Time Varying Channel Tracking with Spatial and Temporal BEM for Massive MIMO Systems

TL;DR

This paper introduces a novel channel tracking method for massive MIMO systems that leverages a spatial-temporal basis expansion model and Markov process learning to efficiently track time-varying channels with reduced complexity.

Contribution

The paper proposes a new spatial-temporal basis expansion model combined with EM-based user movement learning for improved channel tracking in massive MIMO systems.

Findings

Effective reduction of channel dimension using the basis expansion model

Blind tracking of spatial information via Taylor series expansion

Significant complexity reduction in down-link channel estimation

Abstract

In this paper, we propose a channel tracking method for massive multi-input and multi-output systems under both time-varying and spatial-varying circumstance. Exploiting the characteristics of massive antenna array, a spatial-temporal basis expansion model is designed to reduce the effective dimensions of up-link and down-link channel, which decomposes channel state information into the time-varying spatial information and gain information. We firstly model the users movements as a one-order unknown Markov process, which is blindly learned by the expectation and maximization (EM) approach. Then, the up-link time varying spatial information can be blindly tracked by Taylor series expansion of the steering vector, while the rest up-link channel gain information can be trained by only a few pilot symbols. Due to angle reciprocity (spatial reciprocity), the spatial information of the down-link channel can be immediately obtained from the up-link counterpart, which greatly reduces the complexity of down-link channel tracking. Various numerical results are provided to demonstrate the effectiveness of the proposed method.