Comparative analysis of different physics informed neural network architectures for spectrum occupancy prediction in Ara
Date
2024-08
Authors
Somiah, Christ Ampomah
Major Professor
Advisor
Qiao, Daji
Zhang, Hongwei
Song, Jiming
Committee Member
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Abstract
In this study, we present a comprehensive analysis of different neural network architectures
integrated into Physics-Informed Neural Networks (PINNs) for spectrum occupancy prediction in
wireless communication systems. The rapid expansion of wireless technologies has intensified the
demand for efficient spectrum management strategies, making accurate spectrum occupancy
prediction essential. Traditional methods often fall short in dynamic and complex environments,
necessitating advanced approaches like PINNs that embed physical laws into neural network
frameworks to enhance prediction accuracy and robustness. The work focuses on the application
and comparative performance of Convolutional Neural Networks, Fully Connected Neural
Networks, and Encoder-Decoder architectures within PINNs. Our findings highlight the strengths
and limitations of each architecture in capturing spatial patterns and handling in pathloss
prediction.
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