Enhanced Beam Alignment for Millimeter Wave MIMO Systems: A Kolmogorov Model

TL;DR

This paper introduces an improved beam alignment method for mmWave MIMO systems using a scalable Kolmogorov model enhanced by discrete monotonic optimization, reducing complexity and eliminating subjective thresholds.

Contribution

It proposes a new Kolmogorov model approach with DMO for scalable beam alignment in mmWave MIMO, and introduces a KS criterion for objective hypothesis testing.

Findings

Significantly reduced computational complexity.

Effective beam alignment demonstrated through simulations.

Elimination of subjective threshold setting in hypothesis testing.

Abstract

We present an enhancement to the problem of beam alignment in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems, based on a modification of the machine learning-based criterion, called Kolmogorov model (KM), previously applied to the beam alignment problem. Unlike the previous KM, whose computational complexity is not scalable with the size of the problem, a new approach, centered on discrete monotonic optimization (DMO), is proposed, leading to significantly reduced complexity. We also present a Kolmogorov-Smirnov (KS) criterion for the advanced hypothesis testing, which does not require any subjective threshold setting compared to the frequency estimation (FE) method developed for the conventional KM. Simulation results that demonstrate the efficacy of the proposed KM learning for mmWave beam alignment are presented.