Frequency-Diverse Antenna With Convolutional Neural Networks for Direction-of-Arrival Estimation in Terahertz Communications

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 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 $^\circ$ and root mean squared error of 1.9 $^\circ$ . 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.