Frequency-Diverse Antenna With Convolutional Neural Networks for Direction-of-Arrival Estimation in Terahertz Communications
Mingxiang Stephen Li, Mariam Abdullah, Jiayuan He, Ke Wang, Christophe Fumeaux, Withawat Withayachumnankul
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-of-sight propagation and terahertz communications operates within a challenging power budget limitation. Therefore, accurate and efficient direction of arrival (DoA) estimation for maximizing received power becomes paramount to achieve reliable terahertz communications. In this paper, 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 (MSE) of 3.9° and root mean squared error (RMSE) 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, cost-effective solution for practical terahertz communications.