Multiuser MIMO Sequential Beamforming with Full-duplex Training

TL;DR

This paper introduces a full-duplex based sequential beamforming method for multiuser MIMO systems, reducing training overhead and enhancing spectral efficiency, especially in moderate to high SNR regimes.

Contribution

It proposes a novel full-duplex sequential beamforming strategy that improves spectral efficiency by reducing CSI training overhead in multiuser MIMO systems.

Findings

Achieves up to 130% spectral efficiency improvement with closed-loop training.

Derives optimal training durations and characterizes spectral efficiency in different SNR regimes.

Demonstrates performance gains using real 3GPP channel data in LTE systems.

Abstract

Multiple transmitting antennas can considerably increase the downlink spectral efficiency by beamforming to multiple users at the same time. However, multiuser beamforming requires channel state information (CSI) at the transmitter, which leads to training overhead and reduces overall achievable spectral efficiency. In this paper, we propose and analyze a sequential beamforming strategy that utilizes full-duplex base station to implement downlink data transmission concurrently with CSI acquisition via in-band closed or open loop training. Our results demonstrate that full-duplex capability can improve the spectral efficiency of uni-directional traffic, by leveraging it to reduce the control overhead of CSI estimation. In moderate SNR regimes, we analytically derive tight approximations for the optimal training duration and characterize the associated respective spectral efficiency. We further characterize the enhanced multiplexing gain performance in the high SNR regime. In both regimes, the performance of the proposed full-duplex strategy is compared to the half-duplex counterpart to quantify spectral efficiency improvement. With experimental data [1] and 3D channel model [2] from 3GPP, in a 1.4 MHz 8X8 system LTE system with the block length of 500 symbols, the proposed strategy attains a spectral efficiency improvement of 130% and 8% with closed and open loop training, respectively.