Spatial-Temporal BEM and Channel Estimation Strategy for Massive MIMO Time-Varying Systems

TL;DR

This paper introduces a DFT-aided spatial-temporal basis expansion model for efficient channel estimation in massive MIMO systems operating in time-varying environments, reducing training and feedback overhead.

Contribution

It proposes a novel ST-BEM approach suitable for TDD and FDD systems, leveraging FFT for efficient deployment and addressing challenges in time-varying massive MIMO channels.

Findings

Significantly reduces training overhead and feedback cost.

Effective in both TDD and FDD systems due to angle reciprocity.

Numerical results validate the proposed scheme's performance.

Abstract

This paper proposes a new channel estimation scheme for the multiuser massive multiple-input multiple-output (MIMO) systems in time-varying environment. We introduce a discrete Fourier transform (DFT) aided spatial-temporal basis expansion model (ST-BEM) to reduce the effective dimensions of uplink/downlink channels, such that training overhead and feedback cost could be greatly decreased. The newly proposed ST-BEM is suitable for both time division duplex (TDD) systems and frequency division duplex (FDD) systems thanks to the angle reciprocity, and can be efficiently deployed by fast Fourier transform (FFT). Various numerical results have corroborated the proposed studies.