Massive MU-MIMO Downlink TDD Systems with Linear Precoding and Downlink Pilots

TL;DR

This paper proposes an efficient beamforming training scheme for massive MU-MIMO downlink TDD systems that improves channel estimation at users, reduces overhead, and enhances spectral efficiency, especially in low-mobility scenarios.

Contribution

It introduces a pilot-based channel estimation method at users with overhead independent of BS antennas, and derives capacity bounds for different precoding schemes.

Findings

The scheme reduces channel estimation overhead proportional to the number of users.

Capacity bounds show improved spectral efficiency with the proposed method.

The approach is preferable in moderate and low-mobility environments.

Abstract

We consider a massive MU-MIMO downlink time-division duplex system where a base station (BS) equipped with many antennas serves several single-antenna users in the same time-frequency resource. We assume that the BS uses linear precoding for the transmission. To reliably decode the signals transmitted from the BS, each user should have an estimate of its channel. In this work, we consider an efficient channel estimation scheme to acquire CSI at each user, called beamforming training scheme. With the beamforming training scheme, the BS precodes the pilot sequences and forwards to all users. Then, based on the received pilots, each user uses minimum mean-square error channel estimation to estimate the effective channel gains. The channel estimation overhead of this scheme does not depend on the number of BS antennas, and is only proportional to the number of users. We then derive a lower bound on the capacity for maximum-ratio transmission and zero-forcing precoding techniques which enables us to evaluate the spectral efficiency taking into account the spectral efficiency loss associated with the transmission of the downlink pilots. Comparing with previous work where each user uses only the statistical channel properties to decode the transmitted signals, we see that the proposed beamforming training scheme is preferable for moderate and low-mobility environments.