Bayesian Optimization-Based Beam Alignment for MmWave MIMO Communication Systems

TL;DR

This paper introduces a Bayesian optimization-based beam alignment method for mmWave MIMO systems, leveraging machine learning to efficiently find optimal beam pairs with reduced overhead and improved spectral efficiency.

Contribution

The paper proposes a novel BO algorithm using gradient boosting regression trees for mmWave beam alignment, enhancing efficiency over existing methods.

Findings

Achieves higher spectral efficiency with less overhead.

Outperforms OMP and TS-MAB algorithms in simulations.

Demonstrates effectiveness of surrogate models in BO for beam alignment.

Abstract

Due to the very narrow beam used in millimeter wave communication (mmWave), beam alignment (BA) is a critical issue. In this work, we investigate the issue of mmWave BA and present a novel beam alignment scheme on the basis of a machine learning strategy, Bayesian optimization (BO). In this context, we consider the beam alignment issue to be a black box function and then use BO to find the possible optimal beam pair. During the BA procedure, this strategy exploits information from the measured beam pairs to predict the best beam pair. In addition, we suggest a novel BO algorithm based on the gradient boosting regression tree model. The simulation results demonstrate the spectral efficiency performance of our proposed schemes for BA using three different surrogate models. They also demonstrate that the proposed schemes can achieve spectral efficiency with a small overhead when compared to the orthogonal match pursuit (OMP) algorithm and the Thompson sampling-based multi-armed bandit (TS-MAB) method.