Kolmogorov Model for Large Millimeter-Wave Antenna Arrays : Learning-based Beam-Alignment

• A new approach is presented to the problem of beam alignment for large-dimensional millimeter-wave antenna systems, with a single radio-frequency chain, based on the application of the Kolmogorov Model (KM) framework to enable learning-based beam alignment. Unlike the conventional exhaustive search-based approach, the proposed KM does not require the entire beam space search, i.e., the number of beam soundings can be extremely smaller than the conventional approach, which is achieved by exploiting the predictive power of KM. We show, across several metrics, that by just sounding 25% of the beams, the proposed method approaches the performance of the exhaustive search method. Simulation results that validate the training and test performance of KM and illustrate the new method with significantly reduced overhead are presented.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)

Nyckelord

beam-alignment

Kolmogorov model

learning-based beam alignment

Millimeter wave

prediction

Alignment

Antenna arrays

Computational complexity

Computer circuits

Microwave antennas

Millimeter waves

Beam alignments

Conventional approach

Millimeter wave antenna arrays

Millimeter-wave antennas

Predictive power

Radio frequency chains

Test performance

Learning systems

Publikations- och innehållstyp

ref (ämneskategori)

kon (ämneskategori)