Frequency-Diverse Antenna with Convolutional Neural Networks for Direction-of-Arrival Estimation in Terahertz Communications
Date
2024
Authors
Li, M.S.
Abdullah, M.
He, J.
Wang, K.
Fumeaux, C.
Withayachumnankul, W.
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Journal article
Citation
IEEE Transactions on Terahertz Science and Technology, 2024; 14(3):354-363
Statement of Responsibility
Mingxiang Stephen Li, Mariam Abdullah, Jiayuan He, Ke Wang, Christophe Fumeaux, and Withawat Withayachumnankul
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Abstract
The IEEE 802.15.3d standard for point-to-point wireless terahertz communications is defined to support high-capacity channels. By nature, terahertz signal transmission requires line-ofsight propagation and terahertz communications operates within a challenging power budget limitation. Therefore, accurate and efficient direction-of-arrival (DoA) estimation formaximizing received power becomes paramount to achieve reliable terahertz communications. In this article, we present a frequency-diverse antenna with a machine-learning-based approach utilizing convolutional neural networks (CNNs) to estimate DoA in the terahertz communications band. The antenna is deliberately designed to break symmetry, generating quasi-random radiation patterns, while the CNN captures the relationship between the radiation patterns and their respective angles of arrival. Based on experiments, the DoA estimation results converge to a minimum validation mean squared error of 3.9◦ and root mean squared error of 1.9◦. The estimation efficacy is further substantiated by a consistent performance demonstrated across diverse scenarios, encompassing various obstacles and absorbers around the propagation path. The proposed DoA estimation method shows considerable advantages as a compact, integrable, and cost-effective solution for practical terahertz communications.
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© 2024 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.