Direction of Arrival Estimation in Terahertz Communications using Convolutional Neural Networks
Date
2024
Authors
Abdullah, M.
Li, M.S.
He, J.
Wang, K.
Fumeaux, C.
Withayachumnankul, W.
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Conference paper
Citation
International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz, 2024, pp.1-2
Statement of Responsibility
Mariam Abdullah, Mingxiang Stephen Li, Jiayuan He, Ke Wang, Christophe Fumeaux, and Withawat Withayachumnankul
Conference Name
International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz (1 Sep 2024 - 6 Sep 2024 : Perth, Western Australia)
Abstract
We demonstrate an approach to accurately estimating the Direction of Arrival (DoA) in terahertz communications using Convolutional Neural Networks (CNNs). Quasi-random patterns are generated with a frequency-diverse antenna which is deliberately designed to break symmetry, and a CNN model is then trained to capture the relationship between the spectrally resolved radiation patterns and their respective angles of arrival. The CNN converges to a minimum validation mean squared error (MSE) of 3.9◦ and root mean squared error (RMSE) of 1.9◦ .
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© 2024 IEEE