EM-based parameter iterative approach for sparse Bayesian channel estimation of massive MIMO system

Sulin Mei,Yong Fang

doi:10.1186/s13638-017-0967-3

Abstract

One of the main challenges for a massive multi-input multi-output (MIMO) system is to obtain accurate channel state information despite the increasing number of antennas at the base station. The Bayesian learning channel estimation methods have been developed to reconstruct the sparse channel. However, these existing methods depend heavily on the channel distribution. In this paper, based on sparse Bayesian method, an expectation maximization-based parameter iterative approach is proposed to estimate the massive MIMO channel with unknown channel distribution. Using the approximate sparse feature, the massive MIMO channel is modeled as a non-zero Gaussian mixture and the sparse Bayesian channel estimation is introduced. The channel marginal probability density function is expressed by using the general approximate message-passing algorithm. All of the required channel parameters are iteratively estimated by the EM method. Simulation results show that the proposed scheme enables evident performance in channel estimation accuracy with a lower complexity when channel distribution is unknown.

Highlights

Massive multi-input multi-output (MIMO) [1], which equipping a large number antennas at the base station (BS) to simultaneously serve tens of users in the same time-frequency channel, is widely considered as one of the key techniques for future communication network
All of the parameters needed for expectation maximization (EM) update are computed by the general approximate message passing (GAMP) [9, 10] algorithm, which provides a huge gain in reducing the computational complexity
5 Conclusions To obtain an accurate channel state information (CSI) of massive MIMO system, we propose an EM-based parameter iterative approach based on sparse Bayesian method

Summary

Introduction

Massive multi-input multi-output (MIMO) [1], which equipping a large number antennas at the base station (BS) to simultaneously serve tens of users in the same time-frequency channel, is widely considered as one of the key techniques for future communication network Such systems can greatly improve the system capacity and energy efficiency by exploiting the increased degree of spatial freedom. By exploiting the temporal correlation and the sparsity of massive MIMO channels in timedomain, [4] applied a sparse channel estimation scheme and sharply reduced the pilot overhead. An expectation maximization (EM)-based parameter iterative approach is proposed to estimate the massive MIMO channel based on sparse Bayesian method. E{·} and δ(·) denote the expectation operator and Dirac delta, respectively

Sparse channel model

EM-based parameter iterative approach for sparse Bayesian channel estimation

EM-GAMP algorithm

Simulation results

Findings

Conclusions