Time-Varying Downlink Channel Tracking for Quantized Massive MIMO Networks

TL;DR

This paper introduces a Bayesian channel estimation method for time-varying massive MIMO systems that accounts for quantization effects and exploits channel sparsity and temporal correlations to improve tracking accuracy.

Contribution

It develops a novel sparse Bayesian learning framework combined with EM, GAMP, and clustering techniques for efficient and accurate downlink channel tracking in quantized massive MIMO networks.

Findings

Enhanced channel tracking accuracy demonstrated in simulations

Effective exploitation of sparsity and temporal correlation

Reduced computational complexity through EM and GAMP algorithms

Abstract

This paper proposes a Bayesian downlink channel estimation algorithm for time-varying massive MIMO networks. In particular, the quantization effects at the receiver are considered. In order to fully exploit the sparsity and time correlations of channels, we formulate the time-varying massive MIMO channel as the simultaneously sparse signal model. Then, we propose a sparse Bayesian learning (SBL) framework to learn the model parameters of the sparse virtual channel. To reduce complexity, we employ the expectation maximization (EM) algorithm to achieve the approximated solution. Specifically, the factor graph and the general approximate message passing (GAMP) algorithms are used to compute the desired posterior statistics in the expectation step, so that high-dimensional integrals over the marginal distributions can be avoided. The non-zero supporting vector of a virtual channel is then obtained from channel statistics by a k-means clustering algorithm. After that, the reduced dimensional GAMP based scheme is applied to make the full use of the channel temporal correlation so as to enhance the virtual channel tracking accuracy. Finally, we demonstrate the efficacy of the proposed schemes through simulations.