Gui Guan, Chen Zhang-xin, Xu Li, Wan Qun, Huang Jiyan, Adachi Fumiyuki
Department of Electronics and Information Systems, Akita Prefectural University, Akita 015-0055, Japan.
Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
ScientificWorldJournal. 2014;2014:274897. doi: 10.1155/2014/274897. Epub 2014 Jun 24.
Channel estimation problem is one of the key technical issues in sparse frequency-selective fading multiple-input multiple-output (MIMO) communication systems using orthogonal frequency division multiplexing (OFDM) scheme. To estimate sparse MIMO channels, sparse invariable step-size normalized least mean square (ISS-NLMS) algorithms were applied to adaptive sparse channel estimation (ACSE). It is well known that step-size is a critical parameter which controls three aspects: algorithm stability, estimation performance, and computational cost. However, traditional methods are vulnerable to cause estimation performance loss because ISS cannot balance the three aspects simultaneously. In this paper, we propose two stable sparse variable step-size NLMS (VSS-NLMS) algorithms to improve the accuracy of MIMO channel estimators. First, ASCE is formulated in MIMO-OFDM systems. Second, different sparse penalties are introduced to VSS-NLMS algorithm for ASCE. In addition, difference between sparse ISS-NLMS algorithms and sparse VSS-NLMS ones is explained and their lower bounds are also derived. At last, to verify the effectiveness of the proposed algorithms for ASCE, several selected simulation results are shown to prove that the proposed sparse VSS-NLMS algorithms can achieve better estimation performance than the conventional methods via mean square error (MSE) and bit error rate (BER) metrics.
信道估计问题是采用正交频分复用（OFDM）方案的稀疏频率选择性衰落多输入多输出（MIMO）通信系统中的关键技术问题之一。为了估计稀疏MIMO信道，将稀疏不变步长归一化最小均方（ISS-NLMS）算法应用于自适应稀疏信道估计（ACSE）。众所周知，步长是控制算法稳定性、估计性能和计算成本这三个方面的关键参数。然而，传统方法容易导致估计性能损失，因为ISS不能同时平衡这三个方面。在本文中，我们提出了两种稳定的稀疏可变步长NLMS（VSS-NLMS）算法，以提高MIMO信道估计器的精度。首先，在MIMO-OFDM系统中阐述了ACSE。其次，将不同的稀疏惩罚引入到用于ACSE的VSS-NLMS算法中。此外，还解释了稀疏ISS-NLMS算法和稀疏VSS-NLMS算法之间的差异，并推导了它们的下限。最后，为了验证所提出的算法对ACSE的有效性，给出了几个选定的仿真结果，以证明所提出的稀疏VSS-NLMS算法通过均方误差（MSE）和误码率（BER）指标可以实现比传统方法更好的估计性能。
