Blind Pilot Decontamination in Massive MIMO by Independent Component Analysis
Blind Pilot Decontamination in Massive MIMO by Independent Component Analysis
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DOI：
10.1109/glocomw.2017.8269156
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发表时间：
2017-12
期刊：
影响因子：
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通讯作者：
Ebrahim Amiri;R. Müller;W. Gerstacker
中科院分区：
文献类型：
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作者：
Ebrahim Amiri;R. Müller;W. Gerstacker
研究了具有导频污染的多输入多输出大规模系统。为了提高信道估计的性能，提出了一种基于复独立分量分析（cICA）的盲数据辅助信道估计方法，该方法以互信息率为对比度函数。该方法首先利用子空间投影抑制干扰和噪声，然后盲提取上行数据。最后，基于所估计的感兴趣小区中所有用户的上行链路数据，获得信道的最小二乘估计。仿真结果表明：1）随着接收天线数量和数据长度的增加，导频污染效应逐渐减弱; 2）在所考虑的场景下，该方法的性能优于基于峰度的cICA和纯子空间投影。
A massive multiple-input multiple-output system with pilot contamination is considered. In order to improve channel estimation we propose a blind data-aided method based on complex independent component analysis (cICA) with the aid of mutual information rate as contrast function. In this approach, interference and the noise are first suppressed by subspace projection, then the uplink data is extracted blindly. Finally, based on the estimated uplink data of all the users in the cell of interest, the least square estimate of the channel is obtained. Simulation results confirm that 1) the pilot contamination effect decays, as the number of receive antennas and the data length increase; and 2) that the proposed approach outperforms both kurtosis-based cICA and pure subspace projection for the considered scenarios.