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Channel Estimation for Finite Scatterers Massive Multi-User MIMO System

机译：有限散射体大规模多用户MIMO系统的信道估计

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摘要

In finite scattering propagation environment, when the number of scatterers is very small compared to the number of base station antennas and users in the cell and if the same scatterers are shared by all users, then the correlation among the users increases. Hence, the high-dimensional multi-user MIMO system is likely to have low-rank channel. To estimate the channel matrix, weighted nuclear norm minimization method is proposed in this paper. Iterative weighted singular value thresholding algorithm is used to solve the optimization problem. To recover the low-rank channel, a partial random Fourier matrix (PRFM) satisfying the restricted isometric property is adapted as the training matrix. The PRFM forces the iterative algorithm to converge in one iteration which reduces the huge computational complexity of the proposed channel estimation method. The mean square error and achievable sum rate are the criteria used to measure the performance of the proposed method. The results show that the proposed method outperforms the least square estimation method and the nuclear norm minimization method for various finite scatterers in different SNR levels.
机译：在有限散射传播环境中，当散射体的数量与小区中基站天线和用户的数量相比非常小时，并且如果所有用户共享相同的散射体，则用户之间的相关性会增加。因此，高维多用户MIMO系统可能具有低秩信道。为了估计信道矩阵，本文提出了加权核范数最小化方法。迭代加权奇异值阈值算法用于解决优化问题。为了恢复低秩信道，将满足受限等距特性的部分随机傅里叶矩阵（PRFM）用作训练矩阵。 PRFM强制迭代算法收敛一次迭代，从而降低了所提出的信道估计方法的巨大计算复杂性。均方误差和可达到的总和比率是用来衡量所提出方法性能的标准。结果表明，对于不同信噪比水平下的各种有限散射体，该方法优于最小二乘估计法和核规范最小化法。

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