Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Learning Assisted Phase-less Millimeter-Wave Beam Alignment in
  Multipath Channels   Communication systems at millimeter-wave (mmW) and sub-terahertz frequencies
are of increasing interest for future high-data rate networks. One critical
challenge faced by phased array systems at these high frequencies is the
efficiency of the initial beam alignment, typically using only phase-less power
measurements due to high frequency oscillator phase noise. Traditional methods
for beam alignment require exhaustive sweeps of all possible beam directions,
thus scale communications overhead linearly with antenna array size. For better
scaling with the large arrays required at high mmW bands, compressive sensing
methods have been proposed as their overhead scales logarithmically with the
array size. However, algorithms utilizing machine learning have shown more
efficient and more accurate alignment when using real hardware due to array
impairments. Additionally, few existing phase-less beam alignment algorithms
have been tested over varied secondary path strength in multipath channels. In
this work, we introduce a novel, machine learning based algorithm for beam
alignment in multipath environments using only phase-less received power
measurements. We consider the impacts of phased array sounding beam design and
machine learning architectures on beam alignment performance and validate our
findings experimentally using 60 GHz radios with 36-element phased arrays.
Using experimental data in multipath channels, our proposed algorithm
demonstrates an 88\% reduction in beam alignment overhead compared to an
exhaustive search and at least a 62\% reduction in overhead compared to
existing compressive methods.

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The categories of this paper are: plasm-ph
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The categories of this paper are: math
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The categories of this paper are: solv-int
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The categories of this paper are: dg-ga
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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: stat
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The categories of this paper are: hep-lat
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The categories of this paper are: comp-gas
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The categories of this paper are: gr-qc
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The categories of this paper are: atom-ph
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The categories of this paper are: cs
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The categories of this paper are: q-bio
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The categories of this paper are: econ
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The categories of this paper are: math-ph
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The categories of this paper are: cond-mat