Compressed channel estimation for MIMO amplify-and-forward relay networks
Compressed channel estimation for MIMO amplify-and-forward relay networks
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DOI：
10.1109/iccchina.2013.6671145
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发表时间：
2013-11
期刊：
影响因子：
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通讯作者：
Aihua Zhang;Guan Gui;Shou-yi Yang
中科院分区：
文献类型：
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作者：
Aihua Zhang;Guan Gui;Shou-yi Yang
在这项工作中，我们研究了多输入多输出（MIMO）合作网络中，采用放大转发（AF）传输方案的信道估计问题。在系统中提出了最小二乘（LS）和期望条件最大化（ECM）。然而，这两种方法都没有充分利用信道稀疏性，从而导致估计性能的损失。与线性信道估计方法不同，本文基于压缩感知理论，提出了几种利用MIMO协作信道稀疏性的压缩信道估计方法。首先利用稀疏分解理论将信道估计问题转化为压缩感知问题。其次，推导了估计的下界，并采用压缩采样匹配追踪（CoSaMP）算法对MIMO中继信道进行重构。最后，通过各种数值仿真验证了所提方法相对于传统线性信道估计方法的优越性。仿真结果表明，我们的双迭代接收机提供了一个很好的BER性能。
In this work, we investigate channel estimation problem in Multi-Input Multi-Output (MIMO) cooperative networks that employ the amplify-and-forward (AF) transmission scheme. Least square (LS) and expectation conditional maximization (ECM) have been proposed in the system. However, both of them never take advantage of channel sparsity and then they cause the estimation performance loss. Unlike the linear channel estimation methods, we propose several compressed channel estimation methods to exploit sparsity of the MIMO cooperative channels based on the theory of compressed sensing. At first, we formulate the channel estimation problem as compressed sensing problem by using sparse decomposition theory. Secondly, the lower bound is derived for the estimation and the MIMO relay channel is reconstructed by compressive sampling matching pursuit (CoSaMP) algorithms. Finally, various numerical simulations are given to confirm the superiority of proposed methods than traditional linear channel estimation methods. Simulation results show that our doubly iterative receiver provides an excellent BER performance.