THESIS
2023
1 online resource (ix, 50 pages) : illustrations (some color)
Abstract
5G New Radio specifications demands support of highly dense wireless mobile networks.
In these dense environments, power allocation is an important issue for maximizing channel
rate and saving energy consumption of devices. The power control problem can be
abstracted in to a weighted sum-rate maximization optimization, in which the task is to
allocate the optimal power that maximizes the channel rate within a power constraint.
However, optimizing this problem is not trivial as it is non-convex and NP-hard.
Power control in a device-to-device network with single-antenna transceivers has been
widely analyzed with both classical methods and learning-based approaches. Classical
algorithms guarantee a convergence to a local maximum, but its iterative nature and
computational complexity makes...[
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5G New Radio specifications demands support of highly dense wireless mobile networks.
In these dense environments, power allocation is an important issue for maximizing channel
rate and saving energy consumption of devices. The power control problem can be
abstracted in to a weighted sum-rate maximization optimization, in which the task is to
allocate the optimal power that maximizes the channel rate within a power constraint.
However, optimizing this problem is not trivial as it is non-convex and NP-hard.
Power control in a device-to-device network with single-antenna transceivers has been
widely analyzed with both classical methods and learning-based approaches. Classical
algorithms guarantee a convergence to a local maximum, but its iterative nature and
computational complexity makes them scale poorly for large network sizes. Although the
learning-based methods, i.e., data-driven and model driven, offer performance improvement,
the widely adopted graph neural network suffers from learning the heterophilous
power distribution.
In this work, we propose a deep learning architecture in the family of graph transformer
for wireless power control problems to circumvent the issue. Experiment results show
that the proposed methods achieves the state-of-the-art performance across a wide range
of untrained network configurations. While the proposed method perform better than
available methods, we show there is a trade off between model complexity and generality.
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