The advent of diverse frequency bands in 5G networks
has promoted measurement studies focused on 5G signal
propagation, aiming to understand its pathloss, coverage, and
channel quality characteristics. Nonetheless, conducting a thorough
5G measurement campaign is markedly laborious given the
large number of 5G measurement samples that must be collected.
To alleviate this burden, the present contribution leverages principled
active learning (AL) methods to prudently select only a few,
yet most informative locations to collect 5G measurements. The
core idea is to rely on a Gaussian Process (GP) model to efficiently
extrapolate 5G measurements throughout the coverage area.
Specifically, an ensemble (E) of GP models is adopted that not
only provides a rich learning function space, but also quantifies
uncertainty, and can offer accurate predictions. Building on this
EGP model, a suite of acquisition functions (AFs) are advocated
to query new locations on-the-fly. To account for realistic 5G
measurement campaigns, the proposed AFs are augmented with
a novel distance-based AL rule that selects informative samples,
while penalizing queries at long distances. Numerical tests on 5G
data generated by the Sionna simulator and on real urban and
suburban datasets, showcase the merits of the novel EGP-AL
approaches.
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This content will become publicly available on March 21, 2025
Weighted Ensembles for Adaptive Active Learning
Labeled data can be expensive to acquire in several
application domains, including medical imaging, robotics, computer
vision and wireless networks to list a few. To efficiently
train machine learning models under such high labeling costs,
active learning (AL) judiciously selects the most informative
data instances to label on-the-fly. This active sampling process
can benefit from a statistical function model, that is typically
captured by a Gaussian process (GP) with well-documented
merits especially in the regression task. While most GP-based
AL approaches rely on a single kernel function, the present
contribution advocates an ensemble of GP (EGP) models with
weights adapted to the labeled data collected incrementally.
Building on this novel EGP model, a suite of acquisition functions
emerges based on the uncertainty and disagreement rules. An
adaptively weighted ensemble of EGP-based acquisition functions
is advocated to further robustify performance. Extensive tests on
synthetic and real datasets in the regression task showcase the
merits of the proposed EGP-based approaches with respect to
the single GP-based AL alternatives.
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- Award ID(s):
- 2212318
- PAR ID:
- 10518969
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE transactions on signal processing
- ISSN:
- 1053-587X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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