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Regularized Multipath Matching Pursuit for Sparse Channel Estimation in Millimeter Wave Massive MIMO System

机译:毫米波大规模MIMO系统中稀疏信道估计的正则化多径匹配追踪

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摘要

Sparse channel estimation is investigated for millimeter wave massive MIMO systems, where a base station equipped with a uniform planar array serves several single-antenna users. At first, the 2-D multiuser channel estimation is formulated as several sparse recovery problems. Then a regularized multipath matching pursuit (RMMP) algorithm is proposed for sparse channel estimation. Compared to the existing multipath matching pursuit (MMP) algorithm, a regularization step is introduced in RMMP to screen the candidate paths, which can reduce the computational complexity as well as the storage overhead. Simulation results show that the proposed RMMP algorithm outperforms the existing orthogonal matching pursuit and orthogonal least squares algorithms. In particular, RMMP has the same sparse channel estimation performance as MMP while the computational complexity of the former is much lower than the latter.
机译:针对毫米波大规模MIMO系统研究了稀疏信道估计,在该系统中,配备有统一平面阵列的基站为多个单天线用户提供服务。首先,将二维多用户信道估计公式化为几个稀疏恢复问题。提出了一种用于稀疏信道估计的正则化多径匹配追踪算法。与现有的多路径匹配追踪(MMP)算法相比,在RMMP中引入了一个正则化步骤来筛选候选路径,这可以减少计算复杂性以及存储开销。仿真结果表明,所提出的RMMP算法优于现有的正交匹配追踪算法和正交最小二乘算法。特别地,RMMP具有与MMP相同的稀疏信道估计性能,而前者的计算复杂度远低于后者。

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