Direction of Arrival Estimation in Terahertz Communications Using Convolutional Neural Networks
2024 49TH INTERNATIONAL CONFERENCE ON INFRARED, MILLIMETER, AND TERAHERTZ WAVES, IRMMW-THZ 2024（2024）
Univ Adelaide | RMIT Univ | Univ Queensland
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 degrees and root mean squared error (RMSE) of 1.9 degrees.
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Key words
Terahertz communications,direction of arrival (DoA) estimation,machine learning,convolutional neural networks (CNNs)
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