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

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 K...

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Bibliographic Details
Published in:arXiv.org 2020-07
Main Authors: Duan, Qiyou, Kim, Taejoon, Ghauch, Hadi
Format: Article
Language:English
Subjects:
Alignment
Complexity
Computer simulation
Criteria
Hypothesis testing
Machine learning
Millimeter waves
MIMO (control systems)
Optimization
Online Access:Get full text
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Summary: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.
ISSN:2331-8422