On hybrid precoder/combiner for downlink mmWave massive MU-MIMO systems

TL;DR

This paper introduces four hybrid precoder/combiner designs for downlink mmWave massive MU-MIMO systems, focusing on interference reduction and improved BER performance by considering colored Gaussian noise in the digital design.

Contribution

It proposes novel hybrid precoder/combiner schemes that account for colored Gaussian noise and reduce interference, which were not addressed in prior work.

Findings

Improved BER performance demonstrated through numerical results.

One hybrid combiner achieves maximum of the objective function per Hadamard's inequality.

Simple detection approach maintains performance with reduced complexity.

Abstract

We propose four hybrid combiner/precoder for downlink mmWave massive MU-MIMO systems. The design of a hybrid combiner/precoder is divided in two parts, analog and digital. The system baseband model shows that the signal processed by the mobile station can be interpreted as a received signal in the presence of colored Gaussian noise, therefore, since the digital part of the combiner and precoder do not have constraints for their generation, their designs can be based on any traditional signal processing that takes into account this kind of noise. To the best of our knowledge, this was not considered by previous works. A more realistic and appropriate design is described in this paper. Also, the approaches adopted in the literature for the designing of the combiner'/precoder' analog parts do not try to avoid or even reduce the inter user/symbol interference, they concentrate on increasing the signal-to-noise ratio (SNR). We propose a simple solution that decreases the interference while maintaining large SNR. In addition, one of the proposed hybrid combiners reaches the maximum value of our objective function according with the Hadamard's inequality. Numerical results illustrate the BER performance improvements resulting from our proposals. In addition, a simple detection approach can be used for data estimation without significant performance loss.