Channel estimation for hybrid mmWave massive MIMO via low rank Hankel matrix reconstruction Online publication date: Wed, 08-Jun-2022
by Yujian Pan; Zongfeng Qi; Jingke Zhang; Feng Wang
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 22, No. 2, 2022
Abstract: The underdetermined model in the hybrid massive multiple-input and multiple-output (MIMO) brings challenges to channel estimation. This paper proposes a low rank Hankel matrix reconstruction based method for this problem. First, the channel is modelled as a superposition of finite complex exponential functions based on the millimetre wave (mmWave) channel sparsity in angular domain. Then, channel estimation is converted into seeking a low rank Hankel matrix of the channel. For the low rank matrix reconstruction, an inequality constrained nuclear norm minimisation problem is built, and an efficient Alternating Direction Method of Multipliers (ADMM) based algorithm is derived for solving this problem. The new method estimates the channel using only one pilot. It is gridless, efficient, free of path number estimation, and has no minimum angle separation requirement. Its performances are verified by simulations and compared with representative algorithms.
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