engineering, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan.
Faculty of Systems Science and Technology, Akita Prefectural University, Akita 015-0055, Japan.
ScientificWorldJournal. 2014;2014:927894. doi: 10.1155/2014/927894. Epub 2014 Apr 10.
In orthogonal frequency division modulation (OFDM) communication systems, channel state information (CSI) is required at receiver due to the fact that frequency-selective fading channel leads to disgusting intersymbol interference (ISI) over data transmission. Broadband channel model is often described by very few dominant channel taps and they can be probed by compressive sensing based sparse channel estimation (SCE) methods, for example, orthogonal matching pursuit algorithm, which can take the advantage of sparse structure effectively in the channel as for prior information. However, these developed methods are vulnerable to both noise interference and column coherence of training signal matrix. In other words, the primary objective of these conventional methods is to catch the dominant channel taps without a report of posterior channel uncertainty. To improve the estimation performance, we proposed a compressive sensing based Bayesian sparse channel estimation (BSCE) method which cannot only exploit the channel sparsity but also mitigate the unexpected channel uncertainty without scarifying any computational complexity. The proposed method can reveal potential ambiguity among multiple channel estimators that are ambiguous due to observation noise or correlation interference among columns in the training matrix. Computer simulations show that proposed method can improve the estimation performance when comparing with conventional SCE methods.
在正交频分复用（OFDM）通信系统中，由于频率选择性衰落信道会在数据传输过程中导致严重的码间干扰（ISI），因此接收机需要信道状态信息（CSI）。宽带信道模型通常由极少数主导信道抽头来描述，并且可以通过基于压缩感知的稀疏信道估计（SCE）方法来探测这些抽头，例如正交匹配追踪算法，该算法可以有效地利用信道中的稀疏结构作为先验信息。然而，这些已开发的方法容易受到噪声干扰和训练信号矩阵列相关性的影响。换句话说，这些传统方法的主要目标是捕获主导信道抽头，而不报告后验信道不确定性。为了提高估计性能，我们提出了一种基于压缩感知的贝叶斯稀疏信道估计（BSCE）方法，该方法不仅可以利用信道稀疏性，还可以在不增加任何计算复杂度的情况下减轻意外的信道不确定性。所提出的方法可以揭示多个信道估计器之间由于观测噪声或训练矩阵列之间的相关干扰而产生的潜在模糊性。计算机仿真表明，与传统的SCE方法相比，所提出的方法可以提高估计性能。
