GAMP-SBL-based channel estimation for millimeter-wave MIMO systems

Jianfeng Shao,Xianpeng Wang,Ting Su,Xiang Lan,Zhiguang Han

doi:10.1186/s13634-021-00792-w

Jianfeng Shao, Xianpeng Wang + Show 3 more

Open Access

https://doi.org/10.1186/s13634-021-00792-w

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Abstract

Based on the finite scattering characters of the millimeter-wave multiple-input multiple-output (MIMO) channel, the mmWave channel estimation problem can be considered as a sparse signal recovery problem. However, most traditional channel estimation methods depend on grid search, which may lead to considerable precision loss. To improve the channel estimation accuracy, we propose a high-precision two-stage millimeter-wave MIMO system channel estimation algorithm. Since the traditional expectation–maximization-based sparse Bayesian learning algorithm can be applied to handle this problem, it spends lots of time to calculate the E-step which needs to compute the inversion of a high-dimensional matrix. To avoid the high computation of matrix inversion, we combine damp generalized approximate message passing with the E-step in SBL. We then improve a refined algorithm to handle the dictionary matrix mismatching problem in sparse representation. Numerical simulations show that the estimation time of the proposed algorithm is greatly reduced compared with the traditional SBL algorithm and better estimation performance is obtained at the same time.

Highlights

In the 5G cellular communication, millimeter wave has been drawing enormous attention from academia, colleges and governments due to the wide available spectrum [1, 2]
We will prove the performance of the proposed algorithm superioritybased channel estimation scheme through MATLAB simulation with the following parameters
Each item of the transmitted pilots X is defined as xi,j = ρ Nt ejβi,j, where ρ is the transmitted power, βi,j is the random phase uniformly dirstributed in [0, 2π )

Summary

Introduction

In the 5G cellular communication, millimeter wave (mmWave) has been drawing enormous attention from academia, colleges and governments due to the wide available spectrum [1, 2]. One solution to the power loss problem in the channel is to use large MIMO antennas. For both the base station and the mobile station, large antennas are equipped to get transceiver powerful communication. Another challenge is that the conventional training overhead for the channel state information (CSI) acquisition grows proportionally with the BS and MS antenna size. With the uniform linear array (ULA) assumption, the mmWave channel can be approximately transformed into the sparse representation under the discrete Fourier transform (DFT) basis when large-scale antennas are equipped [6,7,8].

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Conclusion