Enhancing Multi-Stream Beamforming Through CQIs For 5G NR FDD Massive MIMO Communications: A Tuning-Free Scheme

TL;DR

This paper introduces a tuning-free algorithm that leverages CQI feedback to enhance beamforming in 5G NR FDD massive MIMO systems, improving performance without complex parameter tuning.

Contribution

It proposes an empirical Bayes based method to learn optimal beamforming vectors and regularization parameters, applicable to various MIMO scenarios.

Findings

Significant performance improvements in beamforming vector accuracy.

Effective learning of regularization parameters across scenarios.

Robustness of the method in different MIMO configurations.

Abstract

In the fifth-generation new radio (5G NR) frequency division duplex (FDD) massive multiple-input and multiple-output (MIMO) systems, downlink beamforming relies on the acquisition of downlink channel state information (CSI). Codebook based limited feedback schemes have been proposed and widely used in practice to recover the downlink CSI with low communication overhead. In such schemes, the performance of downlink beamforming is determined by the codebook design and the codebook indicator feedback. However, limited by the quantization quality of the codebook, directly utilizing the codeword indicated by the feedback as the beamforming vector cannot achieve high performance. Therefore, other feedback values, such as channel qualification indicator (CQI), should be considered to enhance beamforming. In this paper, we present the relation between CQI and the optimal beamforming vectors, based on which an empirical Bayes based intelligent tuning-free algorithm is devised to learn the optimal beamforming vector and the associated regularization parameter. The proposed algorithm can handle different communication scenarios of MIMO systems, including single stream and multiple streams data transmission scenarios. Numerical results have shown the excellent performance of the proposed algorithm in terms of both beamforming vector acquisition and regularization parameter learning.