Abstract:

Massive Multiple Input Multiple Output（MIMO） systems have massive antennas at base station. The high dimension of the channel makes it difficult for mobile users to accurately estimate downlink channel in real time. To address this issue, a weighted Orthogonal Matching Pursuit（OMP） algorithm is proposed in this paper. Firstly, the estimated signal calculated by the algorithm in each iteration is divided into two parts, the signal estimated from current residual signal and the signal estimated from previous residual signal. Secondly, these two parts of the signal are set with different weights to improve the estimation accuracy under low Signal Noise Ratio（SNR）. Moreover, by adjusting the weight values with iterations, the estimation accuracy of the channel under both low and high SNR can be improved. The simulation results show that the proposed algorithm can obtain higher estimation accuracy than the standard OMP algorithm under different signal-to-noise ratios.

Key words: massive Multiple Input Multiple Output（MIMO）, channel estimation, sparse reconstruction, compressed sensing, orthogonal matching pursuit

摘要：

针对大规模多输入多输出系统基站天线数目众多，移动用户很难实时精确完成信道估计等问题。提出了一种加权的正交匹配追踪算法。该算法在每次迭代过程中，计算得到的估计信号值由当前残差信号估计值和迭代之前估计值两部分组合而成；分别对当前残差信号估计值和迭代之前估计值设置不同的权值，以提高信号在低信噪比情况下的估值精度；通过调整不同迭代次数权值大小，可以提升信号在不同信噪比情况下的计算精度。仿真结果表明，在不同的信噪比情况下，该算法都可以获得比标准正交匹配追踪算法更高的估计精度。

关键词: 大规模多输入多输出（MIMO）, 信道估计, 稀疏重建, 压缩感知, 正交匹配追踪