Title: Channel estimation for hybrid mmWave massive MIMO via low rank Hankel matrix reconstruction

Authors: Yujian Pan; Zongfeng Qi; Jingke Zhang; Feng Wang

Addresses: State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE), Luoyang 471003, China; School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, Zhejiang, China ' State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE), Luoyang 471003, China ' State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE), Luoyang 471003, China ' School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, Zhejiang, China

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.

Keywords: ADMM; channel estimation; hybrid massive MIMO; low rank Hankel matrix reconstruction; nuclear norm minimisation.

DOI: 10.1504/IJWMC.2022.123313

International Journal of Wireless and Mobile Computing, 2022 Vol.22 No.2, pp.131 - 139

Received: 03 Aug 2021
Accepted: 28 Jan 2022
Published online: 08 Jun 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article